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    ">> EL COSTO DE LA Evolución ES EXPONENCIAL\n",
    ">>>>\n",
    ">>> José Rodríguez\n",
    ">>>>\n",
    ">>> jose@ejristos.com\n",
    ">>>>\n",
    ">>> 27 May 2026.\n",
    ">>>>\n",
    ">>> Bogotá, Colombia.\n",
    "\n",
    "**Resumen**\n",
    "\n",
    "La Evolución es una máquina informacional que utiliza recursos para producir complejidad y función. Los recursos son la computación en paralelo, dado por el tamaño de la población, y la computación en serie, dada por las generaciones del correspondiente Proceso Evolutivo. La Evolución no es magia. La Evolución es real y se puede estudiar profundamente. En particular, nada le sale gratis, todo tiene un costo. Hemos propuesto el Modelo del Perdedor o Underdog para entender a la Evolución. Aquí ponemos a prueba este modelo para diferentes predicciones relacionadas con el costo de la Evolución en términos de los recursos empleados para resolver una tarea modelo. \n",
    "\n",
    "# INTRODUCCIÓN\n",
    "\n",
    "La Evolución no es magia. La Evolución es real y se puede estudiar profundamente. Ella utiliza el tamaño de la población y las generaciones para producir complejidad y función. Por lo tanto, es una herramienta para proponer soluciones aproximadas a los más variados tipos de problemas. En consecuencia, la Evolución tiene un costo. Necesita tiempo, reflejado en el número de generaciones, y memoria, dada por el tamaño de la población. Para cuantificar el costo, le damos una tarea a la Evolución y medimos el número necesario de generaciones dado un tamaño poblacional fijo. Pero hay un problema. La Evolución no puede resolver tareas complejas en un tiempo limitado. Por lo tanto, la tarea no puede ser encontrar la perfección. Tampoco puede ser alcanzar, digamos, el 95% de la perfección. Esto también puede tomar más tiempo del disponible. La razón es que cada tarea genera un Proceso Evolutivo que propone aproximaciones cada vez mejores, pero puede terminar en una meseta por debajo de la perfección, que es un estado estable. Este subproceso se puede describir deterministicamente. Pero, debajo de él, un mundo de fluctuaciones se agita intentando alcanzar la perfección. Dado que la meseta es estable, las fluctuaciones, tanto positivas como negativas, se absorben y el sistema regresa al estado estable, la meseta. \n",
    "\n",
    "Nuestros propósitos para esta contribución son: \n",
    "\n",
    "1. Mostrar una meseta.\n",
    "2. Comparar las mesetas para diferentes configuraciones de complejidad que se dan por la longitud de la cadena objetivo que la Evolución debe igualar o adivinar.\n",
    "3. Determinar la rampa sobre la cual la Evolución se eleva hacia la meseta.\n",
    "4. Comparar diferentes tipos de mesetas para diferentes configuraciones de complejidad.\n",
    "5. Poner a prueba el poder del modelo del Perdedor para que se ajuste a la silueta del Proceso Evolutivo descrito por el ascenso hacia la perfección. Como subproducto, esperamos predecir la forma y la altura de la meseta en función de la complejidad de la tarea y del tamaño de la población.\n",
    "\n",
    "Nuestros experimentos en esta contribución incluyen la recombinación, pero el modelo Underdog no la tiene. Por lo tanto, el papel de la recombinación podría ser evaluado inmediatamente si el modelo no se ajusta a las observaciones en las simulaciones. \n",
    "\n",
    "# LA Evolución COMBATE LA COMPLEJIDAD\n",
    "\n",
    "Le damos a la Evolución la tarea que consiste en emparejar una cadena binaria objetivo. Este es el *Desafío Shakespeare Binario*. La Evolución lucha contra la complejidad resolviendo una instancia de este problema y luego enfrentándose a uno más complejo, en el que se da una cadena objetivo más larga. \n",
    "\n",
    "Cada tarea genera un Proceso Evolutivo que tiende a una meseta. En algunos casos, la meseta coincide con la perfección, en otros la meseta termina por debajo de la perfección.\n",
    "\n",
    "## La meseta\n",
    "\n",
    "Veamos en detalle cómo la Evolución siempre tiende a una meseta.\n",
    "\n",
    "**Ejemplo.** El siguiente programa nos muestra lo que sucede cuando un tamaño poblacional de 30 cromosomas lucha contra una tarea que consiste en emparejar una cadena binaria compleja con 1000 bits. La Evolución alcanza rápidamente una meseta que está por debajo de la perfección, la cual se define mediante el emparejamiento de todos los bits, e indicada por una línea horizontal roja. El dibujo muestra que las fluctuaciones siempre funcionan. Aparecen como una línea quebrada que gira alrededor de un valor constante promedio. \n"
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    "# Program NumberOfMatchedSites_VS_Generations.py\n",
    "# The task given to la Evolución is to match a binary string of 1000 ones. \n",
    "# The program will run for a fixed number of generations, \n",
    "# and it will print the best fitness (number of matched sites) at each generation. \n",
    "# With the assitance of the AI Of Visual Studio Code.\n",
    "# Modified from a program made with the help of Gemini.\n",
    "\n",
    "import datetime\n",
    "import random\n",
    "import matplotlib.pyplot as plt # Import matplotlib for plotting\n",
    "\n",
    "numberOfGenerations = 10000\n",
    "length_of_target_sequence = 1000\n",
    "target_sequence = '1'*length_of_target_sequence #1111...1 (800 ones)\n",
    "population_size = 30\n",
    "alphabet = '01' # Binary alphabet, binary strings\n",
    "include_clone_populationk1 = True\n",
    "include_mutate_allk1 = True\n",
    "include_crossover_allk1 = False\n",
    "mutation_rate = 0.01\n",
    "recombination_rate = 0\n",
    "# alphabet = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ .,'\n",
    "# Boolean flag to print the generation, best chromosome and fitness.\n",
    "# This is the ancestral line of the solution.\n",
    "print_best_fitness = True\n",
    "show_graph = True\n",
    "\n",
    "def print_parametersk1():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: solve an instance of the Shakespeare challenge\")\n",
    "    print(f\"Date of Experiment = {datetime.datetime.now()}\")\n",
    "    print(f\"alphabet = {alphabet}\")\n",
    "    print(f\"target_sequence = {target_sequence}\")\n",
    "    print(f\"length_of_target_sequence = {length_of_target_sequence}\")\n",
    "    print(f\"population_size = {population_size}\")\n",
    "    print(f\"include_clone_populationk1 = {include_clone_populationk1}\")\n",
    "    print(f\"include_mutate_allk1 = {include_mutate_allk1}\")\n",
    "    print(f\"include_crossover_allk1 = {include_crossover_allk1}\")\n",
    "    if include_mutate_allk1:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk1:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    if include_clone_populationk1:\n",
    "        print(f\"print_best_fitness = {print_best_fitness}\")\n",
    "\n",
    "def maxk1(data):\n",
    "    max_value = data[0]\n",
    "    for x in data:\n",
    "        if x > max_value:\n",
    "            max_value = x\n",
    "    return max_value\n",
    "\n",
    "\n",
    "def mean_stddev_and_maxk1(data):\n",
    "    mean = sum(data) / len(data)\n",
    "    variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
    "    stddev = variance ** 0.5\n",
    "    max_value = maxk1(data)\n",
    "    return mean, stddev, max_value\n",
    "\n",
    "def create_populationk1(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with random chromosomes.\"\"\"\n",
    "    # print(\"create_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        random_string = ''.join(random.choice(alphabet) for _ in range(len(target_sequence)))\n",
    "        population.append(random_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def create_perfect_populationk1(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with perfect chromosomes.\"\"\"\n",
    "    # print(\"create_perfect_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        perfect_string = '1'* len(target_sequence)\n",
    "        population.append(perfect_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def calculate_fitnessk1(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome based on its similarity to the target sequence.\"\"\"\n",
    "    # print(\"\\n *calculate_fitness8(chromosome, target_sequence)\")\n",
    "    fitness = 0\n",
    "    for i in range(len(chromosome)):\n",
    "        if chromosome[i] == target_sequence[i]:\n",
    "            fitness += 1\n",
    "    # print(chromosome, \" fitness: \", fitness)\n",
    "    return fitness\n",
    "\n",
    "def calculate_all_fitnessk1(population, target_sequence):\n",
    "    # print(\"\\n*calculate_all_fitness8(population, target_sequence)\")\n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = [calculate_fitnessk1(chromosome, target_sequence) for chromosome in population]\n",
    "    # print(\"fitness_scores: \", fitness_scores)\n",
    "    return fitness_scores\n",
    "\n",
    "def select_the_bestk1(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome for reproduction.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk1(population, target_sequence)\n",
    "    best_fitness = maxk1(fitness_scores)\n",
    "    best_chromosome_index = fitness_scores.index(best_fitness)\n",
    "    best_chromosome = population[best_chromosome_index]\n",
    "    # print(\"\\n*select_the_bestk1(population)\")\n",
    "    # print(\"best_fitness = \", best_fitness)\n",
    "    # print(\"The chro = \", best_chromosome )\n",
    "    return best_fitness, best_chromosome\n",
    "\n",
    "def clone_populationk1(best_chromosome, population_size):\n",
    "    \"\"\"Creates a new population by cloning the best chromosome.\"\"\"\n",
    "    return [best_chromosome] * population_size\n",
    "\n",
    "def mutatek1(chromosome, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "            mutated_chromosome_list[i] = random.choice(alphabet)\n",
    "            counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "def mutate_allk1(population, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    touched_numbers = [] # List to store the number of touched sites for each chromosome\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek1(chromosome, mutation_rate, alphabet)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "        touched_numbers.append(counter)\n",
    "    return mutated_population, touched_numbers\n",
    "\n",
    "def crossoverk1(parent1, parent2, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, len(target_sequence))\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "def crossover_allk1(population, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 = population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk1(parent1, parent2, target_sequence, recombination_rate)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "def run_one_generationk1(population, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    # Calculate fitness scores for the current population\n",
    "    best_fitness, best_chromosome = select_the_bestk1(population, target_sequence)\n",
    "    # print(f\" best_chromosome {best_chromosome}, best_fitness {best_fitness}\")\n",
    "    # Clone the best to build the new population\n",
    "    new_population = []\n",
    "    if include_clone_populationk1:\n",
    "        new_population = clone_populationk1(best_chromosome, population_size)\n",
    "    if include_mutate_allk1:\n",
    "        new_population, touched_numbers = mutate_allk1(new_population, mutation_rate, alphabet)\n",
    "    if include_crossover_allk1:\n",
    "        new_population = crossover_allk1(new_population, target_sequence, recombination_rate)\n",
    "    return best_fitness, best_chromosome, new_population, touched_numbers\n",
    "\n",
    "\n",
    "def plot_Best_Fitnessk1(best_fitness_history, length_of_target_sequence, population_size):\n",
    "    # Plot the fitness history\n",
    "    plt.plot(best_fitness_history, label='Best Fitness', color='blue')\n",
    "    plt.axhline(y=length_of_target_sequence, color='red', linestyle='--', linewidth=2)\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Genetic Algorithm - Fitness over Generations, length_of_target_sequence = {length_of_target_sequence}, popSize {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def maink1( length_of_target_sequence, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    print_parametersk1()\n",
    "    all_touched_numbers = [] # List to store the arrays of number of touched sites for each generation\n",
    "    population = create_populationk1(population_size, target_sequence, alphabet)\n",
    "    #population = create_perfect_populationk1(population_size, target_sequence, alphabet)\n",
    "    # print(population)\n",
    "    best_fitness, best_chromosome = select_the_bestk1(population, target_sequence)\n",
    "    generation = 0\n",
    "    best_fitness_history = [] # List to store best fitness of each generation\n",
    "    best_fitness_history.append(best_fitness)\n",
    "    if print_best_fitness:\n",
    "        print(\"Generation and best_fitness\")\n",
    "    #while best_fitness < len(target_sequence):\n",
    "    while generation < numberOfGenerations :\n",
    "        generation += 1\n",
    "        best_fitness, best_chromosome, population, touched_numbers = run_one_generationk1(population, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        if print_best_fitness:\n",
    "            #print(f\" {generation} {best_chromosome} {best_fitness}\")\n",
    "            print(f\" {generation} {best_fitness}\")\n",
    "        best_fitness_history.append(best_fitness)\n",
    "        all_touched_numbers.append(touched_numbers) # Append the array of touched numbers for this generation to the main list\n",
    "        #print(f\"\\nTarget sequence matched in generation {generation}\")\n",
    "        #print(f\"Final chromosome: {best_chromosome}\")\n",
    "    totalCost = population_size * generation\n",
    "    print(f\"Total cost = population size * number of generations: {totalCost}\")\n",
    "    if show_graph:\n",
    "        plot_Best_Fitnessk1(best_fitness_history, length_of_target_sequence, population_size)\n",
    "    \n",
    "if __name__ == \"__main__\": maink1( length_of_target_sequence, target_sequence, mutation_rate, recombination_rate, alphabet)\n"
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    "**Ejercicio.** Ejecuta el programa anterior y juega con él para ver cómo se comporta la meseta cuando varía la complejidad de la tarea, que viene dada por la longitud de la secuencia objetivo. \n",
    ">\n",
    "> *Respuesta.* La Evolución puede encontrar la perfección para valores bajos de complejidad, en este caso, alrededor de una longitud de la cadena objetivo de 410 o menos. Cuando crece la complejidad, el nivel de la meseta cae y la perfección parece inalcanzable a pesar de las fluctuaciones.\n",
    "\n",
    "**Ejercicio.** Compara las mesetas creadas en diferentes entornos de complejidad. Activar y desactivar la recombinación para ver el efecto en las variables de salida.\n",
    ">\n",
    "> *Respuesta.* El siguiente programa dibuja los gráficos de los Procesos Evolutivos para diferentes valores de complejidad o longitud de la secuencia objetivo. Para establecer comparaciones, cada historial de aptitud se normaliza por la longitud de su secuencia objetivo, de modo que transcurre entre 0 y 1. Al final, estamos comparando las proporciones relativas de realización de la tarea planteada. La recombinación también se activa y desactiva."
   ]
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     "text": [
      "Lab: Simple Genetic Algorithm\n",
      "Purpose: compare diverse plateaus for different degrees of complexity\n",
      "Date of Experiment  = 2026-06-22 08:50:40.830104\n",
      "alphabet =  01\n",
      "population_size = 30\n",
      "include_clone_populationk2 = True\n",
      "include_mutate_allk2 = True\n",
      "include_crossover_allk2 = True\n",
      "mutation_rate = 0.01\n",
      "recombination_rate = 0.01\n",
      "print_best_fitness = False\n",
      "\n",
      "Target sequence matched in generation 400\n",
      "Final chromosome: 1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111\n",
      "Total cost = population size * number of generations: 12000\n"
     ]
    },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 700\n",
      "Final chromosome: 11111111111111111111111111111111111101111111111111111111111111111111111111010111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111011111111111111111111111111111111111111111111111111111111111111111111111111111111111111101111111111111111111111111111111111111111111111111111111111111011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111\n",
      "Total cost = population size * number of generations: 21000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 700\n",
      "Final chromosome: 1110111111111111101111111111011111111111111011111111111110111111101111111110111111111011111111011011111111101111111111111011111111111111111111111111111011111111111111011111101111111111111111111011011111111111011111111111111010111110111111110111111111011111011111101111111111111111111110111111111011111111111111111111111111111111011111111111110111111111111111111111111111111111110111110010110111111111101111110111111011111011111111111111101111111001101111111011111111111111111011111101111111111011111111111111111110111111110101111111111111111101111111111101011111111111101010110111111110110111111111111111111111111111111111011111101111111101001011111111101101111111110011111101011111011101011101111111111111111111111111111111111111111111111111111111111111111011110110011111111011111110111111111111111110111111111111111111111111111111111111111110111111110111111111111111111110111111101111111110111111111111111111111111111111010101111111011111011111111111111110111110101111011010111101101111101111111010\n",
      "Total cost = population size * number of generations: 21000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 700\n",
      "Final chromosome: 10111101111011110111111111011011001100111111001011011011001111111111111001111111011111111110101111010111111111010111101101010010111111111111011110011111110111010111111111011110111110011111111101110101111111111111111001101111111101011111001110111111111111111111110111110101101111100111111111111111111111111110111011011111110101111011111111110111111101011111011111111111111111010110111101100010111111101101111110111111010011111111111111111111001111111101110101111011111011010111111010011100100111111111111111111111111110011111111111111101011111011111000111111110110111011111111101011110110101111101101111111111111111101101111101110111111111101110101011101111011111110111111010111010100011111001111110010111111111111101101111010000011111111111111111110111011011111011101111111111111111111111100111110111111111111101110011111000011111001111011101111110011011111010011111101101101111111111110111111111011110100111110111111111011111101111111101111111110111110101111110011111011111111110011111111111111111011101011111111101111110111011101101111111111011101111111111111110111111110111110011101101111111011001111111110111110110011111111111111111101111011110111110101111101110001111011011111111111011111111111011111111100111101111101111101001111111110110110110111111111110111111111111110111111111101111110110111111101111110111111111110111111001111110111111101100111111111011101111110011110111110111011111111111101111101111101111100110110111100110011011111111111111111111111011100100111101011110111111011111111111001011111101110111110101111100111111010111110011111111111011111011111110111110111111111100111111110111111111011110111111101111010110111100011110101110110100011010111111111011111111101111111111111111111111111111111111011111111011111111101101101011111111001001111110111001111011111000111111110111111110010011111110111110101110111111011101111011101111110111010101011111111111111111101111111111011010011111110110111110111101011110111111111111110111111111100011111100110111111001110111111111111010111011\n",
      "Total cost = population size * number of generations: 21000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 900\n",
      "Final chromosome: 11101111010111111111011111010111110001110001111101111111110110110110011101011101111011110011010111110110110011111111111011111111111010100101000011011101110111101111111101010111100110001110101010010110001010111011011111101111111100100011111110100110110111010010011111110111111010101011111111100111101011100011010011101101111110001111110111111101110110110100111010101111011110111001101110110111011101111111011011011100111111111111100100110111010110110010111111111110111110001111110111111100110001110110011101011010010101011011010111110100111001101100110111110111101011011111100110111111110010001111011001011110101111111011111101110111001111110111010111100011111011010110111011010010101101001111110111111111111101011011011100011100001111011101010111110110111111101101101110111110101111011100111101101111111010001010111001101101101101111110101011011101101100110001110111010111011111110010111110111111111100001111111100011001111011111110111111011001011001101101111111110111100111111010110111111111111111100011111011001110011110011111111011110110101111011011011111111111100001011111111111110111111100011111111101001111111101001111110010010110010011111101101111111100111110111111111101111111110111110111011111111110111000010010101110011110111110111011011110010001110100111111111101101100001011010111111011001110111000101111111110011001010111111001001110011011111010111111100111111101010001111011111111111010111101110111100100010111011110000011111111101011011111111111101111001001010100011011011111101110111111101110011110010101010111111111101111001100011010111010111101011011011101110101111100111100100000111101011011001101110011110111010011001011111111011111011011011010101001110110001110010011010101110010000111000111110100000011100011111111011101111101101110110101111100111111110110011111101111001011011011111100101101001111011111011111010101110001001101111100110111111111111111111010100110110111111110011110110110111010111111111101011111110110111101011111110001101011100110111000010001111111011111111000101111011111101011010101111011111100101011011101101111011011100110100110100110000100011111100101001011101001101100110110111100111111111001110110000110100100101101101011111011100001101000011101010101111101101110111011001011011001011111110110111111111011110011111011010111101111101110010101101110011110011101111111100011110110010001111111011101110111110111111111111011111101110110000111011011111111011011111101110111010111101110111011111111111000011111111111111010100011101111000101010111101111110111100101111101011010111111111111011111110000011011101010000010000101101101111111101111111111001100111000101111011011001111011111100111100111011111001110110111110011010001111111111010111111111110111111101000101000000111101111000110100111111111111110111111011101011111000110001001001010101110011001111111111100111110110111110110110100111111111110001011010001111111111111110001110110011111110111011101111101110100110101011001011111101101110110010110111111111101011111011110101101101111110101111111111111111111111111110111100100100111011011111111111110011111011111101111111110111111101000011011100011011110010010110110110011101011111001111101100100101001111010111111100111001010111011011010010101111111111111111100100101101111110110101110111111111111101101111101011111110111111110010101110000110100010111101111111110101111111111101110001111111101110111111111011111111111110101100101111011101111101111111111110111111011101111011011101101110101111100100111111111111010011101110111110111001111110101011011111101010111111100011011000100101111101001001100101111010111100101111111111111111011101111111100111001111001101011101101101011110110101110100010101011110001001111111110101011010111101111111101011100110110111100111010111111010011101111101110000100110111010011110000001111111010111101110101001101110110011011101011101101010101111111111011110001110111010111001111110011110111010101011111000010011110011100111111001111111110111111110111011101011110100100001010111111110001110111111001101110110111111111101101100110001110110011000101010101010011100111111111010100111011101101011110111111011010011011111110110111101001011011111010111101101100111010010111101101110100000110111110001111111001111111011101111111110111100111100110011011001011011111110101001111010111111110000111111100101110111111001101101000111100011111111011110011101110111011111111101011101110111111011111011111110000110101110011101110001111001111011111100011110110011100011111111111101111111011111111010111011111110001101111101100101110010100111111001011110001111111101111011110111011001000101111011111001111111011111101011110111110111111101110111111011111011011010111010100110101111010000100010011100111110100000111111111111100101011110101111111011110111111111101101111110111101111111111100111111101111101111111111110110111111111011111100111111100101101111000001010111111011111110111111001111111111111111001111001010111100000000111010101101101101111110110111101101010111111111000011111101110101110011111101111111111111100111111111010110111111000\n",
      "Total cost = population size * number of generations: 27000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 1200\n",
      "Final chromosome: 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\n",
      "Total cost = population size * number of generations: 36000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 1300\n",
      "Final chromosome: 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\n",
      "Total cost = population size * number of generations: 39000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target sequence matched in generation 1300\n",
      "Final chromosome: 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\n",
      "Total cost = population size * number of generations: 39000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Length of target sequence and normalized realized plateau height\n",
      "Length 400: 1.0\n",
      "Length 500: 0.988\n",
      "Length 1000: 0.898\n",
      "Length 2000: 0.8095\n",
      "Length 5000: 0.6994\n",
      "Length 20000: 0.60265\n",
      "Length 50000: 0.56568\n",
      "Length 100000: 0.54567\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power model: y = 7.9874 * x^-0.4421\n",
      "Exponential model: y = 0.3224 * exp(-0.0000 * x)\n"
     ]
    },
    {
     "data": {
      "image/png": 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X1b3RuSwMruwPvJy3c0rL4lyfeEDJgxTnbqdcz4Nze+y2yIOg7du3OwSnN/reie+dAToPIO1PELAucF/bd92zx3pv/965fewxCErNwHKO72K3K9ZR1n+OR7GCuKQwqPnwww/NQSe7p/Gg0mJ1o01Oauo7P+PsJspufOxCZX9wfCNjdK6HXTRddUV2ToST0v3kCvcd97nz90JS31vsouh8X24bjhO8HtZRBsKjR482XVEZGDBgs8b73SjnesfgjieJrO9Lq346f9cxYOC8cc6fXZ78cq53TFxCfE5rXBNfg939WPe4DxgQspstuynyNufueM540oPdAVln2Z3Z/juSz8dtmtTJl+T2qT2ORSOewOPni78F3D7WVAs8GZBUd9rrJdcRSS8KnEQyuKSykaX0rGJq8QeUYwh4kMwzua7mo0ov1tl1Tkab1JgZK82uMx5QcczDihUrTEp1HkhwTAAP9LkuqcddT0qTa9zMjzxbnayxTgz2blZyZXFVn1JSxzgOgmOqWL4XX3zRHCjzcTyY52D1G8FAkQd7fO8cu+JcF/gaPMh1JbXj966HB+U8o54anO+LyTVYz/geOFaMwS1bF1IyiD419Z0tagya+NwNGjQwB/usmxzzZP9aSdVX+6AuLd3K/XSz34X8LmAdnjFjhkmNzhZb1l9+P3D/Z0Tc3wysuDA7HQM57ovrBU78DC9fvty0iDm/d+5TtiBybjtXrEAuNRgg16xZ05TNCpwY4LFeMhCzD7wZTPGkjX0PCpFbRYGTiBskdfCSHimn+YPDs8BMHODM1bqUYCvN119/bQ4CeWCWFA5S5kBnZ2yRsG63/m/evNkc4NhvA+fHWi0O7MKS2oNYC8/IcnnrrbfMQHMGJZMmTbrugYSr98YDCJ59tVp2iAkAOJjcem9pwWp1sg7qXJWFZ4BZHvtWJ+ftnJ4YlPIMOc9O2+9DJjS4Eczux5YWBmLMVOiMdYEHdUyRn1wgaL137ieWz36CUFfZzdJ6m/CsOrv52WP9sG+tSepzn5r6ztfi9rLPdsiz8s6Z2axWNq63uq+Sc8tGWknpfkpq37E7r/P3wo1+b6XkO5aJJLjw88aEByw3u7oxuciNYL2zWlaIXWeZQIEt9/b1k9919vWTwQFbKZ33OxMLsUubfasTPytknx3PFT4/9z9fPzn8PmSCBy5sqXe1T/k+bvQ7OClsNbXPSGqdLGA2VGt7Wdf5XZdZ5+ESz6YxTiJuYP3oOR/UWJmvnNff7EE3f+B49p4/uvYHH+xjfiN4IMAz6MyMxa5pSeGPHTOq8cylhT/6zFLHH/lKlSol3I9ls+bqII4z4P3scVwEf7SZTps/3M54MJwUHiQ7n3m2fniTSnueHOuH3DmDFLv6EM/upiV2GWIXJWuCZOeycDwCu0/ad7X5+OOPTYuEq4OftGad7bffxswyaL/vU4r7ll0SOb6FLZuuDnatbkSsh8743q3PEOs+x5hwW9iXLaWZv252mzjXOY4PYVe6lHwfpKa+u3otvmfnliQrGLMfh8LPpHPq+rSS0v3kCscEclvNnDnTIRjkZKk3KqltzayQLI89BlA8EXEj3w8WfofZd1tl9zm+jjW+lPWT3fKYDdV+/zHYZhdH5+8RPtZ+KgMGWLzOljvWF+tz5zxeiPhdzJaa5DJ68gQWTyLx+8Y5G6f9PuXnmq32zrhdnbejc/ldnbBg2Tjht313d2ZUZCstt5k9XudvZVp/x4qkhFqcRNzA+oFjVxAeHPCghy03PCPLYIIHwOzuwB8N9vvmcjPYnY5dT3j2lIOTeSDDoIfPy3FKqWXNMXQ9TPvNCXN5kMD3yvfDAzSeSeXgcqt1hAOhWR7OccIBzuyiwVS6zimUeX+mvObzcYwJ0xvz4JoHVxxkzjPz7IbnCl+X/f15QM6DRyYb4AEYH2N/NjOlOE8Sz/DzwIgHCwxO+OPP12Erif1Z5rTAOsJUwXzPrgag8+CJLVLcfgxKGYQygQEDBI7JSW+cp4qtTdy+PKDhPuaZetZnVwf9yWFwyJYG1jHnFjbuO3ZF4/bmoHh2pWIdZnpxBkg8w8/ghGOLOLCdB5RMnc77sYzc10xjzJMGrsbopCW+HrtXcp8x5TUPDNkVyb5lwXpPbP3h9uK+4sE9xwZy3E1K6ztfi58ZdtHjNre6WTmPpeJ24jg2JvtgdyzWK7YeczsxLXRaS+l+coWP4/cCk4PwIJ7fC9x+1jiqG2mhT2pbM5kCu4gxdTy/e3mAz+3J7cOxkRYrdTi3v/OcU64wsGEiDwYbbFXidxCT5VgJL7jdmcabz8nuqFxv3Y/jR53TbbN7GruvcjwTy2mNNeX3kJWEguXmduJnkb81DMw4RQL3M7edqxZci/X9wjFs7H5rj3WYdZf1hsEs6xy/c/gaDNRYv/m9w7Il9dnidwHnfuK0B9Y4QT6O3UxZd9m11cLfQwbcnLeJ+4W/lUzBz3Kxx4D9NAoit0ya5ugT8UJW2tmk0ly7SkfO1M3PPPOMSanKVM/2H8Vly5aZFLZMUWuftjmpdOT2aamTSotMCxYssNWsWdM8b+nSpW1ffvmlbeDAgbagoKAbSsed1Pu0T0dOe/bsMSnMc+TIYV6rbt26ttmzZyd6/IEDB2wdO3Y0adPz5Mlj69+/f0LKYvu0w1bq4C5dupgUtkxpy/fbtWtX8x6TSq+8bt062wMPPGDSWvMxTN/bvn1725o1a274/UdHR5tU7EwNzZS9RYsWtQ0ZMsSkobbH8tmnaL6Z1+O+c7XfT5w4YevTp4/ZdtzHTN3tnHI5qX1kX7+YcjslZWFq5MqVKydcZ7rst99+27xXbl/WNe5nV+mOr5eOnI9JKtW1c73+4osvzOeF6dGzZ89u3vegQYNsR48eTbgP0zxzPxUsWNDcr2nTprbNmze7/Jy4ktTnzJmrdOT8jFmv26hRI5Mi3DntNjE9daVKlRKmCLDfdymp70z9bu1/pqBnCvPt27e7fI9r16611atXz9QTfh5Gjx6d6nTkzvXkenU9JfvJ1XbZu3eveT4+jt+X3J5Tp041ZVixYkWS9dHiqv652tZ8Habc5ueL31O5cuWyNWvWzPbnn386PJavz+/sbdu2JbudrO25ZMkSk+I/Z86cZr/06NHDdubMmUT3Z/pxpg7n9whT+Pfr18/sU3vWe+R3FqcaYDn53vhYe5s2bbK9+OKLtlq1apn3wffJOnjfffeZ78Hkto81dYKrxb5OcloJftdxygjWI9a7hg0b2kaOHOmQft0Z06fzu71atWpmWgS+X74m0/0nVfdYd8qXL5/w2/XBBx84pOcXuZV8+OfWhWki4knYMrJly5aE7F0iIp6OrajPP/88Dh8+bFrgbnX2VI5LYmtZcqwJfzmJbVplW2ULF9N+szudiLiHxjiJeAnnOS8YLHEOjZR0NxER8YTvLY5xYrdUZoe71UETx0GxSx+7X4qId9IYJxEvwb7p7I9uzQ3CAbbs+55USlkREXfj1Acck8VELkyWwPEtzBaZVHrz9MQxZTeTKEJEMj4FTiJeggOPmaiB2dc4FxMH2HOeGecJGkVEPAUTAjBBBgMlJrVh4gumy2ZyARGRW01jnERERERERK5DY5xERERERESuQ4GTiIiIiIjIdXjdGKe4uDgcPXrUTHx3I5PniYiIiIhI5sCZmS5dumQmmPb1Tb5NyesCJwZNnLVaRERERESEDh06hCJFiiA5Xhc4saXJ2jhMLeoO0dHRmDdvHlq1aoWAgAC3lEEyFtUZUb0RfdeIp9JvlGTkOsM52tioYsUIyfG6wMnqnsegyZ2BU0hIiHl9d1cWyRhUZ0T1RvRdI55Kv1GSGepMSobwKDmEiIiIiIjIdShwEhERERERuQ4FTiIiIiIiItfhdWOcREQkdWlaY2JiEBsbq83mJeMO/P39ERkZqX0uqjeSab5rAgIC4Ofnd9PPo8BJRERcioqKwrFjxxAREaEt5EWBcoECBUzmWc11KKo3klm+a3x8fEyq8WzZst3U8yhwEhERl5OF79u3z5yh46SAgYGBOpD2kv0eHh5uDi6uNxGkiOqNZITvGgZpp06dwuHDh1G2bNmbanlS4CQiIi5bm/jDxrktmDJWvAP3Ofd9UFCQAidRvZFM812TN29e7N+/33QRvJnASaeTREQk6R8JtTqIiEgG55NG3QEVOImIiIiIiFyHAicREREREZHr0BgnERFJV7FxNqzadxYnL0UiX/Yg1C2ZC36+6Z9FSUREJC2pxUlERNLN3M3H0PjdhXhg/Ar0n7TB/Od1rk8vDz30kOnPzoVzd+TPnx8tW7bE119/bQYkp9S3336LHDlypFs5RUQkY1HgJCIi6YLBUb+J63DsQqTD+uMXIs369Aye2rRpY+agYhal33//Hc2aNUP//v3Rvn17M6GviIhIailwEhGRdOmeN3zWVthc3Gat4+28X3rIkiWLmVyxcOHCqFWrFl5++WXMmDHDBFFsSaLRo0ejatWqyJo1q0m7/uSTT5p5RWjx4sXo06cPLly4kNB6NWzYMHPb999/jzp16iB79uzmNbp3746TJ0+my/sQERHPocBJRETSHMc0Obc02WO4xNt5v1ulefPmqF69OqZNm5aQav2jjz7Cli1b8N1332HhwoUYNGiQua1hw4YYM2YMQkNDTcsVlxdeeMHcxnlA3njjDWzcuBHTp083rVrsHigiIpmbkkOIiEiaYyKItLxfWqlQoQI2bdpkLj/33HMJ60uUKIE333wTffv2xWeffYbAwECEhYWZlia2Ktl7+OGHEy6XKlXKBF+33Xabaa3Kli3bLXw3IiJyK6nFyZ1ObgO2zgRObHFrMURE0hqz56Xl/dKKzWZLmAjxzz//xJ133mm687Hb3YMPPogzZ84gIiIi2edYu3YtOnTogGLFipnHNWnSxKw/ePDgLXkPIiLiHgqc3Mj330nAlAeBjZPcWQwRkTTHlOMFw4KQVNJxruftvN+ttG3bNpQsWdJ0r2OiiGrVqmHq1KkmGPr000/NfaKiopJ8/OXLl9G6dWvThe+HH37A6tWr8euvv173cSIikvEpcHLr1r/WUzJOGZ5EJHPhPE1DO1Qyl52DJ+s6b7+V8zlxDNO///6Le+65xwRKTE0+atQo1K9fH+XKlcPRo0cd7s/uerGxsQ7rtm/fblqlRowYgdtvv910/VNiCBER76DAyZ18FDiJSObVpkpBjO1ZCwXCHLvj8TrX8/b0cvXqVRw/fhxHjhzBunXr8Pbbb6NTp06mlalXr14oU6aMSfLw8ccfY+/evSZT3rhx4xyeg+OeOG5pwYIFOH36tOnCx+55DKisx82cOdMkihARkcxPySHcyU+Bk4hkbgyOWlYqYLLnMREExzSxe156tzTNnTsXBQsWhL+/P3LmzGmy6TGJQ+/evU02PV5nOvJ3330XQ4YMwR133IF33nnHBFUWZtZjsohu3bqZVqahQ4ealORMZ8705nw+pjofOXIkOnbsmK7vR0RE3E+Bkyd01YuNdmsxRETSE4OkBqVz37KNzMDGmqspOc8//7xZ7DFBhL2xY8eaxd4DDzxgFuekEyIikrmpq55bt77V4uTYh15ERERERDyLAie3bn111RMRERERyQgUOHlE4KSueiIiIiIinkyBk1u3vlqcREREREQyAgVObmTTGCcRERERkQxBgZNbt76y6omIiIiIZAQKnNxJ8ziJiIiIiGQICpzcuvU1xklEREREJCNQ4OTWra/ASUREREQkI1Dg5Natr8BJRERuvW+//RY5cuRI1WN8fHwwffr0dCuT3FpxcXG47777zH7t37+/Nr9ICihwcicFTiIiae6hhx4yB4NcAgMDUaZMGbz++uuIiYnR1vaifW+/VK5cOdnHTZkyBTVq1EBISAiKFy+O999/P9F9fvjhB1SvXt3cp2DBgnj44Ydx5syZhNubNm3q8rXbtWvn8jX79u1rbh8zZkyK3hufPy3169cP//zzDz7//HN8/fXXePPNN1P82J9//hkVKlRAUFAQqlatit9++y3Fj126dCn8/f3N9k7KiBEjzLZ57rnnEtadPXsWzzzzDMqXL4/g4GAUK1YMzz77LC5cuODwWK6rXbs2smTJkuxrpKXIyEg89dRTyJ07N7Jly4Z77rkHJ06cSHVdbdOmzS0pr9w4BU4ekVVPP+YiImmJByDHjh3Drl27MHDgQAwbNszlwbA7REVFubsImdaHH35o9ru1HDp0CLly5TItK0n5/fff0aNHDxPIbN68GZ999hk++OADfPLJJw4H+7169cIjjzyCLVu2mMBh1apVeOyxxxLuM23aNIfX5nP5+fm5fO1ff/0VK1asQKFChZJ9P3zdP//802Edry9btgw34+WXX8bcuXPx119/4fHHH8f8+fMxevRoE0RdD1/7gQceMNti/fr16Ny5M7p06YKtW7de97Hnz5832/HOO+9M8j6rV6825ahWrZrD+qNHj5pl5MiRZtuy1ZTvgeVwxqC2W7duuFEMYvbv35/i+z///POYNWuWqRdLliwx5eQ2Sen3lLX89NNPN1xmuUVsXubChQs2vm3+d5eoqCjb9OnTbdE7F9hsQ0Nttk/qua0skjFYdYb/RW5Fvbly5Ypt69at5n+CuDib7Wq4exa+dgr17t3b1qlTJ4d1LVu2tNWvX99cPnv2rO3BBx+05ciRwxYcHGxr06aNbefOndfeYpwtT548tp9//jnhsdWrV7cVKFAg4frff/9tCwwMtF2+fNlcP3funO2RRx4xj8uePbutWbNmtg0bNiTcf+jQoeY5xo8fbytRooTNx8fHZbm/+eYbW1hYmG3WrFm2cuXKmbLdc8895nW+/fZbW/HixU2Zn3nmGVtMTEzC45J7P/bPXbRoUXN7586dbSNHjjSvZY91pWbNmrYsWbLYSpYsaRs2bJgtOjo64Xb+dv76668p3g/cDk899ZTDupMnT9oCAgJsf/75p+1WYHm5vffv35/kfR544AHbvffe67Duo48+shUpUsTUB3r//fdtpUqVSnSfwoULJ/m8H3zwgakP4eHhDusPHz5sHrd582azT3m/pBw8eNCUrV+/frZatWqZ//fdd59Z70qfPn1sVatWtUVGRprrV69etdWoUcPUD8vo0aNtZcqUsR04cMDhsevXrzf13L7uu9K1a1dbu3btHNbVq1fP9tBDD9liY2OTfWy3bt1sr7zySsJnwtmlS5dsZcuWtc2fP9/WpEkTW//+/ZN9vilTppjPon09tST1GinBur5v374U3ff8+fOmTttvt23btpnnWL58eaq+p7xJbGys+e68Xp1JKy5/024gNrjW5CFuoa56IpKRREcAbyd/hjzdvHwUCMx6ww9n1x6rWxW7yLAlaubMmQgNDcXgwYPRtm1bc8Y8ICAAd9xxBxYvXox7770X586dw7Zt28zjt2/fbron8YzybbfdZrpsEVsUeDtbLsLCwszZcp5R37lzp2ntoN27d2Pq1KmmVYKtEEmJiIjARx99hEmTJuHSpUvmrPXdd99txiOxO9TevXtNN6BGjRolnFG/3vtZuXKlOSv/zjvvmNYBnqUfOnSow+v+/fffpiWA3cZq1qxpuhmxBYac72vfdaxEiRLmzL8rjz76KJ5++mmMGjXKdJuiiRMnonDhwmjevLnLx7Acd911V7L7ktuXLUQp8dVXX6FFixam+11Srl69mrAvLdyfhw8fxoEDB8x7bNCggWml4T5g+U6ePIlffvnFbOfkXvv+++9H1qxZHcYVPfjgg3jxxRev232QihYtaloxhgwZgnXr1qF169amRSwprDvsTvjSSy+ZVrP//e9/ppXHvvWMrSNcnLFbG1s9rmf58uUYMGCAw7pWrVqZup2cb775xtRf1oGkugWyuxu7NnKfpaTrILvpsc6z65+7rF27FtHR0abMFn5PsCsht1X9+vWTfCy/Z/Lly4ecOXOazwTfM7v7iedS4OQRgVO0W4shIpJZ8eTxggUL8Mcff5jxEVaAwS5QDRs2TBi7wgNUJj5gEMSAwOqyxK5MDCQKFChgDnJ4QMT/TZo0MbdzjAi7bPFA2goO2JWIz8UDa3aDsrrnTZgwAXnz5k22vDwAGzt2LEqXLm2uM3j7/vvvTSDDsROVKlVCs2bNsGjRIhM4peT9sPsauwQNGjTI3F6uXDnT3YoBlGX48OHmYLt37964ePGi6Sb1xhtvmMckFTjxwJBjfZLCoI+B04wZM9C1a1ezjkGWNbbDlTp16mDDhg3JbqP8+fMjJdhdisHsjz/+mOz9GIwwkGC5uG0Z5DLYIwYSDJwYqHK7cptzPAvHy3Xo0AGffvqpy+dknWB3MgZP9t59911zkM9xOClx5MgR09WUB9a1atUygTyDMZaPAagz1hEGJqyf2bNnN4Ew6wqDi7Ry/PjxRPuA1/kZSArrKesXA+OkghyeLGBwyK56KXH69GlTR63PmLtwe3AspXOyFW4T3pYUfib5GSlZsiT27NljAnMG5Qy2kju5Iu6lwMkjAqdYtxZDRCRFAkLiW37c9dqpMHv2bHMQyUCEZ/m7d+9uxjkxiOKBW7169RLuyzO8HHDOliXiQSezjJ06dcq0LjGQsgInttww6LCCkI0bNyI8PDzRWeIrV66YgyELWzyuFzQRWz6soMk6+OKBO9+Lq4NUlvl674f/2Wpljy0o9oET3weDr7feeithXWxsrAkS2Arm3CJDDASTw8QBbF1h4gEGTjwoZjDBQC8pbOlhMo+08N1335mDWbayJYfjlLiv2rdvb+oLgwzuf9YXX9/4oeBsveO61157zQRaDKjYasRWOefgiLiOSRPq1q3r0DLBIJbbIanA0RnH2bDljq0ZrIcMqjnGietdBU7Wvn3hhRdMUMHWx8aNG+NGHDx40ATqFh7Yc0kt1iN+/hicM2h3hWPRuH051or15noY3LNliuXjfrpZDFgY1Nlji6C1n/j55di2tMQA2MK6wpMV/Ozzeya5MWDiXgqc3MimrnoikpHwIOImusvdSmw54EEmzwRzAH5quvLwIIZd7Bg0cWEwwcCJrQU8G86Da6t1h0ETW114sOPM/gy0fXet5LBrnT0euLlax2AwLfF98MCWQQYvM1CzgoaUHMgmhQf97ALGbm/sqsXuSMl1m0urrnpsaWTAxsCNdSA53J7ct2+//bZpIWCAywCbSpUqZf6zmyNbnRgsEQ9yuU9vv/12073KvuXt8uXLpvWEmRyd3xsDXrbU2QcVbFFiy5CrZAR8TWf2XcJcYd1gEMxWC7ae3Sh+buxb/6xup/wsOGeM43V2OXOFXU7XrFljEkmwBdIqI/cRP5fz5s0zgRC3DVvV7LcNW3zZzZDdKa1WGD4fW2vYosYkG86fjxvx5ZdfmpMdlrJly5pumVZwmtxrcHuwRZldIu0/89wmvC2lWNfy5Mlj9pkCJ8+lwMkjsuqpq56ISFriQa2rlouKFSuablYc92MFPxz7tGPHjoSz6zyQ5gExu5jxLDPP2LPFhQdvPGhndzIrEOKBHg+2eQDIlqFbLSXvh/fh7faY0c0e3wcfw23Gg1i2uliB081gEMrtNX78eNNlzn6sTXp21WPAywNQVxnXksIDc+tAmdnN2HJjtRKy1c05+LYO5ONzCfyHY5JYV3r27OmwnkGcc9DD1iuu79Onz3XL5yo4d4XZIzkej9uAz8+ANSXP74zv19VniNuFgaV9qnC2gnHcnyusS//++6/DOo7TWrhwoenOyq5qDKSc78Mys2ssW82sbc26yffEbrFsubyZoN6eq9Y7Bvgp+Uwz9TkDK24Tjj8kfpbYYsdtlVI8ucDPbnLdX8X9FDi5k58mwBURuZV4JrlTp06mexaDIJ615tgLHjhxvYXdotgSwAN5q5sck0ZwnIvV6kA8EObBEVtq3nvvPdMViWNr5syZY7rH8fHufj8cT8OWC4694jqO97LvpkfsgsauahwbxQNT62CXXeuSGqTPZBJ8HbbGJMdKEsFg07nLYHp11WNXOXZfrFKlSqLbGLyxpcJqVeJYGR7Ac5+zayIDDSuttIXjmbiN2YppddVj4MCueM4pxfnarA/O3Td53XkdD7jZKsGulWmBrTrcl3w/3OdMMc4ucOx+arWe3Szr+TjOit3l2LrGFiXWLwuTWXB8FrtzMgB33g9snWLQY7/e+T6sL9xe1noGTUxCwSCW47h4nQsxwLWCKwbMbDXlCQ22IlmBOE8kXK/18UYwIQwDdCbMYKscPzscT8nvBfvEEAwC+VnhZ8Bq4WWgxf3PrqLs/su6z/olnkvzOLl16ytwEhG51XhgzLPEDBR4cMMWA3bLse+OwwNDdhWyn3SUl53XsXWKj2VQxTPkDJw4doHZ2FKaxCC93w8P3tjiw/E1zLjGrlGvvPKKw3PwYI3jwjjGhN2E2HrFrGzJdavjGfWUZGHjnD9sveD/tGohuF6mNWYwTKq1iYGS/fgzazwUg1wGG2xlZOuO/fgkJo5gEMKgiwfyTLrBYMc5kxxbGpgwJDUtXWmFQR9buVhWBnrExAnstspWLdbdtMC6wdbDL774wtQnBmncDvbjoVgvWD/SEseGseWUAT0DDLbMWAvHSNkH6kzowhMJzGzJy1x4QiO98LPCzx8DIX4XMBhyVTesyXoZ5G3atAkdO3Y03xmsL/wMszunlWRGPJOZTAJehGcneHbASmHpDuwfzx+1to2qIuCTmoB/EPBK8jNMi3dLqDNt26ZJf27xDjdTb3gQtm/fPtON5lYc7IpnYJeptOyqRxy7w0HvHB9mP4ZFMo/0qDeSucXd4jqT3G9aamIDddVzJ7U4iYhIJg7cOWaDrVts9VLQJCIZnU4LeEpXPe9q+BMRkUyOmd3YjYotTePGjXN3cUREbppanDwhcLLmcrKSRYiIiGRwHAvmZaMBRCSTU4uTxwROMe4siYiIiIiIJEOBk8cETprLSURERETEUylwcuvWV4uTiIiIiEhGoMDJk8Y4iYiIiIiIR1Lg5E4+PoBP/EzXiFVXPRERERERT6XAyd00l5OIiIiIiMdT4ORufgHx/5VVT0RE0lCJEiUwZsyYFN//22+/Ra5cuTxiH+zfvx8+Pj7YsGFDqtKfP/fcc+laLsk4FixYgIoVKyI2VkMhbhTnX+vQoUOa7peMToGTu/le66qnwElEJE089NBD5qDbeWnTpk2m3MIMeHLkyJFoPSeeffzxx91SJknawYMH0a5dO4SEhCBfvnx48cUXEROT/JQkHTt2RLFixRAUFGQmFX7wwQdx9OjRhNsXL16MTp06mduyZs2KGjVq4IcffnB4jvHjx+P2229Hzpw5zdKiRQusWrUqydfs27ev+dykNPhu3769R+32QYMG4ZVXXoGf37XjrCT8/PPPqFChgtm2VatWxW+//Xbd5+b2rlWrFrJkyYIyZcqYz6CzTz/91Jy84PPWq1cv0baOjIzEU089hdy5cyNbtmy45557cOLEiZuuK1b5hg0bhpv18MMPY926dfj777+R3j69zvZyxm3u/B3Px6Y3BU7upq56IiJpjkHSsWPHHJaffvrJq7Z03rx5zQGXeA62fvBAOCoqCsuWLcN3331nDgBfe+21ZB/XrFkzTJkyBTt27MDUqVOxZ88e3HvvvQm387mqVatmbtu0aRP69OmDXr16Yfbs2Q4H0w888AAWLVqE5cuXo2jRomjVqhWOHDmS6PV+/fVXrFixAoUKFUq2XEuXLsWff/7psI7XWR53+ueff8w2YjCSHJaT2+SRRx7B+vXr0blzZ7Ns3rw5ycfs27fP7EPuE7aIspXz0UcfxR9//JFwn8mTJ2PAgAEYOnSoCTyqV6+O1q1b4+TJkwn3ef755zFr1iwTuC1ZssQEwl26dLmpusIWIvvX4GNHjRqF6OgbG0cfGBiI7t2746OPPkrV45o2beoymExKSraXK6GhoQ7f8QcOHEC6s3mZCxcucBpz899doqKibNOnTzf/be+Xs9mGhtpsxza5rTzi+RzqjMgtqDdXrlyxbd261fy3xMXF2S5HXXbLwtdOqd69e9s6deqU5O2LFi2yBQQE2P7666+Ede+++64tb968tuPHj5vrTZo0sT311FNmCQ0NteXOndv2yiuvOJTj7NmztgcffNCWI0cOW3BwsK1Nmza2nTt3Jtz+zTff2MLCwmxz5861VahQwZY1a1Zb69atbUePHnUoz/jx483tWbJksZUvX9726aefJty2b98+85s1depUW9OmTc3rVKtWzbZs2bKE98Lb7ZehQ4ea24oXL2774IMPEp5r1KhRtipVqthCQkJsRYoUsfXr18926dKlROU9d+6cLTY2NtF2s8oyefJkW+PGjW1BQUG2OnXq2Hbs2GFbtWqVrXbt2uY9cjucPHky4XF8ruHDh9sKFy5sCwwMtFWvXt32+++/Ozz3ypUrbTVq1DDbgM8zbdo081rr169PuM+///5rnpuvkS9fPlvPnj1tp06dSrid+6x///62lGKZKleunGg9y8d9nR5+++03m6+vb0I9o7Fjx5o6dvXq1RQ/z4wZM2w+Pj7Jfrbbtm1r69OnT5K3x8TE2LJnz2777rvvHNYfPnzY7KvNmzcnqkPODh48aLv33nttffv2NduN/++77z6zPil///13Qv1hPXzmmWds4eHh5jaWhfvX/nPEesrPxeXLl811lun111+33X///aYuFypUyPbJJ584vAY/tyzX9XTt2tXWrl07h3X16tWzPfHEE0k+ZtCgQYnqTbdu3cxn21K3bl1TBvvPAMv5zjvvmOvnz58330E///xzwn22bdtm6vzy5ctvuK7Mnj3blP/ZZ581779Ro0a2kSNH2qKjoxPdl5/R/Pnz2956662EdUuXLjXl+vPPPxPWLVmyxHxuIyIibCnFzyK/T5LDbWJ911xve7lifV/dzG/ajcQGCpzcfTAzqlJ84HR4rTuKIhmEAie51fXG1Y8MA5gq31Zxy8LXTqvAiV588UVzAMYDmHXr1pkDAx6M2v/wZ8uWzRyIb9++3TZx4kRzkPbFF18k3Kdjx462ihUrmgBsw4YN5sCpTJkyCdubP+w8CGnRooVt9erVtrVr15r7d+/ePeE5+LwFCxY0gdHevXvN/1y5ctm+/fZbh2CFgRUPihik8ICIZefBEA+gxowZYw6mjh07ZhYrGHI+6OXlhQsXmudcsGCBORjlQWlqAyeWhcEg60f9+vVNoMOg7p9//jHbktuAB9CW0aNHm/L99NNPZlvywJPbxTo4ZnkZtHK78GB91qxZtlKlSjkETiwT7zNkyBBzgMnXadmypa1Zs2ZJBk4MILkNknLo0CFzYMqgz8LnZUCyZ8+eJB/HA/vkluQOul999VUTYNjjfud75WunxJkzZ8wBPw+Kk8PbBw4cmOTtFy9eNMELt7eF+53blHWKrhc4WQYPHmzew0svvZTs/Xbv3m22EZ+T+58H6jVr1rQ99NBDCfdh4HXbbbeZ+s06z7qyZs2ahNtZJgZ8PKjm5+Gjjz6y+fn52ebNm5dwH55cGDFixHXLXbRo0UTv77XXXjOPT8rtt9+eKED/+uuvTR0nfiZZnl9//dXhPr169TLfGcTPH7cX67W9YsWKmc/LzdQVfqfxM8rvq+vVqTlz5pjty+8n1gd+7p5//nmH+zBg5eeEJ2nSI3Dib8z1tpcrfH4+jtuMATjvy++P9A6c7CYSEveOcdLgRRGRtMIuShw3YO/ll182C7355puYP3++GQPEbjm9e/c240jssSvTBx98YPrOly9fHv/++6+5/thjj2HXrl2YOXOm6arUsGFDc3+OKeFjpk+fjvvuu8+sYxcZdp8pXbq0uf7000/j9ddfT3gNdk1hVxqri07JkiWxdetWfP7556ZMlhdeeMF026Hhw4ejcuXK2L17txmbERYWZspYoECBZLeJfeIEjiXgNuA4ls8++yxV25ZlYTca6t+/v+nqxIH4jRo1MuvY7cm+m87IkSMxePBg3H///eb6u+++a7qLcewMxzX8+OOPiIuLw1dffWXGKPC9HT58GP369Ut4jk8++QQ1a9bE22+/nbDu66+/Ntt7586dKFeuXKJy5smTJ2G7u1KkSBHzPr755hvcdtttZh0vN2nSBKVKlUrycddLWMHuQ0k5fvw48ufP77DOus7bksNtyO0QERGB+vXrO3TDc8ZufRzjxnqU3POxKx7HOlm4b/z9/fHss88iJdjNb+DAgWaMHbtXnTt3zuxn1unChQsnuv8777yDHj16JNTFsmXLmm5g3OZjx441+59lZrdDlmHatGlmrE7t2rUdnod17aWXXjKXue/5OeRns2XLlmYdu2xdr5thcvsjuX2R1GMuXryIK1eumG3Abnau7rN9+/aE52A3OOexifavfSN1Ze7cueb7gV0w2fWP25BdOvm942qsV9u2bc33GfdJnTp1zPg47iN77O7L75j06gZ3+vTp624vV/idzO8A1pULFy6Y7xl+F2/ZssV8ttOLAid3U1Y9Eckggv2DsbL7Sre9dmpw/AEPxOzZZ4zjQQsDHf7oFi9e3Bx0OePBKQMSS4MGDcwBIX/kt23bZg4wOYjZwkHe/DHnbfYHHfYH7xy8b/Xbv3z5shmHwUCDBy8WDv7mgYo9ltP+OYjPw8AppTj2hAdFPBjhQR5fhwPUeSCemrFQ9mWxDnY4qN5+nfUe+To8gLOCKguvb9y40Vzm9uJz2g/s5ra2x/sy2HIOhonb0FXgxINFLsnhducA+NGjR8PX19cEca7qgj0mA3AHJgZgXeEBLA+OrTFM9nWUuJ04xonJIBiEujJixAhMmjTJjHuytvvatWvx4YcfmjEmzs+ZXPZDju9p3rw57rjjDhOEL1y40Kx3FThxP3IMln3iCvZ+YuDMsUPMgsfEFQyiGdTyQNgKkOw51w9et09iwQDGvj4xyUKlSpVcnkTJTLgNZ8yYYU6+cN9yOzMw5fZNKkkGA44qVaqYsVasA0x44Sw4ONh8TySFJzTsT2pw+3OMnP3nj2VigpO0wn1uXw9YV1h/GHi/8cYbSC8KnDwlOURslLtLIiKSLB5MhQRkjGQDPHN6vQNcawD72bNnzcLHpLWAgGtTTthtQx4oUnh4uPnPA1z7AIycD3Lsn8c6qOXBUErxQJZZz9iK89Zbb5kgkgPoeSDOAeSpCZxclcV5XWrKlhLcVkyLzBYRZ1YgeSP4nDxQZDIEBtNsIbRPuuCKq+DNXs+ePU0roytsFXTOFmZlUrteiyFb0LgwSOQBIlvbeHBqf/DIJAN8Twz+GFgldaDMwImBtH0QzMxpDHjtD255koAtSgxKWIecWQGx/f62b8FytR+feOIJly1a9q/7119/mc8AB/zzBEP27NmRGtxObPmxsPXJvqXQOonCbe6cyY7Xk9sXST2GLY0MMFhuLsk9L//zc3f+/HmHVifn+6S2rlittAxSiHWaLcTJ4YkHntzgPuQ+tj8JYuH3I5PNJIUt1127dk24zhYsJuawT3aRVAsg99X1tldK8DuIrdJsiU9PCpzczf9aZK/ASUTkluHBArNaMWhhRid2i+OBJFsdLCtXOrau8SCVXYv4I88DV7bY8D5WV70zZ86YrGf2Z7aTw5YZHkzs3bvXHGjcKB4cXW+uGp5J5oERW8ys98juXOmNB5N8j+xKxe5YFl6vW7euucxt+f3335vWL6uVgNvaHlM/M2McuxiypS+t8Lm479lFj9uR3cx48Jucm+mqxyCHgSsDFKaXJnYZ5WNSWm/sA5WrV68mrGMLA4NjBpdJpaF/7733zOszAxy7ZtljinPnoIetPlzPFqzrSa7roP1+5EF9cic1eEKD74EZ59idkK0WzChnz7l+8DrrkYUH0FbwYO1nV6/J/cFupvbdWLk/nFu0nB/jnLLc/jGsR+xayOdlhj5rf/G61QLD23mgz3VW5j9+d7BlzHqem6krzGrH5XoYvDHQ79atm2ktZ+shuyRbr2d9V/KzyW2aFAai9i36/AzxOVLSOpuS7ZUS/A5k2dn9MF3ZvIzHZdX7slV8cogt/w1KFkm2zojcyHdNKiU3kNbTMTkEs69ZyRKsxcrAxmxiTGpwzz33mOvMcsesee+9916i5BAcKM2EBj/++KMZ1D5u3LiE+zABRaVKlUyWMCaH4Gs6J4dwzvrEAdD2P73MqMdMeR9++KEZ6L5p0yYz0JwZ8OwTMthnl+OAaq6zBmtzgD2vMxMW36N99jFr4DvLx/tw0D8TH0yYMMFkTrMfoJ7S5BD2ZbGy+tkPcnd+3ywDB85PmjTJbEsmEnBODpEnTx6TJW/Lli1mwDq3o/1rHTlyxCSHYGIMJnNgkgEmqGBSAe5Pa5/ZD9r/+OOPbc2bN79ufWE5OMicy4oVK2zpiWVlZsNWrVqZfcL3YCW9sM8wyMQdzG5HLBPfC7fF/v37TWKBhg0b2kqXLm2LjIw092HSDyYD4PPY13kmkrAwWQKToPzyyy8O97HPrOgspckh7DOkJWfjxo2mvjODGt8Ptz2/o6yMalaCggEDBpjr/Dww06J99jmWifWJmTD5mWFGPe47bksLE0Ywacn18LPj7+9vMs8x6QgTirBuMoOjhQkvmD3TPkEDtzUTzPAxzILp/Pqs6yw3k7zwe/Txxx832TftM+QxgQoTG3DfMflFgwYNzJKaunKzXnjhBVuJEiXMMTH3HbMdOmcZ5OeZ+yQ1mqQyq15Kthf3gX3yEWbF/OOPP8z3GRPvMMsik53wO8QVZdXLLIHTtx3iA6eNU9xWHvF8CpzkVtebjB44Oafo5sKDUesHl5nsTp8+nfAYZrPjQSUPUKwf/ieffNIc3PAgLWfOnLaXX37ZZTpyBgk8GGRWPVfpyJMLnOiHH34wqbj5+nydO+64w6TjTmngRCwng7/k0pEzWxfft1VWBk+3InDicw0bNswEajwodZWOnCmYuZ7bgNuC+8P5tbht77777oT078wc9txzzyXsk9Rm1XPOlOYqNXl6YPBz1113mffAgJGZ7+zTRVvblNvbCh6Y6Y7ZFnlwyQNd7m8rsEquznObWLgtXN3Hqi+3InAiBr7MiMgTEzwZwQx2Vkpspk+vWrVqQkBIPInA9269X5aJn2Fm32MAU6BAAXPiwR4DRh5EM1C/nilTptjKlStn6h7rAAN3e9y29tvR2kfWZ5ZBhasggcEuAyPeh+m2nYNyfrfyO4afeb4P1m0GsqmpKzeD74FBI0/8WFjn+H332WefJaxj4JZcWvC0CJxSsr34nNwXFn72rfszrTrT7yeXRTCtAicf/oEX4UBVDrplBo7kmtPTE/tQs5mXzYkBP/cEds4FOn4M1HLdH1nEoc44jZkQSY96w64ZHGjMLG+3YjZ2T8NuLjVq1HAYcO4N2EWGv5P8fbTvtpiZ8TCIXTCffPJJMwmneHa9YXdNdq2z716XVDINlim5zIKSPGaoY+IPZq50TliT0b5rkvtNS01s4B3fih4qNs6GM5HxA2v3Hj9jrouIiMitcerUKZPim+mdUzKORzKO//3vfyZjZlonKvEmTM4xYcKENA+aMjK3Bk7MmsLsLxw4yiw8nPviejj4kYMLmQWHg87s54rISDae8UHTUX9h8b5L5vpPS3ei8bsLMXfzMXcXTURExCtwADvn1friiy9MGmzJPJitjinHvaXlND0wWYg1Z5t4QFY9ppjkhGmcQ8E+ZWFS2MTGCQCZ9pC56ZlxgxlAmIo0I+3YP7acwNc7+UG+iqv+8d1nsiAaxy9Eot/EdRjbsxbaVLnx9KoiInJzeJJOMj8vG62QKbhKiy7iFYHTXXfdZZaU4rwI7JvIdKrE1JOch4LzFWSUwInd8d787b+ZkK8i0PwP8okyozTZcW/4rK1oWakA/HxTNgGdiIiIiIikrww1j9Py5ctdzjGQ3ABBznFgP88BB4BZg6a53Gor953F8Yssj49D4MQWJ2LwdOxCJJbvPol6Jf/LiS/ezaqr7qiz4p31hnMU8Ww858bQGAHva4Hhf+13Ub2RzPJdExsba16Lv23Ov4mp+Y3MUIETB29ywkB7vM5g6MqVKy4nrXvnnXcwfPjwROvnzZuXqpnS08ra0wyY/psRPhLxXfWCEOVYvr9X4sw2dSEQR5z8TuRW1BuOO2U3aM4Ynz17dm10L3PpUvz4WxHVG8kM3zURERFmWbRoUaJAjeszZeB0I4YMGeKQXpRBVtGiRdGqVSu3pCPPve8sJuxak3D9qs2xxcnS6vZ6anESh7MhPPht2bKl0pHLLas3J06cMN+ZTN3KE00MpiRz4xlZjj/OmjWr9reo3kim+K6Ji4szr5U7d25Uq1Yt0etZvdEyXeBUoEAB80Nuj9cZALlqbSJm3+PijAcR7pgPp0GZfCgQmgXHL0aa7noJLU4+8S1O3JUFwoLM/TTGSTyl3krGdqP1pnDhwvDz88Pp06fTpVzimQczVg8OBcqieiOZ5bvG19fX/KYFBsY3WNhLze9jhgqcGjRoYCZztMezqVyfUTAYeqVtBTw9aYMJkuzHOFnVZmiHSgqaRMTtrO56TNms8XXegfuZU4XccccdOkkjqjeSab5rAgMD0yQ1vVsDp/DwcOzevdsh3fiGDRuQK1cuFCtWzHSzO3LkiJl8i5iGnBPVDRo0yKQwX7hwIaZMmYI5c+YgI2ldOT8eLheHOceCEXn5vzFO+UOzYFjHykpFLiIeha1OXCTz437m4Gl2z1TrtqjeiL5rHLl1VrA1a9agZs2aZiGOReLl1157LWHG4oMHDybcn6nIGSSxlYnzPzEt+ZdffplhUpG7Opub0OLkwzFOGj8gIiIiIuKJ3Nri1LRp02Qnn/v2229dPmb9+vXIyOwnwI30/a/F6cRFTYArIiIiIuKJ3Nri5I2cJ8CNtBvjZIWQnACX9xMREREREc+gwOkWW+U8Aa4tvsUpy7V5nKwJcHk/ERERERHxDAqcbrGTl5iGHIlbnMwYp6TvJyIiIiIi7qPA6RbLlz3I4bqVHIJjnJK7n4iIiIiIuI8Cp1usbslcZgLc+E55bHGyuurFtzixA1/BsCBzPxERERER8QwKnG4xawJcMhPg2v5rcdIEuCIiIiIinkmBkxsnwOWEt1aLk79PHAqHBmBsz1qaAFdERERExMO4dR4nb1Y9tw2DetyBjftPAT/Fr1vyfH34BYe6u2giIiIiIuJELU5u7rZXr1yh/67HOSaIEBERERERz6DAyd18fAA/JosAEH3F3aUREREREREXFDh5goBrqcdjODGuiIiIiIh4GgVOnsDfCpzU4iQiIiIi4okUOHlS4BQd6e6SiIiIiIiICwqcPKrFSYGTiIiIiIgnUuDkUWOcFDiJiIiIiHgiBU6eQC1OIiIiIiIeTYGTJ9AYJxERERERj6bAyROoxUlERERExKMpcPIEGuMkIiIiIuLRFDh5ArU4iYiIiIh4NAVOnkBjnEREREREPJoCJ0+gFicREREREY+mwMkTaIyTiIiIiIhHU+DkCdTiJCIiIiLi0RQ4eQKNcRIRERER8WgKnDyBWpxERERERDyaAidPoDFOIiIiIiIeTYGTJ1CLk4iIiIiIR1Pg5Ak0xklEREREJHMFTq+//joiIiISrb9y5Yq5TW6AWpxERERERDJX4DR8+HCEh4cnWs9girfJDdAYJxERERGRzBU42Ww2+Pj4JFq/ceNG5MqVK63K5V3U4iQiIiIi4tH8U3rHnDlzmoCJS7ly5RyCp9jYWNMK1bdv3/QqZ+amMU4iIiIiIpkjcBozZoxpbXr44YdNl7ywsLCE2wIDA1GiRAk0aNAgvcqZuanFSUREREQkcwROvXv3Nv9LliyJhg0bIiAgID3L5V00xklEREREJHMETpYmTZogLi4OO3fuxMmTJ81le3fccUdals87qMVJRERERCRzBU4rVqxA9+7dceDAAdN1zx7HPXG8k6R2LwTF/4+LAWJjAL9U7xYREREREUlHqT5CZwKIOnXqYM6cOShYsKDLDHtyg4ETxUQCftm0CUVEREREMnLgtGvXLvzyyy8oU6ZM+pTIGzkHTlkUOImIiIiIZOh5nOrVq4fdu3enT2m8la8v4Bf4X+AkIiIiIiIZr8Vp06ZNCZefeeYZDBw4EMePH0fVqlUTZderVq1a2pfSG/gHA7FRQLQCJxERERGRDBk41ahRw4xlsk8GwfmcLNZtSg5xM3siC3BVLU4iIiIiIhk2cNq3b1/6l8TbaS4nEREREZGMHTgVL148/Uvi7TSXk4iIiIhI5smqN3PmTJfr2U0vKCjIZNsrWbJkWpTNOwMnjXESEREREcn4gVPnzp0TjXdyHufUuHFjTJ8+HTlz5kzLsmZuAcHx/5VVT0REREQk46cjnz9/Pm677Tbz/8KFC2bhZaYpnz17Nv766y+cOXMGL7zwQvqUODMnhyAFTiIiIiIiGb/FqX///vjiiy/QsGHDhHV33nmn6ab3+OOPY8uWLRgzZoxD1j1JyZ5Qi5OIiIiISKZpcdqzZw9CQ0MTree6vXv3mstly5bF6dOn06aE3tbiFH3F3SUREREREZGbDZxq166NF198EadOnUpYx8uDBg0yXfho165dKFq0aGqf2rsljHHiZE4iIiIiIpKhu+p99dVX6NSpE4oUKZIQHB06dAilSpXCjBkzzPXw8HC88soraV9arxjjpBYnEREREZEMHziVL18eW7duxbx587Bz586EdS1btoSvr29C5j1J7Z5Qi5OIiIiISKYJnIgBUps2bcwiabUnNMZJRERERCRDB04fffSRyZjHzHm8nJxnn302rcrmXTTGSUREREQkYwdOH3zwAXr06GECJ15OCie/VeB0o3tCY5xERERERDJ04LRv3z6XlyUt94TGOImIiIiIZJp05JaoqCjs2LEDMTExaVsib6UxTiIiIiIimSdwioiIwCOPPIKQkBBUrlwZBw8eNOufeeYZjBgxIj3K6B00xklEREREJPMETkOGDMHGjRuxePFiM+bJ0qJFC0yePDmty+eFY5wi3V0SERERERG52XTk06dPNwFS/fr1TTIIC1uf9uzZk9qnk0RjnBQ4iYiIiIhk+BanU6dOIV++fInWX7582SGQkhsd46TASUREREQkwwdOderUwZw5cxKuW8HSl19+iQYNGqRt6bxyjJMCJxERERGRDN9V7+2338Zdd92FrVu3mox6H374obm8bNkyLFmyJH1K6Q00xklEREREJPO0ODVu3BgbNmwwQVPVqlUxb94803Vv+fLlqF27dvqU0htojJOIiIiISOZpcaLSpUtj/PjxaV8ab6YxTiIiIiIiGT9wunjxYoruFxoaejPl8V4a4yQiIiIikvEDpxw5ciSbNc9ms5nbY2Nj06ps3sX/2pxYcdFAXCzg6+fuEomIiIiISGoDp0WLFjkESW3btjWZ9AoXLpzSp5CUBE5WZr3ArNpeIiIiIiIZLXBq0qSJw3U/Pz8zCW6pUqVuqgCffvop3n//fRw/fhzVq1fHxx9/jLp167q8b3R0NN555x189913OHLkCMqXL493330Xbdq0QWYKnH5fvw858hRE3ZK54OerubFERERERDJcVr20NHnyZAwYMABDhw7FunXrTODUunVrnDx50uX9X3nlFXz++ecmuGIK9L59++Luu+/G+vXrkdHN3XoCUdfi2OG/rsMD41eg8bsLMXfzMXcXTURERETE67k1cBo9ejQee+wx9OnTB5UqVcK4ceMQEhKCr7/+2uX9v//+e7z88summyBbuvr162cujxo1ChkZg6N+E9ch0hZgrgf5RJn/xy9EmvUKnkREREREMmA6cktyySKuJyoqCmvXrsWQIUMS1vn6+qJFixZmTihXrl69iqAgu7FAAIKDg/HPP/8k+Tp8DBfn7IDs9sfFHazX5f/YOBuGzdwCG8uKQABXkAXxt3Mdt/DwWVvQtGxuddvzYvZ1RkT1RvRdI55Ev1GSketMasqQ4sCpS5cuDtcjIyNNV7msWR2TGEybNi1Fz3f69GmTgS9//vwO63l9+/btLh/DbnxspbrjjjvMXFILFiwwr5dcJj+OiRo+fHii9Zy4l61b7jR//nzsuuCD4xfjM+hdxbUWJ8S3OFnB07ELV/HJ5LkoG8Zr4s1YZ0RUb0TfNeKJ9BslGbHOREREpH3gFBYW5nC9Z8+euNU+/PBD07WvQoUKprWLwRO7+SXVtY/YosVxVPYtTkWLFkWrVq3cNucUI1tWlJYtWyJ222lg679mfaQt0DQxWS1O9kpVroG21Qq6obTiCezrTEBAfIAtonoj+q4RT6DfKMnIdSalc9WmKnD65ptvkJby5MljMvOdOHHCYT2vFyhQwOVj8ubNi+nTp5vWrjNnzqBQoUJ46aWXks3slyVLFrM4405y947i6xfM8V+LXUKLE8c4OTUu8X7uLq+4nyfUW8l4VG9EdUb0XSOeKMADjmtS8/puSw4RGBiI2rVrm+52lri4OHO9QYMGyT6W45w4f1RMTAymTp2KTp06IaNiyvGCYUFmLFOkGePk2OLE9byd9xMRERERES/MqscudOPHjzfzMm3bts1kybt8+bLpfke9evVySB6xcuVKM6Zp7969+Pvvv838TQy2Bg0ahIyK8zQN7VDJXL5qcxzjZKXe4O2az0lEREREJINm1btZ3bp1w6lTp/Daa6+ZCXBr1KiBuXPnJiSMOHjwoMm0Z2EXPc7lxMApW7ZsJhU5U5TnyJEDGVmbKgUxtmct2KYFAXH/pSMvEBZkgibeLiIiIiIiXho40dNPP20WVxYvXuxwvUmTJmbi28yIwZFtc2Fg+zr0rFMAXarVN93z1NIkIiIiIuJ+bg+c5D8+AcHmf/X8WYDSubVpREREREQyauA0YcKEZG/nuCS50b1xLftfTKQ2oYiIiIhIRg6c+vfvnygPOyeOYpY8TiirwOkmXGtxUuAkIiIiIpLBs+qdO3fOYQkPD8eOHTvQuHFj/PTTT+lTSm+hFicRERERkcybjrxs2bIYMWJEotYoSSX/ay1O0eqqJyIiIiKSKedx8vf3x9GjR9Pq6byTWpxERERERDLHGKeZM2c6XLfZbDh27Bg++eQTNGrUKC3L5n00xklEREREJHMETp07d3a47uPjg7x586J58+YYNWpUWpbN+6jFSUREREQkcwROcXFx6VMS0RgnEREREZHMPsZJ0oBanEREREREMkeLEx0+fNiMdTp48CCioqIcbhs9enRalc37aIyTiIiIiEjmCJwWLFiAjh07olSpUti+fTuqVKmC/fv3myQRtWrVSp9SeougsPj/V867uyQiIiIiInIzXfWGDBmCF154Af/++y+CgoIwdepUHDp0CE2aNMF9992X2qcTe1nzxf8PP6ntIiIiIiKSkQOnbdu2oVevXglzN125cgXZsmXD66+/jnfffTc9yug9suWN/3/1gibBFRERERHJyIFT1qxZE8Y1FSxYEHv27Em47fTp02lbOm8TlAPwC4y/fPmUu0sjIiIiIiI3Osapfv36+Oeff1CxYkW0bdsWAwcONN32pk2bZm6Tm+DjA2TNC1w8Alw+CeQoqs0pIiIiIpIRAydmzQsPDzeXhw8fbi5PnjwZZcuWVUa9tGAFTuFqcRIRERERybCBE7Pp2XfbGzduXFqXybtlu5Yggi1OIiIiIiKScSfAPX/+PL788kuTYe/s2bNm3bp163DkyJG0Lp/3UWY9EREREZGM3+K0adMmtGjRAmFhYWb+psceewy5cuUyY5w4Ie6ECRPSp6Re1+KkrnoiIiIiIhm2xWnAgAF46KGHsGvXLjOPk4WJIv7666+0Lp/3Bk7hJ9xdEhERERERudHAafXq1XjiiScSrS9cuDCOHz+e2qcTV8khSMkhREREREQybuCUJUsWXLx4MdH6nTt3Im/eawf9cuOUHEJEREREJOMHTh07dsTrr7+O6Ohoc93Hx8eMbRo8eDDuueee9Cijd1FyCBERERGRjB84jRo1yszdlC9fPly5cgVNmjRBmTJlkD17drz11lvpU0pvbHGKPA/ERLm7NCIiIiIiciNZ9ZhNb/78+Vi6dCk2btxogqhatWqZTHuSBoJyAL7+QFxMfGa9sMLarCIiIiIiGS1wYrrxbt26oVGjRmaxREVFYdKkSejVq1dal9G7+PrGJ4i4dCx+ElwFTiIiIiIiGa+rXp8+fXDhwoVE6y9dumRukzSgzHoiIiIiIhk7cLLZbCYhhLPDhw+bbnySBrLlj/+vuZxERERERDJWV72aNWuagInLnXfeCX///x4aGxuLffv2oU2bNulVTu+ilOQiIiIiIhkzcOrcubP5v2HDBrRu3RrZsmVLuC0wMBAlSpRQOvK0oq56IiIiIiIZM3AaOnSo+c8AickhgoKC0rNc3k0tTiIiIiIiGTurXu/evdOnJPIfTYIrIiIiIpKxAyeOZ/rggw8wZcoUHDx40KQht3f27Nm0LJ93ypY3/j/ncRIRERERkYyXVW/48OEYPXq06a7HtOQDBgxAly5d4Ovri2HDhqVPKb2NWpxERERERDJ24PTDDz9g/PjxGDhwoMms98ADD+DLL7/Ea6+9hhUrVqRPKb11jNOVs0BstLtLIyIiIiLi9VIdOB0/fhxVq1Y1l5lZz5oMt3379pgzZ47Xb9A0EZwL8PGLv6zueiIiIiIiGS9wKlKkCI4dO2Yuly5dGvPmzTOXV69ejSxZsqR9Cb2Rr69dSvKT7i6NiIiIiIjXS3XgdPfdd2PBggXm8jPPPINXX30VZcuWRa9evfDwww97/QZNM0oQISIiIiKScbPqjRgxIuEyE0QUK1YMy5cvN8FThw4d0rp83ksJIkREREREMm7g5KxBgwZmkTSmSXBFRERERDJW4DRz5swUP2HHjh1vpjxiSRjjpLmcREREREQyRODUuXPnFD2Zj4+PmSBX0oBanEREREREMlbgFBcXl/4lEUca4yQiIiIikjGz6tlsNuzatQtbtmxBTExM+pVKlFVPRERERCQjBk779u1DtWrVUKFCBfO/VKlSZu4mSe8WpxPaxCIiIiIiGSVwevHFF00r08SJE/HLL7+gaNGi6Nu3b/qWzptlyx//P+IsEKvWPRERERGRDJGO/J9//jEBU+PGjc31+vXro0iRIrh8+TKyZs2anmX0TiG5AB9fwBYHRJwGshdwd4lERERERLxWilucTp48aSa5tRQsWBDBwcFmvaTHnvEDQvLEXw7XNhYRERERyRAtTkw1Hh4eboIli6+vLy5duoSLFy8mrAsNDU37UnpzSvLLJ+MXERERERHx/MCJGfXKlSuXaF3NmjUTLmsepzSmSXBFRERERDJW4LRo0aL0LYkkpklwRUREREQyVuDUpEmT9C2JJNPipK56IiIiIiIZZgJccVOLkwInERERERG3UuDkZqciTl1/ElwlhxARERERcSsFTm608NBCtJnaBr/u+vU6LU7JBFciIiIiIpLuFDi50eLDixEVF4XXlr2G91a/h5i4GMc7KDmEiIiIiIhHUODkRsPqD0Pf6n3N5e+3fo8n/3wSF65eSNxVL+IMEBfrplKKiIiIiEiKsup16dIlxVtq2rRp2qop5Ovji6dqPIUyOcrg1aWvYvmx5eg+pzs+bv4xSuUoBYTk5tTDgC0uPniyWqBERERERMTzWpzCwsISltDQUCxYsABr1qxJuH3t2rVmHW+X1GtdojUm3DUBBbMWxMFLB9H9t+5YdHAR4Od/LXgCYi+dwPI9ZzBjwxHzPzbOpk0tIiIiIuJJLU7ffPNNwuXBgweja9euGDduHPz8/My62NhYPPnkkyaokhtTIVcFTGo/CQMWD8DaE2vx7KJn8WSNJ/FEtrzwjTiN57+eh5mXKiTcv2BYEIZ2qIQ2VQpqk4uIiIiIeNoYp6+//hovvPBCQtBEvDxgwABzm9y4XEG5ML7VeDxQ4QFz/bMNn+GpLLEI9/GB32XHzHrHL0Si38R1mLv5mDa5iIiIiIinBU4xMTHYvn17ovVcFxcXl1bl8loBvgF4ud7LeL3h6+byP/6R6FGoAAIDjzvcz+qoN3zWVnXbExERERHxhK569vr06YNHHnkEe/bsQd26dc26lStXYsSIEeY2SRt3l70bEZfz4OO1z2JvIHC42Hr4HamK2MsVHIKnYxcisWrfWTQoHT8WSkREREREPCBwGjlyJAoUKIBRo0bh2LH4bmIFCxbEiy++iIEDB6ZDEb1XNpRCqwM1sa/wP9gQlAXBRb9D1KmWiDrT1KGx8OSlSLeWU0REREQks0t1Vz1fX18MGjQIR44cwfnz583Cy1xnP+5Jbl6+7EG4HJMXXx87gcYXAuHjY0OWfPMQXGQC4BvhcD8REREREfGwCXA5zunPP//ETz/9BB8fH7Pu6NGjCA8PT+vyebW6JXMhLmseBAB45nQ0rhy9B7Y4f/hn346sJT+GX9Bhk12P9xMREREREQ/qqnfgwAG0adMGBw8exNWrV9GyZUtkz54d7777rrnONOWSNvx8fXDvHbWABUAenwuIuXAbIiILI7jID/ANPIPg4mPRqsRT8PVprk0uIiIiIuJJLU79+/dHnTp1cO7cOQQHByesv/vuu80kuKn16aefokSJEggKCkK9evWwatWqZO8/ZswYlC9f3rx20aJF8fzzzyMyMvOO8WlUvZL5n8vnInwRh7irhXB539Pwu1IVPr6xmHbwI7z8z8uIiP6v656IiIiIiLi5xenvv//GsmXLEBgY6LCewQ/HOqXG5MmTzfxPbKVi0MSgqHXr1tixYwfy5cuX6P4//vgjXnrpJTNfVMOGDbFz50489NBDprvg6NGjkSllzWP++SMOk3uWw9GYbGZM020lumDitgkYs24MZu+dje1nt2NU01EoFVbK3SUWEREREcl0Uh04ca6m2NjYROsPHz5suuylBoOdxx57LCGNOQOoOXPmmMCIAZIzBmyNGjVC9+7dE4K1Bx54wKRDTwq7D3KxXLx40fyPjo42iztYr5vS1/cPzgWfK2dRI2ckauSLD4xscbHoUb4HKuasiJeWvoTd53fjgdkP4LV6r6FV8VbpWn7x/Dojonoj+q6RW0W/UZKR60xqyuBjs9msuVRTpFu3bggLC8MXX3xhAqVNmzYhb9686NSpE4oVK4ZvvvkmRc8TFRWFkJAQ/PLLL+jcuXPC+t69e5tMfTNmzHDZ4vTkk09i3rx5Zg6pvXv3ol27dnjwwQfx8ssvu3ydYcOGYfjw4S6fi6+fETTbNgShkUewrPQgnAqtkuj2S3GXMCViCvbF7DPXGwQ2QOvg1vD3SXVcLCIiIiLiNSIiIkyjzIULFxAaGpq2gRNbltidjg/btWuXGe/E/3ny5MFff/3lsoudK8zCV7hwYdOK1KBBg4T1TGu+ZMmSJFuRPvroI7zwwgvm9Zndr2/fvhg7dmyqWpw4Nur06dPX3TjpGdnOnz/fJNYICGDOvOT5TewM3wP/IKbTWNiq3OfyPjFxMRi7aSy+2RofuFbJXQXvNn4XBbMWTPPyi+fXGRHVG9F3jdwq+o2SjFxnGBswjklJ4JTqJokiRYpg48aNZnwS/zMF+SOPPIIePXo4JItID4sXL8bbb7+Nzz77zIyJ2r17t0lW8cYbb+DVV191+ZgsWbKYxRl3krt3VIrLkL2A+ed/5Swf5Pq5EIABtw1A7QK1MeSfIdh8ZjN6zO2BEbePQKPCjdK66OImnlBvJeNRvRHVGdF3jXiiAA84rknN699QXy5/f38TKHG5UYzsOGHuiRMnHNbzeoEC8YGCMwZH7Jb36KOPmutVq1bF5cuX8fjjj+N///ufmZw3U8p2rRXv8snr3rVJ0SaY0n4KBiwegG1nt6Hfn/3Qt3pfPFHtCfj5aoJiEREREZEbkepIg8FOs2bNcPbs2UQBD29LKWblq127tkMKcyae4HX7rnvOfRCdgyPrNVPZ4zBjyZo3/n/4qRTdvUj2Ivi+7ffoWq4rbLBh7MaxeHLBkzgb6bjPREREREQknQInBigcM8SxTVu2bEl0W2owFfn48ePx3XffYdu2bejXr59pQbKy7PXq1QtDhgxJuH+HDh3MeKZJkyZh3759pm8kW6G4PjVBW2ZucbJk8cuCVxu8ircbv40gvyAsO7oMXWd1xYaTG9KvnCIiIiIimVSqu+pxzqSpU6dixIgRpmXo+++/Nxn1rNtSm6Hv1KlTeO2113D8+HHUqFEDc+fORf78+c3tBw8edGhheuWVV8xr8D/njGI2PwZNb731FjK1rNcCp3DHbo0p0aF0B1TIVcF03dt/cT/6zO2DgXUGokfFHqneXyIiIiIi3irVgRNbldi68+GHH6Jy5com+GEgY407Sq2nn37aLEklg3AorL8/hg4dahavki11XfWclc1ZFpPaT8JrS1/DvAPz8O7qd7H+5HoMbzgc2QKzpW1ZRUREREQyoZvKpsCkDL///jvGjBljutVJOrc4XT7FgWA39hQBWTGyyUi8VPclM78TA6gH5jyAned2pm1ZRUREREQyoVQHTsWLF3cYT8REEStWrMChQ4fSumzinBzCFgtcOXfD24Vd89hF75s23yB/SH7Tda/HnB6YsXtG5k6uISIiIiJyqwMnJmXInTu3w7oyZcpg/fr12Lt3r3ZIevAPBIJypDpBRFJq5KuBKR2moEHBBoiMjcQrS19BvwX9cPjS4Zsvq4iIiIhIJpRmEx8FBQWZ1ihJJ9niE2Yg/OYDJ8oVlAtjW4zFMzWfQYBvAJYeWYq7Z9yNr/79CtFx0WnyGiIiIiIiXhU45cqVC6dPnzaXc+bMaa4ntUh6pyS/sQQRrnBC3MerPY6pHaeiboG6pvVpzLox6Da7m9KWi4iIiIikNqveBx98gOzZs5vLTAQh7pwEN21anOyVDCuJL1t9iZl7ZmLkmpHYdW4Xev3eC13Ld8WztZ5FaGBomr+miIiIiEimC5x69+7t8rJ49iS4qU0c0alMJ9xR5A6MWjMKM/bMwOQdk7Hg4AIMrjsYrYu31rxPIiIiIuK1UtRV7+LFiyleJOO1ONnLGZQTbzZ+E1+1+golQkvg9JXTeHHJi3hqwVM4En4kXV9bRERERCRDtzjlyJHjuq0NTGfN+8TGxqZV2cRVi1M6B06WugXr4peOv5hkEV/++yX+PvI3Ok/vjCdrPImelXqahBIiIiIiIt4iRYHTokWL0r8kksJJcG9N4ERZ/LKYQKlNyTZ4Y/kbWHNiDUavHY3Ze2djaIOhqJa32i0ri4iIiIiIxwdOTZo0Sf+SSPJyFIv/f3o3EHMV8M9yy7ZYqbBS+Lr115i+ezpGrR2Fned2oudvPdGtfDeTPCJ7YHziEBERERGRzOqG53GKiIjA9u3bsWnTJodF0km+ikC2AkD0ZeDA0lu+mdkN8+6yd2Nm55noWLojbLBh0o5J6DS9E+btn2e6aoqIiIiIZFapDpxOnTqF9u3bm/TklStXRs2aNR0WSSccY1a2RfzlnfPctpk5ce5bjd8y6cuLhxbHqSunMHDJQDy98GkcDT/qtnKJiIiIiHhU4PTcc8/h/PnzWLlyJYKDgzF37lx89913KFu2LGbOnJk+pZR4ZVvH/9/lvsDJUq9gPTNx7hPVnoC/rz/+OvwXOs/ojO+2fIeYuBh3F09ERERExL2B08KFCzF69GjUqVMHvr6+KF68OHr27In33nsP77zzTtqWThyVagowm93ZPcCZPW7fOkwe8XTNpzG1w1TUylcLV2KumAl0H5jzAP499a+7iyciIiIi4r7A6fLly8iXLz7DW86cOU3XPapatSrWrVuXdiWTxIJCgeIN4i/v/MNjtlCpHKXwTZtvMLzhcIQGhmL72e3o8VsPvLPyHYRHhbu7eCIiIiIitz5wKl++PHbs2GEuV69eHZ9//jmOHDmCcePGoWDBgjdfIskw3fXs+fr4okvZLiZ5RPtS7U3yiB+3/2iSR/x54E8ljxARERER7wqc+vfvj2PHjpnLQ4cOxe+//45ixYrho48+wttvv50eZRR75a4FTsysd9XzWnNyB+fGO7e/gy9afoGi2Yvi5JWTeH7x83h24bM4Fh5fb0REREREMn3gxPFMDz30kLlcu3ZtHDhwAKtXr8ahQ4fQrVu39Cij2MtdBshZAoiNAvYu9tht06BQA0zrOA2PVX3MJI9YfHgxOs3ohAlbJih5hIiIiIh4zzxOUVFRpsteYGAgatWqhTx58qRtySSZtOSe2V3PWZB/kJkg9+f2P6NmvpomecT7a95H9zndseX0FncXT0REREQk/QInTnz7yCOPICQkxMzjdPDgQbP+mWeewYgRI1L7dHIjyrWK/79rPpABJp4tk7MMvm3zLYY1GIbsgdmx7ew2dP+tO0asGoHLnNBXRERERCSzBU5DhgzBxo0bsXjxYgQFBSWsb9GiBSZPnpzW5RNXijcGAkKAS0eBE5szxDZi8oh7yt1jkke0LdkWcbY4/LDtB3Sc3hELDi5wd/FERERERNI2cJo+fTo++eQTNG7cGD7sNnYNW5/27HH/3EJeISAIKNnE49KSp0Se4Dx494538XmLz1EkWxGcjDiJ5xY9Z5JHHL983N3FExERERFJm8CJ8zZZ8zg5z+9kH0jJrequ59njnJLSsHBD/NrpVzxa9VH4+/hj0aFFJnX5xK0TERsX6+7iiYiIiIjcXOBUp04dzJkzJ+G6FSx9+eWXaNDg2uSskv7KtIz/f3g1EHE2Q25xJo/oX6s/pnSYghp5ayAiJgLvrn7XjH/ackbJI0RERETEc/in9gGcq+muu+7C1q1bERMTgw8//NBcXrZsGZYsWZI+pZTEchQF8lUGTm4Bdi8Aqt2XYbdS2Zxl8d1d32Hqrqn4YM0H2Hpmq8m8171CdzxT8xmEcDyXiIiIiEhGanHi2KYNGzaYoKlq1aqYN2+e6bq3fPlyM6+TuKO7XsYa55RU8oj7yt2HmXfPxF0l7jLJIyZum2jmflp0cJG7iyciIiIiXu6G5nEqXbo0xo8fj1WrVpnWpokTJ5og6pdffkn7EkrSyl4LnHb/CWSScUFMHvFek/cwtsVYFM5W2CSMeHbRsyaBhJJHiIiIiEiGCJzYyrR582bs3LnTYf2MGTNQvXp19OjRI63LJ8kpUhcIygFcOQccXpOptlXjwo1N8ohHqjxikkcwZTmTRzCFuZJHiIiIiIjHBk4MmMqUKWMCpIoVK6JLly44ceIEmjRpgocfftiMe1I68lvMzx8oc2em6a7nLNg/GM/Vfg6TO0xGtbzVTPIITprb47ce2HZmm7uLJyIiIiJeJMWB0+DBg03gxNal+++/38zn1LRpU3To0AGHDx/GiBEjUKRIkfQtrSTdXS+DpiVPiXI5y+H7u77Hq/VfRfaA7Cbj3v1z7sf7q99HRHSEu4snIiIiIl4gxYHT6tWrMXLkSLRv3x6fffaZWffyyy/jhRdeQHBwcHqWUZJTpgWTwgPH/wUuHs2024rJI7qW74oZnWegdYnWJnnEhK0T0HlGZyw5pGyOIiIiIuIhgdPp06dRqFAhczksLAxZs2ZF/fr107NskhJZ8wBF6mT6VidL3pC8GNlkJD6981MUyloIxy4fw9MLn8aAxQNw4vIJdxdPRERERLw9cOJEt5cuXcLFixdx4cIFc/3KlSvmuv0i7uyuN99rNv8dRe4wySP6VOkDPx8/zD8w36Qu/3Hbj0oeISIiIiLuC5xsNhvKlSuHnDlzIleuXAgPD0fNmjXNdS45cuQw/8WNgdOeRUDMVa/ZBZwYd0DtAZjcfjKq5amGy9GX8c6qd/Dg7w9i+9nt7i6eiIiIiGQi/im946JFmoTUYxWsDmQrAIQfBw4sBUo3hzcpn6s8Jtw1AT/v/BkfrvsQ/57+F/fPvh8PVnoQ/ar3MwGWiIiIiMgtCZyYdlw8lI8PULYFsH5ifHc9LwucyM/XD/dXuB/NizXHu6vexbwD8/Dtlm8xb/88/K/+/0zXPhERERGRWzIBrniwsq3j/+/MfPM5pUa+kHwY1XQUPmn+CQpmLYijl4/iqQVPYeDigTgZcdLdxRMRERGRDEqBU2ZRqingGwCc3QOc2QNv16RoE0zvNB0PVX7IJI9gC1Sn6Z0wafskJY8QERERkVRT4JRZBIUCxRt4TVrylODYpoF1BmJS+0mokrsKwqPD8dbKt9Dr917YcXaHu4snIiIiIhmIAqfMRN31XKqQqwImtp2IIXWHIGtAVmw6vQndZnfD6LWjEREdcav3koiIiIhkQAqcMpNy18Y5MbPe1XB3l8bjkkd0r9gdMzrNQMviLRFri8U3m79Bl5ld8M+Rf9xdPBERERHJDFn1unTpkuInnDZt2s2UR25G7jJAzhLAuf3AviVAhXbank7yZ82P0U1HY/Ghxabb3pHwI+j3Zz+0KdEGg+sORp7gPNpmIiIiInJjLU5hYWEJS2hoKBYsWIA1a9Yk3L527VqzjreLu9OSK7teSjQt2tS0PvWq1Au+Pr6Yu38uOv7aEVN2TEGcLS7dd5WIiIiIZMIWp2+++Sbh8uDBg9G1a1eMGzcOfn5+Zl1sbCyefPJJE1SJm5VtBaz6PH4+J5stPpiSJJNHvHjbi2hXqh1eX/46tpzZgjdWvIGZe2ZiaIOhKJuzrLaciIiIiNzYGKevv/4aL7zwQkLQRLw8YMAAc5u4WYnGQEAIcOkocGKzu0uTIVTKXQk/tP0BL9V9CSH+Idh4aiO6zuqKMWvH4ErMFXcXT0REREQyYuAUExOD7du3J1rPdXFx6uLkdgFBQMkm8Ze9fDLc1CaP6FGxB2Z0noE7i92JGFsMvtr8FbrM6IKlR5a6u3giIiIiktECpz59+uCRRx7B6NGj8c8//5hl1KhRePTRR81t4gHKtoz/r/mcUq1A1gIY02wMPmz2IfKH5Mfh8MPo+2dfDPprEE5fOZ3mu0pEREREMtEYJ3sjR45EgQIFTLB07Ngxs65gwYJ48cUXMXDgwPQoo9zIOCc6vBqIOAuE5NI2TKXmxZqjXsF6+GT9J/hx+4/4fd/vJm35gNoD0KVsF5NQQkRERES8R6qP/nx9fTFo0CAcOXIE58+fNwsvc539uCdxoxxFgXyVAWaH271Au+IGcbJcpij/sd2PqJirIi5FXcLw5cPx0NyHsPvcbm1XERERES9yQ6fNOc7pzz//xE8//QSfa1nbjh49ivBwTbrqed31NM7pZlXOXdkET4NuG4Rg/2CsP7ke9826Dx+t+wiRMZE3v69EREREJPMFTgcOHEDVqlXRqVMnPPXUUzh16pRZ/+6775pse+Ihyl2bz2n3n0BcrLtLk+H5+/rjwUoPYmbnmWYOKCaPGP/veHSZ2QXLji5zd/FERERExNMCp/79+6NOnTo4d+4cgoODE9bffffdZhJc8RBF6gJBYcCVc8Dh/yYrlptPHvFx849NAol8Iflw6NIhPDH/Cbz090s4c+WMNq+IiIhIJpXqwOnvv//GK6+8gsDAQIf1JUqUMGOdxEP4+QOl74y/rO56aY4py2d0mmFSmPvAB3P2zkHH6R0xdedUxHFsmYiIiIh4d+DEuZpiYxN3/Tp8+DCyZ8+eVuWStOyup7Tk6SJbYDYzaa6VPOJi1EUMWz4Mfeb2wZ7ze9LnRUVEREQkYwROrVq1wpgxYxKuMzkEk0IMHToUbdu2Tevyyc0o04J7CDj+L3DxqLZlOqmSp4oJnl6o84JJHrHu5DrcO+tefLz+Y1yNvartLiIiIuKNgRPnb1q6dCkqVaqEyMhIdO/ePaGbHhNEiAfJmgcoXDv+slqd0j15RO/KvTG903Q0KdIEMXEx+GLTF+gyowtWHFuRvi8uIiIiIp4XOBUpUgQbN27E//73Pzz//POoWbMmRowYgfXr1yNfvnzpU0pJg+5687UVb4FC2QqZ5BEfNP0A+YLz4eClg3hs3mN4+e+XcTbyrPaBiIiISAblf0MP8vdHjx49zCIermwrYNFbwJ5FQMxVwD+Lu0uU6bH7aoviLVCvYD3TXW/S9kmYtXcW/jryFwbWHojOZTonzH8mIiIiIpm0xcnPzw/NmjXD2bOOZ89PnDhhbhMPU6AakC0/EH0ZOLDU3aXxKtkDs+Plei9jYtuJKJezHC5cvYDXlr2GPn/0wd4Le91dPBERERFJz8DJZrPh6tWrZi6nLVu2JLpNPIyvL1C2Zfxldddzi2p5q2FS+0mmtYnJI9aeWIt7Zt6DTzd8quQRIiIiIpk1cGIXo6lTp6JDhw5o0KABZsyY4XCbeKCy18Y57fzD3SXxWgG+AXioykP4tdOvuL3w7SZ5xLiN43DvzHux6tgqdxdPRERERNKjxYld8j788EOMHDkS3bp1w5tvvqnWJk9WqingGwCc3QOc0fxC7lQ4W2F8euenGNlkJPIE58H+i/vxyLxH8L9//odzkefcWjYRERERScPAyd7jjz+O33//3czr1KtXr5t5KklPQaFA8Qbxl5WW3O3YMtu6RGvM7DwT3cp3gw98MHPPTHSc3hHTd0/XSQgRERGRzBA4FS9e3CEJBBNFrFixAocOHUrrsklaUnc9j0we8Ur9V/B92+9RNmdZnL96Hq8ufdW0QO27sM/dxRMRERGRmwmc9u3bh9y5czusK1OmjJnHae9eZQrz6LTkxMx6V8PdXRqxUz1vdUxuPxnP134eQX5BWH18tUkeMXbDWETFRmlbiYiIiGT0rnr2goKCTGuUeKg8ZYGcJQAeiO9b4u7SiIvkEQ9Xedgkj2hUuBGi46Lx2cbPTADFQEpEREREMkDglCtXLpw+fdpczpkzp7me1HIjPv30U5QoUcIEX/Xq1cOqVUlnGWvatKkZI+K8tGvX7oZe22sw46FTd73YOBuW7zmDGRuOmP+8Lu5VJHsRjL1zLN6/433kDsptkkc8/MfDGLZiGCLiIrR7RERERNzEPyV3+uCDD5A9e3ZzmYkg0tLkyZMxYMAAjBs3zgRNfP7WrVtjx44dyJcvX6L7T5s2DVFR/3VfOnPmDKpXr4777rsvTcuVabvrrfrczOc099+jGD57G45diEy4uWBYEIZ2qIQ2VQq6tZjejicC2pRsg4aFG+LDtR9iys4pmLl3JuZhHnau2oku5bqgSp4qSv8vIiIi4mmBU+/evV1eTgujR4/GY489hj59+pjrDKDmzJmDr7/+Gi+99FKi+zu3ak2aNAkhISEKnFKiRGMgIAS4dBQf/Tgdx2yOXSuPX4hEv4nrMLZnLQVPHiA0MBSvNngVHUp3wPDlw7H7/G78svsXs5QOK41OZTqZ25jWXEREREQ8IHC6ePFiip8wNDQ0xfdly9HatWsxZMiQhHW+vr5o0aIFli9fnqLn+Oqrr3D//fcja9asLm+/evWqWZzfS3R0tFncwXrdW//6fvAtfjv8dv+Bpr7rsTXWMXBiRz1OYTx81hY0LZsbfr6a0NgTVM5ZGd+3+B5f/P4FjuU+hkVHFmHPhT0YvXY0Plz3IRoWbIiOpTrijsJ3IMAvwN3FFQ/ivu8ayahUZ0T1RrztuyY6FWXwsXFG2+tgMMPuQ8nh0/A+sbGxKX7xo0ePonDhwli2bBkaNLg2zxCAQYMGYcmSJVi5cmWyj+dYKHbv4/3q1q3r8j7Dhg3D8OHDE63/8ccfTUuVtwk5tBAtT3+LNXHlcG/UsCTv93SlWJQN05gnTxRpi8S/Uf9iXdQ6HIr9bxqAEJ8QVAuohlqBtVDQr6C68omIiIhcR0REBLp3744LFy5ctwEoRS1OixYtgidia1PVqlWTDJqIrVkcQ2Xf4lS0aFG0atUqVa1jaR3Zzp8/Hy1btkRAwK1tIfhzRQFgwbeo6bMLOXAJ5xE/ds1Zqco10Laaxjp5Cuc60wVdzHomj5i1dxZm75uNU1dOYUXUCrOUzVHWtEK1LdEWOYNyurv44ibu/K6RjEl1RlRvxNu+ay6momddigKnJk2aID3kyZPHTKZ74sQJh/W8XqBAgWQfe/nyZTO+6fXXX0/2flmyZDGLM+4kd+8od5QhR6Ey2BZXFBV9D+EO302YGdfI5f0K5sjq9u0j168zZXOXxYDcA9C/dn8sP7YcM3bPwMKDC7Hr/C6MWjcKH67/EHcUucOMh7q9yO0m7bl4H0/4vpOMRXVGVG/EW75rAlLx+ikKnJJq1jp48KBDhjuqVq1aip8jMDAQtWvXxoIFC9C5c2ezLi4uzlx/+umnk33szz//bMYu9ezZ8wbfgXeqWzIXfgiog4qxh9Dcb32iwIkdMguEBZn7Scbh5+uHxoUbm+XC1QuYu28upu+ejs1nNmPhoYVmyRWUC+1LtTdBVLmc5dxdZBEREZEMJdWB06lTp0wGvN9//93l7akZ40TsRsdMfXXq1DFd7piOnK1JVpa9Xr16mXFQ77zzTqJuegy2cufOndq34NWY8KF843uAJb+iie8m+CIOcdem87JGsTEluRJDZFxhWcLQrUI3s+w+txsz9szArD2zcCbyDCZsnWCWSrkroVPpTmhXqp25v4iIiIikceD03HPP4fz58yYhAyej/fXXX03XujfffBOjRo1K7dOhW7duJhh77bXXcPz4cdSoUQNz585F/vz5ze1s1WJyCnuc4+mff/7BvHnzUv16AtS74y5ELwtFzuiLqOGzG+ts8a0PbGnSPE6ZS5mcZTCwzkA8W+tZLDuyzLRCLT68GFvPbDXLyDUj0bRoU3Qu0xkNCzWEv+8NN0KLiIiIZGqpPkpauHAhZsyYYVqIGNAUL17cDOxiogW2CrVr1y7VhWC3vKS65i1evDjRuvLly5ssfnKD/PwRUK4FsGUaPqp9EmvLdEW+7PHd89TSlDlxbFOTok3Mci7yHH7b95sZD7Xt7DbMPzDfLHmD86J96fboXLozSuUo5e4ii4iIiHgUx6acFGA3unz58pnLOXPmNK1FxOx269atS/sSSvoo19r8K3L6b3SqURgNSmveJm/BLHs9KvbAlA5T8EuHX9CzYk/kzJLTZOX7ZvM36DSjE3rM6YEpO6bgYlTKM82IiIiIZGapDpzY2sOuclS9enV8/vnnOHLkCMaNG4eCBZW+OsMo0yJ+VNPxf4GLR91dGnGT8rnKY3DdwVhw3wKMaTrGdNvz8/HDptOb8MaKN9B8SnMM+muQ6eYXG5e68YsiIiIiXt1Vr3///jh27Ji5PHToULRp0wY//PCDyZD37bffpkcZJT1kzQMUrg0cWQPsmgfUfkjb2YsF+AXgzuJ3muX0ldOYs3eOGQ+1+/xu/L7vd7PkD8mPjqU7mqx8xUOLu7vIIiIiIp4dONmn/2Yq8QMHDmD79u0oVqyYmZdJMlh3PRM4zVfgJAnyBOdB78q90atSL2w9uxXTd003Y6JORJzA+H/Hm6VmvpomoUSr4q2QLTCbtp6IiIhkeqnuqucsJCQEtWrVUtCUEZVtFf9/z0Jg5x/uLo14GB8fH1TOXRn/q/8/LOq6CCObjMTthW+Hr48v1p9cj6HLhqL5z83x8t8vY9WxVYizxbm7yCIiIiKe0+LEbHa//PILFi1ahJMnT5oJa+1NmzYtLcsn6alAtWvd9dYCP3YFKnUG7noXyF5A210cBPoFonWJ1mY5GXHSzAvFrnz7L+7HrL2zzFI4W2HTlY9LkexFtAVFRETEu1ucOI/Tgw8+iH379iFbtmwICwtzWCQD4fxYvWcBDZ8FfPyArdOBT+oCq78CnAJiEUu+kHx4pOojmNl5Jia2nYj7yt2HbAHZcCT8CMZuHIu7pt2Fh/942KQ7j4iO0IYTERER72xx+v77702rUtu2bdOnRHJrBWYFWr0BVL0PmNUfOLoOmDMA2DQZaD8GyF9Je0SS7MpXPW91swy6bRAWHlxoWqFWHFuB1cdXm+XtlW+jVYlWZjxUrXy1zGNEREREvKLFia1KpUppcsxMp2A14NE/gbveAzjY/9BK4PPbgQWvA9FX3F068XBB/kFoW6otvmj1Bf645w88U/MZFMteDBExESaYemjuQ2j3azt8vvFzHAuPz8opIiIikqkDp2HDhmH48OG4ckUH05mOrx9Q7wngqVVAhfZAXAzw9yhgbENg72J3l04yiILZCuLxao9j9t2z8V2b73B3mbsR4h+CQ5cO4ZMNn6D11NZ4bN5jJuV5ZEyku4srIiIikj5d9bp27YqffvoJ+fLlQ4kSJRAQEOBw+7p161L7lOJpwgoD9/8AbJsF/DYIOLsXmNAJqHY/0Pqt+DmgRK6D3fJq5a9llpfqvoQ/D/5pWp/YhY/d+bhwbFSbkm3QqXQn0+VPXflEREQk0wROvXv3xtq1a818Tvnz59eBTmZWsQNQsgmw8A1g1Xhg06T4yXJbvQnU6M4jY3eXUDKIkICQhIx7hy8dxsw9M83ChBK/7PzFLCVCS5jJdXkfJqAQERERydCB05w5c/DHH3+gcePG6VMi8SxBoUDb94Fq3eKTR5zYDMx4Etj4U3zyiDxl3F1CyWCYqvzJGk+ib/W+WHN8DWbsmYH5B+ab1OYfrvsQH6//GA0LNTRBVLOizZDFL4u7iywiIiKS+jFORYsWRWhoqDadtylSB3h8MdBiOOAfDOz/O37s05L3gJgod5dOMiBOpFu3YF281fgtM8Hu6w1fN5n3OJHuP0f+wYtLXkTzKc3x5oo3seX0FjOHnIiIiEiGCZxGjRqFQYMGYf/+/elTIvFcfgFA4+eAJ5cDpe8EYq8Ci94CxjUGDix3d+kkA8sakBV3l70b3931nUkq8VjVx5A/JD8uRl3E5B2Tcf+c+9FlZhd8t+U7nL5y2t3FFRERES+U6sCJY5sWLVqE0qVLI3v27MiVK5fDIl4gV0mg51Tgnq+ArHmB0zuAb9oAM58Frpxzd+kkgyseWhzP1nrWpDX/vOXnuKvkXaa73u7zuzFyzUi0+LkFnlnwDBYcWIDo2Gh3F1dERES8RKrHOI0ZMyZ9SiIZCxNDVL0XKN0c+HMosG4CsO47YMfvwF0jgMpdlDxCboqfr58Z68SFLU9z980146E2ndqExYcXmyVnlpxoV6qdGQ9VIVcFbXERERHxjMApOjoaS5YswauvvoqSJUumX6kk4wjJBXT8OD5V+ezngNM7gV8eBjb8BLQbBeQs7u4SSiYQGhiKruW7mmXv+b2Yvmc6Zu+ZjVNXTmHitolmYeDEtOYMpHIG5XR3kUVERMSbu+pxzqapU6emX2kk4yrRCOj7D9D0ZcAvENg9H/isPrD0IyA2xt2lk0ykVI5SGFB7AObdOw+f3vkpWhVvhQDfAGw/ux3vrn4XzX9ujucXPY/FhxYjOk5d+URERMRNY5w6d+6M6dOnp9HLS6binwVoOhjotwwo3hiIjgDmvwqMbwocWevu0kkm4+/rjzuK3IFRTUdh4X0L8XK9l1EpdyXExMWYyXafWfgMWv7cEiNXj8Tuc7vdXVwRERHxtjFOZcuWxeuvv46lS5eidu3ayJo1q8Ptzz77bFqWTzKiPGWBh2YDG34A/vgfcPxf4MsWQN3HgeavAFmyu7uEksnkCMqBByo8YJad53Zixu4ZmL13Ns5EnsF3W78zS+XcldG5TGeTbCIsS5i7iywiIiKZPXD66quvkCNHDqxdu9Ys9nx8fBQ4iVUZgJo9gbKtgT9eBv6dAqwcB2ybFT+hboV22lKSLsrlLIcXb3sRz9V+Dv8c/gfTd0/HX4f/wpYzW8zy3ur30LxYczMeioknmIRCREREJM0Dp3379qX2IeLNsuUF7hkP1HgAmP08cG4/MKk7UKF9fAAVWsjdJZRMiuOemhVrZpazkWcxZ+8cE0SxReqP/X+YJV9wPnQo3cFk5SsZpoQ3IiIikoZjnOzZbDaziFwX05b3Ww40fh7w9Qe2zwY+qQus/AKIi9UGlHSVKygXHqz0IKZ2nIop7aegR8UeyJElB05eOYmvNn+FjtM7oudvPfHzzp9xKeqS9oaIiIikTeA0YcIEVK1aFcHBwWapVq0avv/++xt5KvEmgSFAi2HAE38BRW4DeID6+4vAV62A45vdXTrxEhVzV8RLdV/CgvsW4IOmH6BJkSbw8/HDxlMb8fry19FsSjMM/mswlh9djjhbnLuLKyIiIhm1q97o0aPNPE5PP/00GjVqZNb9888/6Nu3L06fPo3nn38+PcopmUn+ysDDfwBrvgYWvA4cWQN8fgfQ8GmgyUvxAZZIOgv0C0SL4i3McvrKaTMvFLvy7bmwB7/t+80sBbIWQMfSHc14qGKhxbRPREREvFiqA6ePP/4YY8eORa9evRLWdezYEZUrV8awYcMUOEnKcEB+3cfixzr9PgjYNhNY+iGwZTrQfjRQpoW2pNwyeYLz4KEqD6F35d4mgQQDKAZOxy8fxxebvjBLrXy1TFa+ViVaIWuAYzZRERERyfxS3VXv2LFjaNiwYaL1XMfbRFIltCDQ7XvggUlAaBHg/AFg4j3AL48A4Se1MeWWYmbQKnmq4JX6r2BR10V4/4730ahwI/j6+GLdyXV4bdlrpivf//75H1YfX62ufCIiIl4k1YFTmTJlMGXKlETrJ0+ebOZ4Erkh5e8CnloB1H8S8PEFNv8CfHIbsPY7IE7jTOTWy+KXBW1KtsG4FuMw75556F+rP0qElsCVmCuYuWcmHv7jYbSd1hZjN4zFkfAj2kUiIiKZXKq76g0fPhzdunXDX3/9lTDGiZPhLliwwGVAJZJinBi3zTtA1fuAWf2B45uAWc8CmyYD7ccAectpY4pb5M+aH49WfRSPVHnEJJFgV765++eagOmzjZ+ZpW6BuqYr353F7kRIgMbpiYiIwNsDp3vuuQcrV67EBx98gOnTp5t1FStWxKpVq1CzZs30KKN4m8K1gMcWxU+Yu+gt4MBSYFwjoPEA4PYBgH8Wd5dQvLgrX418NcwyuO5gLDi4wARRq46twqrj8QvHP7Uu0doEUTXy1jCPERERES8MnKh27dqYOHFi2pdGxOLnH59lr2IH4LcXgF3zgCUjgM1TgQ5jgBKNta3ErYL9g9G+VHuzHA0/arrvzdg9A4fDD2ParmlmKR5a3GTk4yS7zNAnIiIiXjoBrki6y1kc6D4FuPcbIFt+4Mwu4Nt2wIyngIiz2gHiEQplK4S+1fvity6/4ZvW35jWJgZWBy4ewEfrP0KrX1rhiflP4Le9vyEyJtLdxRUREZH0bHHy9fW9bpcT3h4TE3Mj5RBJrmIBVboApZsDfw4D1n4DrJ8I7Jj735godYcSD8DvwDoF6phlSN0hmHdgnmmFWnNiDZYdXWaW7AHZTdIJBldV81RVVz4REZHMFjj9+uuvSd62fPlyfPTRR4hT9jNJT8E54rvpVb8/PnnEqe3AtMeAjT8B7UYBuUpp+4vHYIIIBkdcDl08hJl747vyHbt8DD/v/NkspcJKoVOZTuhQqgPyhuR1d5FFREQkLQKnTp06JVq3Y8cOvPTSS5g1axZ69OiB119/PaVPJ3LjitUHnvgbWPYhsOR9YM9C4LMGQJPBQMNnAL8AbV3xKEVDi+KpGk+hX/V+Zv4nJpT488Cf2HthLz5Y+wE+XPchGhVqZIKspkWbItAv0N1FFhERkbQY43T06FE89thjqFq1qumat2HDBnz33XcoXrz4jTydSOr5BwJ3vAg8uRwoeQfAcSMLhgOfNwEOrdYWFY/EiXTrFayHd25/Bwu7LsSwBsNM5r04Wxz+PvI3Bi4ZiOY/N8fbK9/GljNbYLPZ3F1kERERuZHA6cKFCxg8eLCZBHfLli1m7ia2NlWpUiU1TyOSdnKXBnrNBDqPA4JzASe3AF+1BOYMBCIvaEuLx8oemB33lLsH37f9HrM6zzLzROULyYcLVy/gp+0/4f7Z9+OeWfdgwpYJOHPljLuLKyIi4vVSHDi99957KFWqFGbPno2ffvoJy5Ytw+233+71G1A8ABND1HgAeHoNUL07ABuw+kvg03rA1hmAztqLhysRVgL9a/XHvHvmYVyLcbirxF0I9A3ErnO78P6a99Hi5xZ4duGzZt6o6LhodxdXRETEK6V4jBPHMgUHB5vWJnbL4+LKtGnT0rJ8IimXNTdw99j45BGznwPO7gWm9ALK3QW0GwmEFdHWFI/m5+uHRoUbmYUtT3P3zcWMPTPw7+l/sejQIrPkCsqFtiXbmvFQ5XOVd3eRRUREvEaKA6devXopba5kDKWaAP2WA3+PBP4ZA+z8Hdj3F9D8FaDeE4Cvn7tLKHJdYVnC0K1CN7PsPrfbTLA7a+8snL5yGhO3TTRLxVwVTVa+diXbIUdQDm1VERERTwicvv322/Qsh0jaCgiKD5Sq3APMeg44tAL4YwiwaTLQ8SOgYHVtcckwyuQsgwF1BuDZWs+auaCYlY+tT9vObsO2Vdswcs1INCvazLRCNSzUEP6+Kf5qFxERkRTSr6tkbvkqAn1+B9Z9C8wfBhzbAHzRFKj/JNDsZSAwq7tLKJJiDIjuKHKHWc5HnsecfXPM3FAMoOYfmG+WPMF5zLxQbIkqlrWYtq6IiIg705GLZCi+vkCdh4GnVwGV7wZsccDyT4BP6wM757m7dCI3hF3zelTsgSkdpuCXDr+gZ8WeyJklp+nK982Wb9B5Rmf0+qMXVl5diYtRF7WVRUREbpICJ/Ee2QsA930LdP8ZCCsGXDgI/Hgf8PNDwKUT7i6dyA1jkojBdQdjwX0LMKbZGNNtz9/HH5vPbMasK7PQalorDFoyCEuPLEVsXKy2tIiIyA1QVz3xPuVaASVWAIveBlZ8Bmz5Fdi9EGg5DKj1UHwLlUgGFOAXgDuL3WkWzv00c/dM/LDhB5yIO4Hf9/9uFs4V1bF0R3Qq3cmkQRcREZGU0RGieCeObWr9FvD4YqBgDeDqBWD288A3dwEnt7m7dCI3LXdwbvSs0BNPZ38aP7T5AQ9UeMBk6jsZcRJf/vslOkzvgAd/exBTd05FeFS4triIiMh1KHAS78bseo8tBNqMAAKyxmffG3c7sOANIDrS3aUTuWk+Pj4mbfnL9V7GwvsWYlSTUbi98O3w9fHFhlMbMGz5MDSb0gxD/h6CFcdWII5jAEVERCQRddUT4bxO9fsBFTsAv70I7Pgtfg6oLdOA9mPi54USyQQC/QLRqkQrs5yKOGXmhWJq830X9mH23tlmKZS1EDqW6Wi68xXNXtTdRRYREfEYanESsYQVAe7/Eej6PZC9IHB2LzChI/BrX+DyGW0nyVTyhuTFw1UexoxOM/BD2x/QtVxXZA/IjqOXj2LcxnFoO60t+sztYwKriOgIdxdXRETE7RQ4idjz8QEqdQSeWgnc9hhXABt/Aj6pA2z4EbDZtL0k03Xlq5a3Gl5t8CoWdl2Id29/10yi6wMfrDmxBq8ufRVNpzTFK/+8gjXH18Cmz4CIiHgpBU4irgSFAe1GAo/MB/JVBq6cBab3i2+BOrNH20wypSD/ILQt1Raft/wc8+6dh2drPoti2YvhSswVzNgzA33+6GNaotgidSz8mLuLKyIickspcBJJTtHbgCeWAC2GAf5BwL6/gM8aAEveB2KitO0k0yqQtQAeq/YYZt89GxPumoAuZbsgxD8Eh8MP49MNn6L11NZ4dN6jZlwUAysREZHMTskhRK7HLwBo/DxQqRMwewCwdxGw6E1g8y9Ahw+BYvW1DSVTd+Wrma+mWQbfNhgLDi7AjN0zsPL4Sqw8Fr9kC8iG1iVao3OZzqiet7p5jIiISGajFieRlMpVCnjwV6DLeCAkD3BqO/B1a2BWf+DKeW1HyfRCAkLQoXQHfNn6S8y9Zy6erP4kCmcrjPDocEzdNRUP/v4gOk7vaOaJOnH5hLuLKyIikqYUOImkBs+kV+sKPL0aqNkzft3ab4FP6wKbpyl5hHgNBkz9avTDb11+w9etvzbpy4P9g7H/4n58uO5DtJraCn3n98XcfXNxNfaqu4srIiJy09RVT+RGhOQCOn0KVH8AmPUccGYX8Euf+Ax87UYBOYppu4pX4ES6txW4zSycZHfe/nkmhfm6k+uw9OhSs2QPzI62JduarnyVc1dWVz4REcmQ1OIkcjNKNAb6LQWavAT4BQK75gGf1gOWfQzExmjbilfJGpAVd5e9G9/d9R3m3D0Hj1d73CSZuBR1CZN3TMYDcx5Al5ld8O3mb3H6yml3F1dERCRVFDiJ3Cz/LECzIUDfpUDxRgAnC533CjC+GXBknbaveKViocXwTM1nMLfLXHzR8gvT4pTFLwt2n9+NUWtHocXPLfD0gqcx/8B8RMdGu7u4IiIi16WueiJpJW85oPdsYMNEYN6rwPFNwJd3AnWfAJr/D8iSXdtavI6frx8aFGpgFrY8zd0/12Tl23hqI5YcXmKWHFlyJHTlq5i7oruLLCIi4pJanETSkq8vUKtXfPKIqvcBtjhg5Vjg0/rA9t+0rcWrcazTfeXuw8S2EzGj8ww8UuUR5AvOh/NXz+PH7T+i6+yuuHfmvfh+6/c4G3nW3cUVERFxoMBJJD1kywfc8yXQcyqQozhw8TAw6QFgck/g4lFtc/F6pcJK4bnaz+GPe//AZ3d+ZuaBCvANwI5zO/De6vdw55Q70X9hfyw6uAjRcerKJyIi7qeueiLpqUwL4MkVwJJ34xNGbJsF7FkMtBgK1HkY8PXT9hev5u/rj9uL3G6WC1cv4Pd9v5usfFvObMHCQwvNkisoF9qXam+68pXNWdbdRRYRES+lFieR9BYYArQcDjyxBChcG4i6BPz2AvBVK+D4Zm1/kWvCsoTh/gr3Y1L7SZjWcRp6V+ptgiZ225uwdYLJyNdtdjf8tP0nE2SJiIjcSgqcRG6VAlWBR+YDd70PBGYHjqwBvmgCzB8KREVoP4jYYcvSC7e9gD/v+xMfN/8Ydxa7E/4+/th6ZiveXvk2mk1phoGLB+Kvw38hJk6p/0VEJP2pq57IrcSuefUeByq2B357Edg+G1g6Btg6HWg3GihzZ6KHxMbZsHLfWaw97YPc+86iQZl88PP10X4Tr8BxT02LNjULW55+2/sbZuyZge1nt2PegXlmyRucFx1Kd0CnMp3M2CkREZH0oMBJxB1CCwH3/wBsnwPMeQE4tx+Y2CU+E1/rd4Bsec3d5m4+huGztuLYhUgmdsaEXWtQMCwIQztUQpsqBbXvxKuw217PSj3NwsCJac3n7J2DU1dO4evNX5ulWt5q6FS6E9qUbIPQwFB3F1lERDIRddUTcacK7YCnVwH1+gLwAf79GfikDrDue8z99yj6TVx3LWj6z/ELkWY9gyoRb1UhVwUMrjsYC+5bgDFNx6Bpkabw8/HDplOb8MaKN9B8SnMM+msQlh1dhti4WHcXV0REMgG3B06ffvopSpQogaCgINSrVw+rVq1K9v7nz5/HU089hYIFCyJLliwoV64cfvtN8+NIBsaJce96F3hsAZC/KhB5Hpj5NPJNuxclfRKnLrdd+8+WKHbjE/FmAX4BuLP4nfj4zo/NeKgX6ryAMjnK4GrsVZOh74n5T6D11Nb4aN1HOHDxgLuLKyIiGZhbA6fJkydjwIABGDp0KNatW4fq1aujdevWOHnypMv7R0VFoWXLlti/fz9++eUX7NixA+PHj0fhwoVvedlF0hwz7j2+GGj5BmL9glHLtgW/B76E/n5TEQjHeWwYLrElatU+TRIqYskTnAe9K/c2GfkmtZuEbuW7mUl3T0ScwPh/x6P9r+3R+/femLZrGi5HX9aGExGRjDPGafTo0XjsscfQp08fc33cuHGYM2cOvv76a7z00kuJ7s/1Z8+exbJlyxAQEGDWsbVKJNPw8wcaPYsFqI+AuS+gmd9GPB8wFR38luOdmAewJa4ETiIn4q6d8zh5ybEbn4gAPj4+qJynsllevO1FLDq0yIyHYre9dSfXmeXNFW+ieGhxlAwriRKhJcx/63K2wGzajCIi4jmBE1uP1q5diyFDhiSs8/X1RYsWLbB8+XKXj5k5cyYaNGhguurNmDEDefPmRffu3TF48GD4+bmeSPTq1atmsVy8eNH8j46ONos7WK/rrtcXzxeStzh6Rg9C+9gVGBowAWV8j+KrwFHmtiibH47ZcuOwLS9Kb6qI2LOlYQsrBuQoGv8/ewHAV3lfRN815Atf3Fn4TrOcjDiJOfvmYNa+Wdh/cT92n99tFlctVwygSmQvYf6bACu0JApkLQBfH7f3cE9X+n0S1Rvxtu+a6FSUwcdms7llkMTRo0dNFzu2HjEYsgwaNAhLlizBypUrEz2mQoUKpptejx498OSTT2L37t3m/7PPPmu6+7kybNgwDB8+PNH6H3/8ESEhIWn8rkTSBocuDV/nh/NRQCguY6D/z2juuwEFfM4iwCf5ge5sjboSmAsRgXlxJTAPIgJzm8sRgXnM9SuBOWHzUWAl3os/e+fizuF03Gmcij3l8D/cFp7k4/zhjzy+eZDHL4/5n9cvL/L65kVuv9zI4pPllr4HERFJGxEREaYh5sKFCwgNDc08gRMTQURGRmLfvn0JLUzs7vf+++/j2LFjKW5xKlq0KE6fPn3djZOeke38+fPNeC2ry6GIsz+2nMAzkzaay9aH1BdxKIBzKOJzCkMaBKNa9ovwOX8QuHAIPhcOARcOwycu+TMnNp4xz14IthxFgTC2Ul1rqeJ/rgstDPgFaodkAvquSb1LUZdMEgm2SO27uA8HLsVfPnTpEKKT+WzlD8mP4tmLx7dUhf7XUsX1GamVSnVGVG/E275rLl68iDx58qQocHLbaWcWkMHPiRMnHNbzeoECBVw+hpn0uHHtu+VVrFgRx48fN13/AgMTH+wx8x4XZ3wed+8oTyiDeK72NYrA39/Pbh6n+NYkW1hhPNyhJWq6mscpLg4IPw4wmDp/CDh/4Nrl+OCK63xirwIXD8Pn4mEArrrF+gDZCwI5itktRa/9Lw6EFQH8dXY9I9F3TcrlCsiFXFlzoWbBmg7rY+JicDT8aHxAdWFfwsLrnJiXCSi4rDrhmBk22D84IZAyY6jC4v8zqOJtnkp1RlRvxFu+awJS8fpuC5wY5NSuXRsLFixA586dzbq4uDhz/emnn3b5mEaNGpkudrwfx0PRzp07TUDlKmgSyeg4yW3LSgWwfPdJzPt7JVrdXg8NyuSDn6+P6wfwc8HJdbkUq+86sLp88r9gyiGounY5JhK4dDR+ObTC9etkK+AiqLILrAI894BQ5Eb4+/qjWGgxs9xR5A6H2y5cvZAQRNkHVIcuHsKVmCvYdnabWZwVzFrQITmFCapCSyJfSD6T4EJERDyLWwc6MBV57969UadOHdStWxdjxozB5cuXE7Ls9erVy3Tne+edd8z1fv364ZNPPkH//v3xzDPPYNeuXXj77bfNGCeRzIpBUr2SuXBmm838TzJoSgkGVkwewaVo3cS3s+fu5VOOrVX2QRWX6Ij4Vi0uh5OYdy1rviSCKl4vCgRmvfH3IOJhwrKEoUa+Gmaxx659Ry4dSRRUsQsgg61jl4+Zhdn+7IX4hyS0TCUEVde6/gX5B93idyciIh4ROHXr1g2nTp3Ca6+9Zrrb1ahRA3PnzkX+/PnN7QcPHkxoWSKOTfrjjz/w/PPPo1q1aiaoYhDFrHoikgZ4ljtbvvilSG3XgVXE2aSDKi5R4fGtWlyOrHH9OiF5kgiqrgVbnBRYJIML8A0wARAXZ+cizyUEU/svXAuqLu7D4UuHERETga1ntprFng98UChboYSWKfsU6swEqFYqEZH05fbUWuyWl1TXvMWLFydax0QSK1Yk0X1IRNI/sMqaO34pXMt1YHXlnOsugFYr1tWLQMTp+OXoetevE5wziaDqWmAVFKY9LRlazqCcZqmZz3EsVXRstElEwSAqIai6dpmJK46EHzHL0iNLHR6XLSBbonFUDK7YtTBQyV5ERDJH4CQimSywCskVvxRy7LaU4Mr5pFuruESejw++uByLzyqYCAOnpIIq/g/KEV8WkQwmwC8ApXKUMos9JsBlEgr7bn/WfwZS4dHh2Hxms1nsMaNf4WyFE42j4uVcQbnUSiUikgoKnETk1grOEb8UrOb69siLSQdVXK6cBSIvAMf/jV9cyRIaH0C5CqoYbLFFS4GVZCDshpc7OLdZ6hSo43BbVGwUDl48aFqmrG5/VlDFgIotWFz+PvK3w+OyB2Z3GEfFgKpI1iKIscXc4ncnIpIxKHASEc8SFAoEVQbyV3Z9+9XwpAMrrmdyC3YHPLE5fnElMJtTUOUUWIXkVmAlGQa74pXJWcYszq1UZyLPOKRPt4IrplZn179NpzaZxZ4vfPH1rK9RKqxUoiQV7F4oIuKtFDiJSMaSJRuQr2L84krUZTMRcJKBVfiJ+AQWp7bFL64EhDgmq3Aeb8XkGWqxkgzQSsWkEVxuK3Cbw22RMZE4eOlgonFUvMzkFLyNCzjdm50cWXIk6vbH/0WyFzHJMEREMjMFTiKSuTDVed7y8Ysr0VdcB1ZWK9alY/Ep10/viF9cYUpoK7W6qyQW2fLHp34X8VBMa14uZzmz2ONk8pPnTEapOqVw6PIhhzTqTJ1+/up5bDi1wSz2/H38UTS0qEP6dCvrH9O1i4hkBgqcRMS7cHLePGXjF1diriYOrOy7Bl48Gj9J8Jld8YsrzGLmHFiF2XUL5Dxavn7p+jZFbrSVKtQ3FHUL1EWjgEYOt0VEx7dE2adP52UGV5zo1wqwFh1a5PA4JqGwD6Ssy0ytzomFRUQyCn1jiYg4fCtmAXKXjl9ciYkCLh5JIuU6A6sjQGwUcHZP/OIKuzSFFXYdVJnAqiDgp69n8SwhASGokKuCWezF2eJwMuIk9l7Ymyg5xYmIEyYbIJd1J9c5PI5BU/Hsxf9Ln34tqOL10MDQW/zuRESuT7/MIiKp4R8I5CoZv7gSGx3fKpVUynW2ZsVFA+f2xy+u+PhdC6yc061fG3MVWhjw03gS8QxMeV4gawGzNCzUMFErlXP6dKuV6mrsVey5sMcsznIH5U6UPp2XC2UtBD+11oqImyhwEhFJSwxochaPX1yJjYkfR+UQVB24NkGwXWBl3eaKj2988JRUZsDQIvEBnogHtFJVyl3JLM6tVBwz5dxCxesnr5w02QC5rDmxxuFxgb6BZlJf+y5/Vva/rAFZb/G7ExFvo8BJRORWYhc8E+AUBYo7np034mKBS8ftAiu7oMpqxWJXQP7ncnCZixfxMd39/MKKotZlP/gu3gDkKm6XxIKBVZZb8W5FXLIm5uXSqLDjWKrwqHAcuHjAdP2zD6o4V1VUXBR2n99tFmf5gvMlSp/OhS1hfD0RkZulwElExJP4Xuumx6VY/cS3x8UBl08mPUEwgykmr7h0FL6XjqIoH7PURXCVrYCLOays8VZF45NoiLhBtsBsqJynslnsxcbF4ujlo4mSU/AyW6fYUsVl1fFVDo8L8gtC8dD/xlLZZ/5ji5iISEopcBIRyUiY5pxZ+bgUrZv4dpstfhLg8wcRc2Yvdqz8ExUKhMDv4uH/WrGYbj38ePxy2PEgM0HWfEnPY8V1TPsucgtxbFPR7EXNcnuR2x1uuxh1MWHslJXdj9cPXDqAyNhI7Di3wyzO8ofkdwymwkqYrn9czwyDIiL2FDiJiGQmPNjjBL3Z8sGWvzp278+Ccne1hV9AwH+BVcQZ110ArcucIJitWlyOOI4xSRCSJ+l5rLg+S/Zb+rbFuzELX7W81cxiLyYuBkfDjzp0+bMuM9Mfs/5xWXFshcPjgv2DEzL82SenYMsVbxMR76TASUTE2wKrrHnil8K1E9/OwOrKuaTnseJy9SIQcTp+Obre9esE50wiqLoWWAVpUlRJf0x5zmQSXJqgicNtF65ecAikrMuHLx0281JtO7vNLM4KZi2YaE4q/s8Xkk+tVCKZnAInERFxDKxCcsUvhWq43jJXzicRWF1rxYo8Hx98cTm20fVzMHBKKqji/6Ac8WURSSdhWcJQI18Ns9iLjos2wZMZP2U3joqXGWwxGyCXZUcdxw6G+If8NydV6LVU6mElTStVFj8lYxHJDBQ4iYhI6gTniF8KOnaLShB5wS69uovsgFfOxt/n+L/xiytZQq8lq3ARVDHYYovWDQRWsXE2rNp3FicvRSJf9iDULZkLfr4K0OQ/Ab4BCS1KzdDMYdOcizyXKH06AyoGWhExEdh6ZqtZ7PnAB4WyFXLo9mctnK9KY6lEMg4FTiIikrbYmlSASxXXt1+9FB9EOQRV/H/tOrsAsjvgic3xiyuB2ZIIqq4FViG5EwVWczcfw/BZW3HsQmTCuoJhQRjaoRLaVCmYpptAMqecQTnNUit/LYf10bHROHTpUELLlH1wdSnqEo6EHzHL0iNLHR6XLSBbovTpvMyuhYF+motNxNMocBIRkVuLiSPyV4pfXIm6HD8RsKugiguTVjCBxalt8YsrHMBvF1TtiMyJ2euikN+WFzHIg1PgGCsfHL8QiX4T12Fsz1oKnuSGBfgFoFSOUmaxZ7PZTBIKV8kpGEiFR4fj39P/msXVPFfO46j4P1dQLrVSibiJAicREfEsTHWet3z84kr0lWuBlYugiq1Yl44BMVeA0zviFwB8pk/sTuBH2fxwBVniF1sgYqYGwbY8H3wCgwHO7cN5rAKyXvtvvy4kmXVOt3GSYY3T8mrshpc7OLdZ6hSo43BbVGyUmdTXYRzVtaCKARVbsLj8hb8SZRC0uv3Zj6limnYGcCKSfhQ4iYhIxsLAJE/Z+MWV6Ejg4pGEcVWH9+3A6o0bUMTnFAr7nEYBnEOgTywCEYEwRLDhCbABOHIgbcvp43udQCsl60Kuf39OmiwZDrvilclZxizOrVSnr5x2aKGygiumVuecVZtObTKLPT+f+Hmu7Oekslqq2L1QRG6eAicREclcAoKA3KXjFwBr/Y7g+TUb/rsZMciL8wj2uYogRCEYVxHsE4WnGxdCvSIh8RMEs1Ur0X8ul+0uu7id3QxtsfEvZIuL71LIJT0xY1tyQVhgUi1jiQMyH59AhEYcBM7uAYJD/7uN423UenbLWqnyhuQ1y20FbnO4LTImEgcuHvgvOYVdcMUU6rzOZfHhxQ6Py5Elh8tuf4WzFzbJMEQkZRQ4iYhIpsbsefai4Y+jyBPfymSxAU+Wrw+Uzn3zLxgbnUTQZbcuKiLp21Kyjl0RE17vavzCNPBpcFBg8sjteMXxBh+/m289u14Ax3Fpvr43/R4ysyD/IJTPVd4szq1UJyNOOnT7s4Iqpk4/f/U81p9cbxZ7/j7+KBr6XyuVfVDFdO0i4kiBk4iIZGpMOc7seUwEYR8rWdhTr0BYfGryNMFxJn5h6TvJb1wcEBN5LZBKroUsFeuiImCLjsDV8PPI4hsLH65jqxmxFS3qUvySnvyDkgm+bnLMGf9z/FwmHAfEVqr8WfObpX7B+g63RURH4OClg/+lT78WVHFhK5XVYrXo0CKHxzEJhX1AZQVVTK3OiYVFvJFqvoiIZGqcp4kpx5k9zxrOZLESlvP2DDWfE1tm2ILDJQ3FREfjj99+Q9u2bRHg75+y1rMkuzOmtPXsv/Tw5jIXTp6cXnjQf1OtZ8kFcFbrWZDHtJ6FBISgQq4KZrEXZ4szrVR7L+x1mJOK/09EnDDZALmsO7nO4XHs2lcsezGHcVRFQ4oi0ma3H0UyKQVOIiKS6XGeJqYcd57HiS1NmscpCRzT5B8Yv3DC43RtPUsiwIpKqxa1y/+1nsXFxM8TxiU9sethci1fN9V6dm3xu/HDOKY8L5C1gFkaFmqYqJXKOX06/3N81dXYq9hzYY9ZnI2bNs4hKYXVUlUwa0H4KYmJZAIKnERExGuCp5aVCmDVvrM4eSnSjH1i97wM1dKUGZnWs6zxS3qx2YDYqFS0mkUknwAkqftxrJmFwSCXK2fT730xsYPLgCulY85c3xYSEIxKwQVQKbQk8P/27gQ+iuoO4Pg/x+YiFzcEAkRBUJCCyilKWyiHVPEoUosUlYIHfkRLkVo82lKPotVqq6g9wCrVYiuoiFIqh2AVpNxIEWo4BEIEcpKDHK+f/4NZdzYJu2BIdsnv+/k8dndmdnZ25jE7/7z3/pMR500Moq1UOmbKP326tlppJsBDJcfL2oNrXZsZExkj7VPau5JTnJNyjg2yGmkLHhAmCJwAAA2GBkn9aiMBBMKw9Sz2eIk/g6m5KytOoTXsNBOEaODmdDitLBMpzTtezqQTQVWkJ0HaeOJtudQnVX5l9Dny3/xEKUtvLXsiyyWzskR2VRyVzLI82X0sV45VHpMdOTts8dcivplk2CDKnZxCW8K0VQwIJQROAAAAtUG7o8UmHi911XpWa90Z/YI5/QyHM10OV/+1ReQCfXJY5Bt+8zQ5//7oKMn0eGSXxyOZnugTjx45HB0l2cWHbFmdtcb1vjgj0l6iJSMiRjpEJkhGdJJ0iEmVDrGNJSEmqZr7nXFTapx5BE4AAADhoq5azyrKax575jetoqRQPtu6Uc7LSJeoylKf7oxFElVWLOm2FMnlznuL9b25kl9RLLs80SeCqq8ed3s8UhIRIdul3Bap1KyPh0Q0lisUaVleLhllZZJxrFw66KMt5dKyosKb8KWGnVd9F8avPebML2kI47nOWgROAAAAcNPEE1FJIrFJAfdMZVmZfHZ4kXT81hUS5Qk+3XuyMdK9vFS6+wVk5aWFsq9wr+wq+EIyj+6TXUUHJbPkS9lVmiNHKkvkYHS0LR/Hu9cXbyKkg5ZyYwOpjGOlklFSJO1KiyVeW+q0i6PN+qjdHevxptSnlTTEb3luSl0vCJwAAABQP61nHr13V1yVi9P2J8pAv7fkluR+lfHP54a/XxR8IcVSLtsijGyL0YwUUSKNNF3/8ZT9aQmtpENiG8lolCYZ8S2kQ2xTyYhJleYRMRJRpWUt2JT7fvPPwE2pa953kWfmfme+SUO4KXUVBE4AAAAIC6lxqdIjrof0aNHDNb2ssswGT77p0517U+WV5sn+oixb/i3/cb1Ps/ppQgqbRj05QzKan2eft09uL7HachQsbdGyN6U+hYyNrvFpgZY/8VwTgtjPqxQ5Vni81NtNqRNqmHbyMWcV0fGy/oujsjW7SJp+fkj6dWoVNtlNCZwAAAAQ1vTGvM59o/zllOS4AirnuQZaR8uOytbDW23xFSERkpaYVuWeVFqaxjWViBMp2t2tZyeCCGly5r6ovSl1TVkaayFBiAZz2gJ3Bm9KHSUivU+UvrN/JxEpbcLmfnoETgAAADhrNY5rbMtFLS9yTS+rKJO9BXu93f68rVR5mVJQViD7CvfZsmrfKtf7kjxJVW70q4/tkttJjI49OpOiPMdLXPIZvin1KbaelQXuzlh0tEAKCwskTkolQUolOqJSiiVW8vNK5PZX1tmblId68ETgBAAAgAbHE+WRc1LPscWXMUYOlxz2dvVzgiltpdJASoOqzYc22+JL7zvVNrGtt9ufb3DVJK5J1VaqkL4p9YnxTlI7972rqDQy6NdL5UBpiXdatJRLuW1/svkO5Rdvf2pvUh7K3fYInAAAAIATNMBpFt/MlktaXeLaL6UVpbInf497HNWJoKqwrFD2FOyx5QP5wPW+5JhkdwvViaAqPSnddjM8263JPCIH8r4KmlS5TxiiOQ91vi4XyjcpJ3ACAAAAgqAJIzo17mSLfyvVoeJD7rFUJ1qr9hful/xj+bLxy422+IqKiLLBkzc5hRNUJWfYRBhni+yCklpdrr4QOAEAAABfs5WqeUJzW3q31rQHXykpL5Hd+burdPvTx+LyYvtcy3JZ7npfamxqleQU+rxtUluJjgyvS/gWSXG1ulx9Ca+9DgAAAISRuOg46dyksy3+rVTZRdlVElNoEHXg6AHJLc2V9dnrbfGlQZO2UvmOo3KCqpTYFAlFvTOaSOuUOMnKK7Hd8vzpqKZWKXF2uVBG4AQAAADUQytVy0Ytbenbuq9rXlFZkR0r5RtQaYClLVfaSuWkVZe97nVqEgr/9OkaYGlq9ajI44kY6kNUZIRNOa7Z8zRI8g2enFQQOj+UE0MoAicAAAAghCR4EqRLky62+Ko0lXLw6EF3K9WJ59p6daTkiC3rste53qcJKPSmvq7kFCdarJJikurkOw3r1tqmHNfseb6JIrSlifs4AQAAAKg1mvK8dWJrW/qn9XfN05v52vFSfuOotJVKswHuzN1piz/NHuh/Typ9bN2oda23Ug3r1tqmHP9oZ7b8c+VqGXJZH+nXsUXItzQ5aHECAAAAwlwjTyPp2rSrLf6tVDpmyn8clT5+WfylzQao5ZOsT6pkENSb+rrGUiUff66fdbo0SOqT0UQObzP2MVyCJkXgBAAAAJzFrVRtEtvYMqDNANe8wmOFX6VPr6aVakfODlv8tUhoUW1A1apRK/t5ZysCJwAAAKABSoxJlG7Nutniq6KyQvYf3V8loNqVt0sOlxy246m0rM5a7XpfXFScHUvlm+lPH3WajtsKdwROAAAAALx0bJOmPNdyedvLXXsm/1j+V5n+fIIqzQJYUlEi23O22+JPW6OcQKpdYjs5WHbQZgj0eDxhs+cJnAAAAAAEJTkmWbo3726Lr/LKctlXuM+VPt15nlOaI1lHs2z5+MDH3vdcU3yNJMcnh82eJ3ACAAAA8PWCisho2yVPy8D0ga55uSW5X42l0vTpuZmybf82SWuUFlZ7ncAJAAAAwBmTGpcqPeJ6SI8WPezrsrIyWbRoUb3elPd0nL1pLwAAAACglhA4AQAAAEAABE4AAAAAEACBEwAAAAAEQOAEAAAAAAEQOAEAAABAAAROAAAAABAAgRMAAAAABEDgBAAAAAAETgAAAADw9dDiBAAAAAABEDgBAAAAQAAETgAAAAAQAIETAAAAAARA4AQAAAAAARA4AQAAAEAABE4AAAAAEACBEwAAAAAEEC0NjDHGPubn59fbNpSVlUlRUZHdBo/HU2/bgfBBnQH1BpxrEKr4jUI41xknJnBihJNpcIFTQUGBfUxPT6/vTQEAAAAQIjFCSkrKSZeJMMGEV2eRyspK2b9/vyQlJUlERES9RbYauO3du1eSk5PrZRsQXqgzoN6Acw1CFb9RCOc6o6GQBk1paWkSGXnyUUwNrsVJd0jbtm0lFGhFqe/KgvBCnQH1BpxrEKr4jUK41plALU0OkkMAAAAAQAAETgAAAAAQAIFTPYiNjZWHHnrIPgLUGXCuQajg9wnUG3CuqVmDSw4BAAAAAKeKFicAAAAACIDACQAAAAACIHACAAAAgAAInAAAAAAgAAKnOvbss89Khw4dJC4uTvr06SNr1qyp601AHXj00UelV69ekpSUJC1atJCrr75atm/f7lqmpKREJk2aJE2bNpXExES57rrr5ODBg65l9uzZIyNGjJCEhAS7nqlTp0p5eblrmeXLl8tFF11ks2F17NhR5syZU2V7qHfh57HHHpOIiAi5++67vdOoM6jOvn375MYbb7Tnkvj4eLnwwgtl7dq13vmaA+rBBx+U1q1b2/mDBw+WHTt2uNZx5MgRGTNmjL0RZWpqqowfP14KCwtdy2zatEkuu+wy+/uVnp4uM2fOrLItr7/+unTp0sUuo9uxaNEiDloIqqiokAceeEAyMjJsnTj33HNlxowZtq44qDcN2wcffCBXXnmlpKWl2d+iBQsWuOaHUv0IZltqjWbVQ9147bXXTExMjPnzn/9stm7daiZMmGBSU1PNwYMHOQRnmaFDh5rZs2ebLVu2mA0bNpgrrrjCtGvXzhQWFnqXue2220x6erp5//33zdq1a03fvn1N//79vfPLy8tNt27dzODBg8369evNokWLTLNmzcx9993nXebzzz83CQkJ5sc//rH59NNPze9+9zsTFRVl3nvvPe8y1Lvws2bNGtOhQwfTvXt3M3nyZO906gz8HTlyxLRv397cdNNNZvXq1facsHjxYrNz507vMo899phJSUkxCxYsMBs3bjRXXXWVycjIMMXFxd5lhg0bZr7xjW+Yjz/+2KxcudJ07NjR3HDDDd75eXl5pmXLlmbMmDH2vPbqq6+a+Ph488ILL3iX+fDDD+35Z+bMmfZ8dP/99xuPx2M2b97MgQsxDz/8sGnatKlZuHChyczMNK+//rpJTEw0Tz/9tHcZ6k3Dptcc06dPN2+88YZG02b+/Pmu+aFUP4LZltpC4FSHevfubSZNmuR9XVFRYdLS0syjjz5al5uBepCdnW1PPCtWrLCvc3Nz7X98/bFybNu2zS7z0UcfeU9akZGRJisry7vMrFmzTHJysiktLbWv7733XtO1a1fXZ40ePdoGbg7qXXgpKCgwnTp1MkuWLDEDBw70Bk7UGVRn2rRpZsCAATXunMrKStOqVSvz+OOPe6dpXYqNjbUXKUovRvTc88knn3iXeffdd01ERITZt2+fff3cc8+Zxo0be889zmd37tzZ+/r66683I0aMcH1+nz59zK233srBCzF6nG655RbXtGuvvdZewCrqDXz5B06VIXReCWZbahNd9erIsWPH5D//+Y9tPnRERkba1x999FFdbQbqSV5enn1s0qSJfdS6UFZW5qoP2gzdrl07b33QR22SbtmypXeZoUOHSn5+vmzdutW7jO86nGWcdVDvwo9239Tumf7HlTqD6rz11ltyySWXyKhRo2x33p49e8of/vAH7/zMzEzJyspy1aeUlBTbVdz3XKPdaHQ9Dl1ef6NWr17tXebyyy+XmJgY17lGuyDn5OQEdT5C6Ojfv7+8//778tlnn9nXGzdulFWrVsnw4cPta+oNTiYzhM4rwWxLbSJwqiOHDh2yfYp9L4KVvtYDjrNXZWWlHady6aWXSrdu3ew0PeZ6otCTSk31QR+rqy/OvJMto8FVcXEx9S7MvPbaa7Ju3To7Rs4fdQbV+fzzz2XWrFnSqVMnWbx4sdx+++1y1113yUsvveStN+pkvz36qEGXr+joaPuHnto4H/EbF3p++tOfyve//337BzuPx2MDbv2d0vEoinqDk8kKofNKMNtSm6JrfY0AqrQgbNmyxf41D6jJ3r17ZfLkybJkyRI7ABYI9g8z+hfdRx55xL7WC2A93zz//PMybtw4diKqNW/ePJk7d6789a9/la5du8qGDRts4KSJAKg3QM1ocaojzZo1k6ioqCpZ0/R1q1at6mozUMfuvPNOWbhwoSxbtkzatm3rna7HXLvR5ebm1lgf9LG6+uLMO9kymsFGM8tQ78KHdsXLzs62GRL1r3JaVqxYIc8884x9rn89o87An2aRuuCCC1zTzj//fJuR0/dccbLfHn3UuudLs3dqRqzaOB/xGxd6NEOr0+qkXcLHjh0r99xzj7e1m3qDk2kVQueVYLalNhE41RHtlnXxxRfbPsW+fynU1/369aurzUAd0bGUGjTNnz9fli5dalO++tK6oN0jfOuD9unVix2nPujj5s2bXScebY3QoMi5UNJlfNfhLOOsg3oXPgYNGmSPt/7l1ynakqBdZ5zn1Bn40y7A/rc60HEr7du3t8/13KMXD77nCe3Kq2MMfM81+kccDd4det7S3ygdJ+Aso+mJdWym77mmc+fO0rhx46DORwgdRUVFdqyJL/3jrh5zRb3ByWSE0HklmG2pVbWebgI10rTQmuVjzpw5NtvIxIkTbTpy36xpODvcfvvtNjXm8uXLzYEDB7ylqKjIlVpaU5QvXbrUpiPv16+fLf7pyIcMGWJTmmuK8ebNm1ebjnzq1Kk2K9+zzz5bbTpy6l148s2qp6gzqC51fXR0tE0vvWPHDjN37lx7TnjllVdcqXr1t+bNN980mzZtMiNHjqw2bXDPnj1tSvNVq1bZzI6+aYM1S5WmDR47dqxNG6znFf0c/7TBui1PPPGEPR899NBDpCMPUePGjTNt2rTxpiPXlNN6uwvN1Oqg3jRsmuFVb4WiRcOFJ5980j7fvXt3yNWPYLalthA41TG9z45eLOv9nDRNtOa2x9lHTzLVFb23k0P/Q99xxx02FaeeKK655hobXPnatWuXGT58uL2vgf6oTZkyxZSVlbmWWbZsmenRo4etU+ecc47rMxzUu7MjcKLOoDpvv/22/SOL/oGkS5cu5sUXX3TN13S9DzzwgL1A0WUGDRpktm/f7lrm8OHD9oJG7+Wjtzy4+eab7YWTL70/iqY+13XoRbderPibN2+eOe+88+z5SG+V8M4773DQQlB+fr49t+j1SFxcnP3t0Hv2+KaFpt40bHptUd11zLhx40KufgSzLbUlQv+p/XYsAAAAADh7MMYJAAAAAAIgcAIAAACAAAicAAAAACAAAicAAAAACIDACQAAAAACIHACAAAAgAAInAAAAAAgAAInAAAAAAiAwAkAEJSbbrpJrr766lrfW1lZWfKd73xHGjVqJKmpqRwNAEBIInACgAYQnJyKXbt2SUREhGzYsKFOPu+pp56SAwcO2M/77LPPqszv0KGD3Z6aiu6z+qLb9tvf/jbgchs3bpSrrrpKWrRoIXFxcfZ9o0ePluzs7DrZTgDA1xddC+sAAOC0/e9//5OLL75YOnXqVO38Tz75RCoqKuzzf//733LdddfJ9u3bJTk52U6Lj48/pc87duyYxMTE1NkR+/LLL2XQoEHy3e9+VxYvXmxb1TQ4feutt+To0aN1th0AgK+HFicACCNbtmyR4cOHS2JiorRs2VLGjh0rhw4d8s7/5je/KXfddZfce++90qRJE2nVqpX8/Oc/d63jv//9rwwYMMC2fFxwwQXyr3/9y7bcLFiwwM7PyMiwjz179rTTdZ2+nnjiCWndurU0bdpUJk2aJGVlZSfd5lmzZsm5555rg5XOnTvLyy+/7J2nLS//+Mc/5C9/+UuNrUfNmze330OLfielLTf62uPxyG233SZt2rSRhIQEufDCC+XVV191vV+3/84775S7775bmjVrJkOHDrXTNXDRYE33w7e+9S156aWX7Dbk5uZ637tq1Sq57LLLbHCWnp5u960T7Oh6d+/eLffcc4+39as6H374oeTl5ckf//hHu091/+rnaUubs6+DObb6uT/84Q/tfN3/v/nNb+w26Pdy+B5HhwZqc+bM8b7eu3evXH/99Xa67s+RI0faQM6/1fNkx7m0tFSmTZtm90lsbKx07NhR/vSnPwX9XQAgHBE4AUCY0Av6b3/72/bie+3atfLee+/JwYMH7UWwLw0AdLzQ6tWrZebMmfLLX/5SlixZYudpy41eFGuQofNffPFFmT59uuv9a9assY8aUGkXujfeeMM7b9myZbaFSB/1c/SC3Pei3N/8+fNl8uTJMmXKFHsxfeutt8rNN99s3++0Jg0bNsx+B/2sp59++pT2SUlJiW2teuedd+z6J06caC/Sne/gu080cNMg5vnnn5fMzEz53ve+Z/eFdqPT7fLfD/o9ddu0hWvTpk3yt7/9zQZSGoQp3S9t27a1+1e3XUt1NMArLy+3+8IYc9rHdurUqbJixQp588035Z///KcsX75c1q1bd0r7S4MfDRyTkpJk5cqVdn9ocKPfU1vigj3OGsBpgPrMM8/Itm3b5IUXXrDrCfa7AEBYMgCAkDFu3DgzcuTIaufNmDHDDBkyxDVt7969eiVutm/fbl8PHDjQDBgwwLVMr169zLRp0+zzd99910RHR5sDBw545y9ZssSuY/78+fZ1Zmamfb1+/foq29a+fXtTXl7unTZq1CgzevToGr9P//79zYQJE1zT9D1XXHGF97V+X113MJYtW2a3LScnp8ZlRowYYaZMmeJ9rfukZ8+ermV0f3Tr1s01bfr06a51jx8/3kycONG1zMqVK01kZKQpLi62r3V/PPXUUwG3+2c/+5nd702aNDHDhg0zM2fONFlZWUEf24KCAhMTE2PmzZvnnX/48GETHx9vJk+e7J3mexwdKSkpZvbs2fb5yy+/bDp37mwqKyu980tLS+16Fi9eHNRx1u3Rz9F6c7r1FADCES1OABAmtGVEWwD0L/tO6dKli52nrQOO7t27u96n3a2cJAQ6Nki7V2kriKN3795Bb0PXrl0lKiqq2nVXR1sjLr30Utc0fa3Ta4O2oM2YMcN20dNuZ7pPdBzRnj17XMtpq5Qv3Q+9evVyTfPfD7q/tZXFd39ra01lZaVtsToVDz/8sM0eqK1dug/1UY/d5s2bgzq2WrRFqE+fPt516vfVro+nQj9n586dtsXJ+Rxdj7bc+dahkx1nTeKh8wYOHPi16ikAhBuSQwBAmCgsLJQrr7xSfv3rX1eZpxe2Dh3340vHvejFfm04k+s+HY8//rjt3qeZ7TR40i6KOubHt9uZ0umns7+1C5+Oa/LXrl27U16fjhUaNWqULY888ojtyqbjiLQrXKBjq8FOMPR4+HcH9B2bpJ+jQeTcuXOrHUsWzHEOlIwj2HoKAOGGwAkAwsRFF11kEyloQoXo6NM7fWsLhSYH0DEnOmjfGWfky8k452Sy+zrOP/98O45m3Lhx3mn6WpNS1AZdlyY3uPHGG+1rvbjXlOaB1q/7YdGiRa5p/vtB9/enn35qEx/URPfV6ewnfZ8mzHASTQQ6trqsBjM6Ls0J2nJycux39W350eDHd6zVjh07pKioyPWddKyWJtdwshKeKg1QdT/reKvBgwefkXoKAKGIrnoAEGI0A5t2h/ItGuxoZrMjR47IDTfcYC/ytduTdkvTZAvBXrzrjWb1IlwDGU14oIHH/fffb+c5WeH0olpbFZxB/bo9p0sTGmh3N82spxfxTz75pE2q8JOf/ERqg2bF08QXmqZcu/9pC5FucyC6nGYX1MxwGnzMmzfPm/zA2Q86T9erySD0GOj2a2IGJzmE0uDggw8+kH379tWYNW7hwoU2sNNH/SztJqgtTRq4adCnAh1b7e42fvx4uz+XLl1qE2Fo9rvISPfPuCZl+P3vfy/r16+3iRk046Bv69GYMWNsZkH9XE0OoV0ONcmEtqp98cUXQe1z/c5af2655Rabwc9Zh+7DYL4LAIQrAicACDF6EarduHzLL37xC0lLS7OBjl58DhkyxP7lX7ulaVpp/wvomujYFL3Y1e5UOsbnRz/6kTebnKblVtpKoNnSNFOafqZzcX86NGuddqXTQEHHzeg6Z8+eXSXF+enSoE9bOHTska5Tx24FcwNhTQP+97//3QZxOiZMAztnP2h6baXTtVVFgx1NSa7H4cEHH7T7xKEZ9TSVtwajvl3dfGnrl2Yx1MyCPXr0kL59+9ogQ9OTawZAFcyx1W6Juh3aDU5bejSlvP/YLU1RrmPYdLkf/OAHNkDVz3bocw30tNXq2muvtS2CGpDpGKdTaYHS/aVZCe+44w47fmnChAne1rPaqKcAEIoiNENEfW8EAKD+6EWuXoTrOBoNABoqTeCgSRu0dS9caLCowZiO8QIAnFl0PgaABkbvJ6Rdv7SbmwZLep8lzXTX0IKm5557zra6adIGDR61Rce3Gx4AAL4InACggSkoKLDjdzRlt4530W5f2sWrodExS7/61a/seBztuqZd6e6777763iwAQIiiqx4AAAAABMAoTQAAAAAIgMAJAAAAAAIgcAIAAACAAAicAAAAACAAAicAAAAACIDACQAAAAACIHACAAAAgAAInAAAAABATu7/IPjdzrAuXeAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 0:11:15.436336\n"
     ]
    }
   ],
   "source": [
    "# Program comparingPlateaus.py\n",
    "# la Evolución battles against different degrees of complexity,\n",
    "# which are given by the lengths of target sequences.\n",
    "# The purpose is to compare the heights of the arisen plateaus.  \n",
    "\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt # Import matplotlib for plotting\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "\n",
    "start_time = time.time() # Record the start time\n",
    "\n",
    "#lengths_of_target_sequence = [10, 100, 200, 300, 400, 500, 1000, 2000,   5000,  20000, 100000]\n",
    "#numbersOfGenerations =      [100, 200, 200, 300, 400, 700,  700,  700,    900,   1200,  1300 ]\n",
    "lengths_of_target_sequence = [400, 500, 1000, 2000,   5000,  20000, 50000, 100000]\n",
    "numbersOfGenerations =      [ 400, 700,  700,  700,    900,   1200,  1300,   1300 ]\n",
    "\n",
    "population_size = 30\n",
    "alphabet = '01' # Binary alphabet, binary strings\n",
    "include_clone_populationk2 = True\n",
    "include_mutate_allk2 = True\n",
    "include_crossover_allk2 = True\n",
    "mutation_rate = 0.01\n",
    "recombination_rate = 0.01\n",
    "print_best_fitness = False\n",
    "show_graph = True\n",
    "\n",
    "\n",
    "def print_parametersk2():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: compare diverse plateaus for different degrees of complexity\")\n",
    "    print(f\"Date of Experiment  = {datetime.datetime.now()}\")\n",
    "    print(f\"alphabet =  {alphabet}\")\n",
    "    print(f\"population_size = {population_size}\")\n",
    "    print(f\"include_clone_populationk2 = {include_clone_populationk2}\")\n",
    "    print(f\"include_mutate_allk2 = {include_mutate_allk2}\")\n",
    "    print(f\"include_crossover_allk2 = {include_crossover_allk2}\")\n",
    "    if include_mutate_allk2:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk2:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    if include_clone_populationk2:\n",
    "        print(f\"print_best_fitness = {print_best_fitness}\")\n",
    "\n",
    "def maxk2(data):\n",
    "    max_value = data[0]\n",
    "    for x in data:\n",
    "        if x > max_value:\n",
    "            max_value = x\n",
    "    return max_value\n",
    "\n",
    "\n",
    "\n",
    "def mean_stddev_and_maxk2(data):\n",
    "    mean = sum(data) / len(data)\n",
    "    variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
    "    stddev = variance ** 0.5\n",
    "    max_value = maxk2(data)\n",
    "    return mean, stddev, max_value\n",
    "\n",
    "def create_populationk2(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with random chromosomes.\"\"\"\n",
    "    # print(\"create_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        random_string = ''.join(random.choice(alphabet) for _ in range(len(target_sequence)))\n",
    "        population.append(random_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def create_perfect_populationk2(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with perfect chromosomes.\"\"\"\n",
    "    # print(\"create_perfect_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        perfect_string = '1'* len(target_sequence)\n",
    "        population.append(perfect_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "\n",
    "def calculate_fitnessk2(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome based on its similarity to the target sequence.\"\"\"\n",
    "    #  print(\"\\n *calculate_fitness8(chromosome, target_sequence)\")\n",
    "    fitness = 0\n",
    "    for i in range(len(chromosome)):\n",
    "        if chromosome[i] == target_sequence[i]:\n",
    "            fitness += 1\n",
    "    # print(chromosome, \"  fitness: \", fitness)\n",
    "    return fitness\n",
    "\n",
    "def calculate_all_fitnessk2(population, target_sequence):\n",
    "    # print(\"\\n*calculate_all_fitness8(population, target_sequence)\")\n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = [calculate_fitnessk2(chromosome, target_sequence) for chromosome in population]\n",
    "    # print(\"fitness_scores: \", fitness_scores)\n",
    "    return fitness_scores\n",
    "\n",
    "def select_the_bestk2(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome for reproduction.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk2(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome_index = fitness_scores.index(best_fitness)\n",
    "    best_chromosome = population[best_chromosome_index]\n",
    "    # print(\"\\n*select_the_bestk2(population)\")\n",
    "    # print(\"best_fitness = \", best_fitness)\n",
    "    # print(\"The chro = \", best_chromosome )\n",
    "    return best_fitness, best_chromosome\n",
    "\n",
    "def clone_populationk2(best_chromosome, population_size):\n",
    "    \"\"\"Creates a new population by cloning the best chromosome.\"\"\"\n",
    "    return [best_chromosome] * population_size\n",
    "\n",
    "\n",
    "def mutatek2(chromosome, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "                mutated_chromosome_list[i] = random.choice(alphabet)\n",
    "                counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "def mutate_allk2(population, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    touched_numbers = [] # List to store the number of touched sites for each chromosome\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek2(chromosome, mutation_rate, alphabet)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "        touched_numbers.append(counter)\n",
    "    return mutated_population\n",
    "\n",
    "def crossoverk2(parent1, parent2, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, len(target_sequence))\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "def crossover_allk2(population, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 =  population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk2(parent1, parent2, target_sequence, recombination_rate)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "\n",
    "def run_one_generationk2(population, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    # Calculate fitness scores for the current population\n",
    "    best_fitness, best_chromosome  = select_the_bestk2(population, target_sequence)\n",
    "    # print(f\" best_chromosome {best_chromosome}, best_fitness {best_fitness}\")\n",
    "    # Clone the best to build the new population\n",
    "    new_population = []\n",
    "    if include_clone_populationk2:\n",
    "        new_population = clone_populationk2(best_chromosome, population_size)\n",
    "    if include_mutate_allk2:\n",
    "        new_population = mutate_allk2(new_population, mutation_rate, alphabet)\n",
    "    if include_crossover_allk2:\n",
    "        new_population = crossover_allk2(new_population, target_sequence, recombination_rate)\n",
    "    return best_fitness, best_chromosome, new_population\n",
    "\n",
    "\n",
    "\n",
    "def plot_Best_Fitnessk2(best_fitness_history, length_of_target_sequence, population_size):\n",
    "    # Plot the fitness history\n",
    "    plt.plot(best_fitness_history, label='Best Fitness', color='blue')\n",
    "    plt.axhline(y=length_of_target_sequence, color='red', linestyle='--', linewidth=2)\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Genetic Algorithm - Fitness over Generations, length_of_target_sequence = {length_of_target_sequence}, popSize {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def onela EvoluciónaryProcessk2( length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    all_touched_numbers = [] # List to store the arrays of number of touched sites for each generation\n",
    "    population = create_populationk2(population_size, target_sequence, alphabet)\n",
    "    #population = create_perfect_populationk2(population_size, target_sequence, alphabet)\n",
    "    # print(population)\n",
    "    best_fitness, best_chromosome = select_the_bestk2(population, target_sequence)\n",
    "    generation = 0\n",
    "    best_fitness_history = [] # List to store best fitness of each generation\n",
    "    best_fitness_history.append(best_fitness)\n",
    "    if print_best_fitness:\n",
    "        print(\"Generation  and best_fitness\")\n",
    "    #while best_fitness < len(target_sequence):\n",
    "    while generation < numberOfGenerations :\n",
    "        generation += 1\n",
    "        best_fitness, best_chromosome, population = run_one_generationk2(population, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        if print_best_fitness:\n",
    "            #print(f\" {generation}  {best_chromosome} {best_fitness}\")\n",
    "            print(f\" {generation} {best_fitness}\")\n",
    "        \n",
    "        best_fitness_history.append(best_fitness)\n",
    "    print(f\"\\nTarget sequence matched in generation {generation}\")\n",
    "    print(f\"Final chromosome: {best_chromosome}\")\n",
    "    totalCost = population_size * generation\n",
    "    print(f\"Total cost = population size * number of generations: {totalCost}\")\n",
    "    if show_graph:\n",
    "        plot_Best_Fitnessk2(best_fitness_history, length_of_target_sequence,  population_size)\n",
    "    # best_fitness_history is normalized to the length of the target sequence,\n",
    "    # so it is between 0 and 1. Thus, it can be comapred with other normalizations. \n",
    "    normalized_best_fitness_history = [fitness / length_of_target_sequence for fitness in best_fitness_history]\n",
    "    return normalized_best_fitness_history\n",
    "\n",
    "def draw_all_historiesk2(normalized_best_fitness_histories, lengths_of_target_sequence, population_size):\n",
    "    # Plot all the normalized best fitness histories together\n",
    "    # By the AI of VSCode. \n",
    "    for i in range(len(normalized_best_fitness_histories)):\n",
    "        # Range of x-axis is the number of generations for the current length of target sequence\n",
    "        x_range = range(len(normalized_best_fitness_histories[i]))\n",
    "        plt.plot(x_range, normalized_best_fitness_histories[i], label=f'Length {lengths_of_target_sequence[i]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Normalized Best Fitness')\n",
    "    plt.title(f'Genetic Algorithm - Normalized Fitness over Generations for Different Lengths of Target Sequence, popSize {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def maink2(lengths_of_target_sequence, population_size, numbersOfGenerations,    mutation_rate, recombination_rate, alphabet):\n",
    "    ''' An la Evoluciónary process is triggered for each length of the target string.\n",
    "    The normalized best fitness history is stored in a list, \n",
    "    and then all the histories are plotted together.\n",
    "    A graphic is also plotted to show the tendency of the heights of the plateaus,\n",
    "    by plotting the last entry of the normalized best fitness history \n",
    "    for each length of target sequence.\n",
    "    Two fitting models are tested: a logarithmic model and a power model.\n",
    "    With the assitance of the AI of Visual Studio Code.\n",
    "    '''\n",
    "    print_parametersk2()\n",
    "    # List to store the normalized best fitness history for each length of target sequence   \n",
    "    normalized_best_fitness_histories = []\n",
    "    for i in range(len(lengths_of_target_sequence)):\n",
    "        length_of_target_sequence = lengths_of_target_sequence[i]\n",
    "        target_sequence = '1'* length_of_target_sequence  \n",
    "        numberOfGenerations = numbersOfGenerations[i]\n",
    "        normalized_best_fitness_history = onela EvoluciónaryProcessk2( length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        normalized_best_fitness_histories.append(normalized_best_fitness_history)\n",
    "    draw_all_historiesk2(normalized_best_fitness_histories, lengths_of_target_sequence, population_size)\n",
    "\n",
    "    # To see the tendency of the heights of the plateaus,   \n",
    "    # we can print and draw the last entry of \n",
    "    # the normalized best fitness history for each length of target sequence.\n",
    "    normalized_realized_plateau_heights = [history[-1] for history in normalized_best_fitness_histories]\n",
    "    print(\"\\nLength of target sequence and normalized realized plateau height\")\n",
    "    for i, (length, history) in enumerate(zip(lengths_of_target_sequence, normalized_best_fitness_histories)):\n",
    "        print(f\"Length {length}: {history[-1]}\")\n",
    "    # Draw the realized plateau heights\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(lengths_of_target_sequence, normalized_realized_plateau_heights, 'o-')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Normalized Realized Plateau Height')\n",
    "    plt.title(f'Tendency of Normalized Plateau Heights, popSize {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    #Two fitting models are tested: a power model and an exponential model \n",
    "    #Power model: y = a * x^b\n",
    "    x = np.array(lengths_of_target_sequence)\n",
    "    y = np.array(normalized_realized_plateau_heights) -0.5\n",
    "    # Fit the power model\n",
    "    log_x = np.log(x)\n",
    "    log_y = np.log(y)\n",
    "    coefficients = np.polyfit(log_x, log_y, 1)\n",
    "    a = np.exp(coefficients[1])\n",
    "    b = coefficients[0]\n",
    "    print(f\"Power model: y = {a:.4f} * x^{b:.4f}\")  \n",
    "    # exponential model: y = a * exp(b * x)\n",
    "    #y = y -0.5 # Shift the y values to make them more suitable for exponential fitting\n",
    "    coefficients_exp = np.polyfit(x, np.log(y), 1)\n",
    "    a_exp = np.exp(coefficients_exp[1])\n",
    "    b_exp = coefficients_exp[0]\n",
    "    print(f\"Exponential model: y = {a_exp:.4f} * exp({b_exp:.4f} * x)\")\n",
    "    # Models and data are plotted together\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(x, y + 0.5, 'o', label='Data')\n",
    "    plt.plot(x, a * x**b + 0.5, label=f'Power model: y = {a:.4f} * x^{b:.4f} + 0.5')\n",
    "    plt.plot(x, a_exp * np.exp(b_exp * x) + 0.5, label=f'Exponential model: y = {a_exp:.4f} * exp({b_exp:.4f} * x) + 0.5')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Normalized Realized Plateau Height')\n",
    "    plt.title(f'Fitting Models for Normalized Plateau Heights, popSize {population_size}')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    end_time = time.time()\n",
    "    # Calculate the difference in seconds\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_time = timedelta(seconds=elapsed_time)\n",
    "    print(f\"Elapsed time: {elapsed_time}\")\n",
    "\n",
    "maink2(lengths_of_target_sequence, population_size, numbersOfGenerations,    mutation_rate, recombination_rate, alphabet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b68e12",
   "metadata": {},
   "source": [
    " \n",
    "*Nuestras observaciones*\n",
    "\n",
    "Sin recombinación: \n",
    "> 1. La altura de la meseta, $y$, se ajusta a un modelo polinomial que predice \n",
    "   $ y = 8,2 * x ^{-0.4466} + 0,5$, donde $x$ es la longitud de la secuencia objetivo. Hay una pequeña variación de experimento a experimento.\n",
    "> 2. El límite de la proporción de sitios resueltos, $y $, cuando la longitud de la cadena objetivo, $x$, crece es $0,5$ porque \n",
    "\n",
    " >>> $ y - 0.5 =  8.2132 * x ^{-0.4466}  = \\frac{8.2132}{x^{0.4466}} \\sim \\frac{8.2132}{x ^{0.5}} = \\frac{8.2132}{\\sqrt{x}} \\rightarrow 0$\n",
    "\n",
    " >>> O\n",
    "\n",
    " >>> $ y \\rightarrow 0.5$\n",
    "\n",
    " Con tasa de recombinación = 0,01: \n",
    "> 1. La altura de la meseta, $y$, está ajustada por un modelo de polinomio que predice \n",
    "   $ y = 8,2665 * x^{-0,44585} + 0,5$, donde $x$ es la longitud de la secuencia objetivo.  \n",
    "> 2. El límite de la proporción de sitios resueltos, $y $, cuando la longitud de la cadena objetivo, $x$, crece es $0,5$.\n",
    "\n",
    "  *Nuestra interpretación.* \n",
    "\n",
    " 1. La Evolución es movida por el azar. Por lo tanto, es sorprendente que la altura de la meseta se ajuste con precisión mediante un simple modelo matemático, una ley de potencia o monómica. \n",
    " 2. Los recursos, en este caso el tamaño de la población, se pueden contrastar con la complejidad de la siguiente manera:\n",
    "  >>> Los recursos podrían ser suficientes para completar perfectamente una tarea simple, aquí con una longitud de la cadena objetivo igual a 400 o menos.     \n",
    "  >>> A medida que la complejidad de la tarea crece, la Evolución se queda corta en potencia y la proporción máxima alcanzable de sitios resueltos disminuye según una ley polinómica.    \n",
    "  >>> Una complejidad muy grande puede matar a la Evolución, y su efecto podría ser indistinguible del de la aleatoriedad, cuya firma es la mediocridad, $0,5 $. La mitad de los sitios se resuelven por mera casualidad. \n",
    "3. Las fluctuaciones siempre operan. Son claramente visibles cuando la complejidad es baja, pero disminuyen a medida que la complejidad crece. Por lo tanto, la perfección siempre se puede lograr para cualquier cadena objetivo tal vez más allá de un lapso de tiempo más largo que la edad del Universo.\n",
    "4. Los resultados actuales se obtuvieron con un tamaño de población de 30. Esto representa el recurso disponible. Si la longitud de la cadena objetivo es 5000 bits, la proporción de sitios resueltos tiende a 0,7. Esto significa un déficit del 30 %. Este hecho muestra que la Evolución siempre deja un rastro inextinguible de productos claramente imperfectos. \n",
    "5. La meseta representa un estado estable. Esto significa que si por alguna razón la meseta es perturbada, la dinámica interna de la Evolución  obliga al proceso a volver a su estado habitual. Sabemos esto con certeza porque ninguna fluctuación se amplifica, ya sea negativa o positiva. Todas están amortiguadas y devueltas al estado de equilibrio. Para reforzar esta conclusión, recordemos que en trabajos pasados, hemos programado a la Evolución para comenzar con un cromosoma perfecto que codifica una solución perfecta a la tarea dada, y evoluciona hacia la meseta. Así, el gráfico de la mejor aptitud muestra una función decreciente cuando la condición inicial es la perfección. \n",
    "6. Cuando la Evolución asciende a su estado final dado por la meseta, lo hace en general muy rápido para valores de baja complejidad. Pero la velocidad disminuye a medida que crece la complejidad.  \n",
    "7. El papel de la recombinación es, en nuestra opinión, insignificante. \n",
    "\n",
    "**Desafío.** Cuantifica la variación de las fluctuaciones alrededor de la meseta. Demostrar que cuando la complejidad crece, la magnitud de las fluctuaciones disminuye y, por lo tanto, se vuelve más difícil alcanzar la perfección.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e10e87e",
   "metadata": {},
   "source": [
    "## La rampa\n",
    "\n",
    "El gráfico de la mejor aptitud en la población frente a las generaciones se describe razonablemente mediante una rampa seguida por una meseta, ambas representadas por segmentos rectos. Prestemos atención a la rampa para subir a la meseta. Podemos medir su inclinación y el lugar cuando se encuentre con la meseta. Su coordenada $x$ diría cuántas generaciones se necesitan para alcanzar la meseta.  \n",
    "\n",
    "**Ejemplo.** En relación con un gráfico de la proporción de sitios resueltos frente a generaciones a lo largo de un Proceso Evolutivo, una rampa se define por el punto inicial que es la misma condición inicial del Proceso Evolutivo, y un punto final que es la intersección de la rampa con la meseta. La altura de la meseta viene dada por la ley de potencia previamente encontrada:\n",
    "\n",
    "La altura de la meseta, $y$, está ajustada con precisión por el modelo de polinomio.\n",
    "   $ y = 8,2132 * x ^{-0,4466} + 0,5$, donde $x$ es la longitud de la secuencia objetivo.\n",
    "\n",
    "El procedimiento para determinar la rampa es el siguiente: \n",
    ">\n",
    ">\n",
    "> 1. La función del mejor historial de fitness se suaviza localmente para una mejor evaluación. Una ventana de suavización se determina por un ancho, digamos 10, y para cada generación, los siguientes 10 valores se promedian. \n",
    "> 2. Una generación $g$ se determina donde comienza la meseta. Después de muchas pruebas, descubrimos que la siguiente condición funciona bien:\n",
    ">>>\n",
    ">> $smoothedFitness[g]- smoothedFitness[g-1] < 0,001$\n",
    ">>>>\n",
    ">> Este $g$ es el *breakIndex* o *índice de rompimiento.*.  \n",
    "   \n",
    "> 3. La función de fitness debe limitarse al intervalo $[0, breakIndex]$,  y se encuentra la línea de regresión asociada a ella. \n",
    "> 4. La rampa es la línea de regresión de los puntos de la función de fitness, pero restringida al intervalo $[0, crossIndex]$, donde el $crossIndex$ es la generación en la que la línea de regresión intersecta la meseta.\n",
    "\n",
    "Podríamos reemplazar la función de fitness general por el conjunto de meseta y rampa. La rampa se define en el intervalo $[0, crossIndex]$. La meseta se define en el intervalo $[crossIndex,  infty]$.\n",
    "\n",
    "El código para determinar la rampa es el siguiente.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06e2d932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program ramp.py\n",
    "# The task given to la Evolución is to match a binary string of 1000 ones. \n",
    "# The program will run for a fixed number of generations, \n",
    "# and it will print the best fitness (number of matched sites) at each generation. \n",
    "# The graph of the best-fitness-history is replace by a\n",
    "# a ramp-plateau ensemble, composed of two lineal segments. \n",
    "# The ramp is determined and depicted. \n",
    "# With the assitance of the AI Of Visual Studio Code.\n",
    "# Modified from a program made with the help of Gemini. \n",
    "\n",
    "import datetime\n",
    "import random\n",
    "import matplotlib.pyplot as plt # Import matplotlib for plotting\n",
    "import numpy as np\n",
    "\n",
    "numberOfGenerations = 1000 \n",
    "length_of_target_sequence = 700\n",
    "target_sequence = '1'*length_of_target_sequence #1111...1 (700 ones)\n",
    "population_size = 30\n",
    "alphabet = '01' # Binary alphabet, binary strings\n",
    "include_clone_populationk3 = True\n",
    "include_mutate_allk3 = True\n",
    "include_crossover_allk3 = False\n",
    "mutation_rate = 0.01\n",
    "recombination_rate = 0\n",
    "# alphabet = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ .,'\n",
    "# Boolean flag to print the generation, best chromosome and fitness.\n",
    "# This is the ancestral line of the solution.\n",
    "print_best_fitness = False\n",
    "show_graph = True\n",
    "\n",
    "def print_parametersk3():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: solve an instance of the Shakespeare challenge\")\n",
    "    print(f\"Date of Experiment = {datetime.datetime.now()}\")\n",
    "    print(f\"alphabet = {alphabet}\")\n",
    "    print(f\"target_sequence = {target_sequence}\")\n",
    "    print(f\"length_of_target_sequence = {length_of_target_sequence}\")\n",
    "    print(f\"population_size = {population_size}\")\n",
    "    print(f\"include_clone_populationk3 = {include_clone_populationk3}\")\n",
    "    print(f\"include_mutate_allk3 = {include_mutate_allk3}\")\n",
    "    print(f\"include_crossover_allk3 = {include_crossover_allk3}\")\n",
    "    if include_mutate_allk3:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk3:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    if include_clone_populationk3:\n",
    "        print(f\"print_best_fitness = {print_best_fitness}\")\n",
    "\n",
    "def maxk3(data):\n",
    "    max_value = data[0]\n",
    "    for x in data:\n",
    "        if x > max_value:\n",
    "            max_value = x\n",
    "    return max_value\n",
    "\n",
    "\n",
    "def mean_stddev_and_maxk3(data):\n",
    "    mean = sum(data) / len(data)\n",
    "    variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
    "    stddev = variance ** 0.5\n",
    "    max_value = maxk3(data)\n",
    "    return mean, stddev, max_value\n",
    "\n",
    "def create_populationk3(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with random chromosomes.\"\"\"\n",
    "    # print(\"create_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        random_string = ''.join(random.choice(alphabet) for _ in range(len(target_sequence)))\n",
    "        population.append(random_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def create_perfect_populationk3(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with perfect chromosomes.\"\"\"\n",
    "    # print(\"create_perfect_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        perfect_string = '1'* len(target_sequence)\n",
    "        population.append(perfect_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def calculate_fitnessk3(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome based on its similarity to the target sequence.\"\"\"\n",
    "    # print(\"\\n *calculate_fitness8(chromosome, target_sequence)\")\n",
    "    fitness = 0\n",
    "    for i in range(len(chromosome)):\n",
    "        if chromosome[i] == target_sequence[i]:\n",
    "            fitness += 1\n",
    "    # print(chromosome, \" fitness: \", fitness)\n",
    "    return fitness\n",
    "\n",
    "def calculate_all_fitnessk3(population, target_sequence):\n",
    "    # print(\"\\n*calculate_all_fitness8(population, target_sequence)\")\n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = [calculate_fitnessk3(chromosome, target_sequence) for chromosome in population]\n",
    "    # print(\"fitness_scores: \", fitness_scores)\n",
    "    return fitness_scores\n",
    "\n",
    "def select_the_bestk3(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome for reproduction.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk3(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome_index = fitness_scores.index(best_fitness)\n",
    "    best_chromosome = population[best_chromosome_index]\n",
    "    # print(\"\\n*select_the_bestk3(population)\")\n",
    "    # print(\"best_fitness = \", best_fitness)\n",
    "    # print(\"The chro = \", best_chromosome )\n",
    "    return best_fitness, best_chromosome\n",
    "\n",
    "def clone_populationk3(best_chromosome, population_size):\n",
    "    \"\"\"Creates a new population by cloning the best chromosome.\"\"\"\n",
    "    return [best_chromosome] * population_size\n",
    "\n",
    "def mutatek3(chromosome, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "            mutated_chromosome_list[i] = random.choice(alphabet)\n",
    "            counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "def mutate_allk3(population, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    touched_numbers = [] # List to store the number of touched sites for each chromosome\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek3(chromosome, mutation_rate, alphabet)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "        touched_numbers.append(counter)\n",
    "    return mutated_population, touched_numbers\n",
    "\n",
    "def crossoverk3(parent1, parent2, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, len(target_sequence))\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "def crossover_allk3(population, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 = population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk3(parent1, parent2, target_sequence, recombination_rate)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "def run_one_generationk3(population, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    # Calculate fitness scores for the current population\n",
    "    best_fitness, best_chromosome = select_the_bestk3(population, target_sequence)\n",
    "    # print(f\" best_chromosome {best_chromosome}, best_fitness {best_fitness}\")\n",
    "    # Clone the best to build the new population\n",
    "    new_population = []\n",
    "    if include_clone_populationk3:\n",
    "        new_population = clone_populationk3(best_chromosome, population_size)\n",
    "    if include_mutate_allk3:\n",
    "        new_population, touched_numbers = mutate_allk3(new_population, mutation_rate, alphabet)\n",
    "    if include_crossover_allk3:\n",
    "        new_population = crossover_allk3(new_population, target_sequence, recombination_rate)\n",
    "    return best_fitness, best_chromosome, new_population, touched_numbers\n",
    "\n",
    "def find_the_ramp(best_fitness_history, length_of_target_sequence, plateau_height):\n",
    "    '''# The ramp is expected to be formed when the best fitness reaches ceases to grow.\n",
    "    # The ramp is a line that best represents the graph of the fitness function\n",
    "    # in the interval [0, break_index], \n",
    "    # starting from the first point of the fitness graph and ending at the break point.\n",
    "    # This break point is found when the change in the smoothed best fitness history \n",
    "    # is less than a threshold (0.001) for the first time.\n",
    "    # The ramp parameters are calculated by fitting a line to the best fitness history, \n",
    "    # with the x-axis being the generations and the y-axis being the best fitness at each generation.\n",
    "    # The line is fitted using the least squares method, \n",
    "    # which minimizes the sum of the squared differences \n",
    "    # between the observed values and the values predicted by the line.\n",
    "    # the range is restricted to the interval [0, break_index] \n",
    "    # to focus on the ramp part of the graph.\n",
    "    # Our proposal is to replace the best fitness history \n",
    "    # with a broken line version of it. \n",
    "    #  We have a ramp in the initial part, and a plateau in the second part.\n",
    "    # The ramp goes from the first point of the best fitness history to the cross point,\n",
    "    # and the plateau goes from the cross point \n",
    "    # to the last point of the best fitness history.\n",
    "\n",
    "    # With the assitance of the AI of Visual Studio Code.\n",
    "    '''\n",
    "    \n",
    "    # Best_fitness_history is smoothed for a better assessment of the ramp.\n",
    "    width_of_window = 30\n",
    "    smoothed_best_fitness_history = []\n",
    "    for i in range(len(best_fitness_history) - width_of_window + 1):\n",
    "        window_average = sum(best_fitness_history[i : i + width_of_window]) / width_of_window\n",
    "        smoothed_best_fitness_history.append(window_average)\n",
    "    # Draw the smoothed best fitness history\n",
    "    plt.plot(smoothed_best_fitness_history, label='Smoothed Best Fitness', color='purple')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Smoothed Best Fitness')\n",
    "    plt.title(f'Smoothed Best Fitness over Generations, length_of_target_sequence = {length_of_target_sequence}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()  \n",
    "    print(f\"Expected height of the plateau: {plateau_height:.2f}\") \n",
    "    # A break point is found when the change in the smoothed best fitness history \n",
    "    # is less than a threshold (0.001) for the first time.\n",
    "    break_index = None\n",
    "    for i in range(1, len(smoothed_best_fitness_history)):\n",
    "        change =  smoothed_best_fitness_history[i] - smoothed_best_fitness_history[i-1]\n",
    "        if change < 0.001:\n",
    "            break_index = i\n",
    "            print(f\"break index (end of growth): {break_index:.2f}, best fitness at break point: {smoothed_best_fitness_history[break_index]:.2f}\")\n",
    "            break\n",
    "    # The ramp is a line that best represents the graph of the fitness function\n",
    "    # in the interval [0, break_index], \n",
    "    # starting from the first point of the fitness graph and ending at the break point.\n",
    "    \n",
    "\n",
    "    # The ramp parameters are calculated by fitting a line to the best fitness history, \n",
    "    # with the x-axis being the generations and the y-axis being the best fitness at each generation.\n",
    "    # The line is fitted using the least squares method, \n",
    "    # which minimizes the sum of the squared differences \n",
    "    # between the observed values and the values predicted by the line.\n",
    "    # the range is restricted to the interval [0, break_index] \n",
    "    # to focus on the ramp part of the graph.\n",
    "    Initial_segment_of_the_best_fitness_history = best_fitness_history[:break_index]\n",
    "    x = np.array(range(len(Initial_segment_of_the_best_fitness_history)))\n",
    "    y = np.array(Initial_segment_of_the_best_fitness_history)\n",
    "    coefficients = np.polyfit(x, y, 1)\n",
    "    m = coefficients[0]\n",
    "    b = coefficients[1]\n",
    "    print(f\"Ramp parameters y = {m:.2f}x + {b:.2f}: b = {b:.2f}, m = {m:.2f}\")\n",
    "    # The cross_point is the point where the ramp and the plateau intersect,\n",
    "    # and it is calculated by finding the intersection of the ramp line \n",
    "    # and the horizontal line that represents the plateau. \n",
    "    # To find the cross point, \n",
    "    # we can set the equation of the ramp line equal \n",
    "    # to the equation of the horizontal line and solve for x.\n",
    "    # Equation of the ramp line: y = mx + b\n",
    "    # Equation of the horizontal line: y = plateau_height\n",
    "    # Setting them equal gives: mx + b = plateau_height\n",
    "    # Solving for x gives: x = (plateau_height - b) / m\n",
    "    cross_index = int((plateau_height - b) / m) if m != 0 else None\n",
    "    print(f\"Cross index (intersection of ramp and plateau): {cross_index:.2f}, best fitness at cross point: {best_fitness_history[cross_index]:.2f}\")\n",
    "    print(f\"The ramp goes from 0 to {cross_index:.2f}, and the plateau goes from {cross_index:.2f} to {len(best_fitness_history)-1:.2f}\")\n",
    "\n",
    "    return b, m, cross_index\n",
    "\n",
    "def plot_Best_Fitnessk3(best_fitness_history, length_of_target_sequence, population_size,  ):\n",
    "     # The plateau is expected to be formed when the best fitness reaches ceases to grow.\n",
    "    # The height of the plateau is expected to be less than the length of the target sequence, and it is expected to decrease as the length of the target sequence increases.\n",
    "    # The expected height of the plateau follows a power law: $ y = 8.2132 * x ^{-0.4466} + 0.5 $\n",
    "    normalized_height_of_plateau =  8.2132 * length_of_target_sequence ** (-0.4466) + 0.5\n",
    "    height_of_plateau = normalized_height_of_plateau * length_of_target_sequence\n",
    "    \n",
    "    b, m, cross_index = find_the_ramp(best_fitness_history, length_of_target_sequence, height_of_plateau)\n",
    "    # Plot the fitness history\n",
    "    plt.plot(best_fitness_history, label='Best Fitness', color='blue')\n",
    "    # The red dashed line represents the length of the target sequence, which is the maximum fitness.\n",
    "    plt.axhline(y=length_of_target_sequence, label='Target Sequence Length', color='red', linestyle='--', linewidth=2)\n",
    "    # The ramp parameters are calculated by fitting a line to the best fitness history, \n",
    "    # with the x-axis being the generations and the y-axis being the best fitness at each generation.\n",
    "    x = np.array(range(len(best_fitness_history[:cross_index])))\n",
    "    y = np.array(best_fitness_history[:cross_index])\n",
    "    plt.plot(x, m * x + b, label='Ramp', color='green', linestyle='-', linewidth=2)\n",
    "    \n",
    "\n",
    "    plt.axhline(y = height_of_plateau, label='Expected Plateau Height', color='orange', linestyle='--', linewidth=2)\n",
    "    # The red dashed line serves as a reference point for the best fitness, indicating the optimal solution when the target sequence is fully matched.\n",
    "    # The blue line represents the best fitness at each generation, which is expected to increase over time and eventually reach the red dashed line when the target sequence is matched.\n",
    "    # The x-axis represents the generations, and the y-axis represents the best fitness at each generation.\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Genetic Algorithm - Fitness over Generations, length_of_target_sequence = {length_of_target_sequence}, popSize {population_size}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def maink3( length_of_target_sequence, population_size, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    print_parametersk3()\n",
    "    all_touched_numbers = [] # List to store the arrays of number of touched sites for each generation\n",
    "    population = create_populationk3(population_size, target_sequence, alphabet)\n",
    "    #population = create_perfect_populationk3(population_size, target_sequence, alphabet)\n",
    "    # print(population)\n",
    "    best_fitness, best_chromosome = select_the_bestk3(population, target_sequence)\n",
    "    generation = 0\n",
    "    best_fitness_history = [] # List to store best fitness of each generation\n",
    "    best_fitness_history.append(best_fitness)\n",
    "    if print_best_fitness:\n",
    "        print(\"Generation and best_fitness\")\n",
    "    #while best_fitness < len(target_sequence):\n",
    "    while generation < numberOfGenerations :\n",
    "        generation += 1\n",
    "        best_fitness, best_chromosome, population, touched_numbers = run_one_generationk3(population, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        if print_best_fitness:\n",
    "            #print(f\" {generation} {best_chromosome} {best_fitness}\")\n",
    "            print(f\" {generation} {best_fitness}\")\n",
    "        best_fitness_history.append(best_fitness)\n",
    "        all_touched_numbers.append(touched_numbers) # Append the array of touched numbers for this generation to the main list\n",
    "        #print(f\"\\nTarget sequence matched in generation {generation}\")\n",
    "        #print(f\"Final chromosome: {best_chromosome}\")\n",
    "    totalCost = population_size * generation\n",
    "   \n",
    "    print(f\"Total cost = population size * number of generations: {totalCost}\")\n",
    "    \n",
    "    if show_graph:\n",
    "        plot_Best_Fitnessk3(best_fitness_history, length_of_target_sequence, population_size)\n",
    "   \n",
    "if __name__ == \"__main__\": maink3( length_of_target_sequence, population_size, target_sequence, mutation_rate, recombination_rate, alphabet)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5487e63e",
   "metadata": {},
   "source": [
    "*Nuestras observaciones.* Tenemos un procedimiento para reemplazar la función de fitness por el conjunto *rampa-meseta*:\n",
    ">\n",
    "> 1. El punto inicial de la línea de rampa está bastante distante del verdadero punto inicial.\n",
    "> 2. La pendiente de la línea está, por lo tanto, sesgada hacia valores bajos.\n",
    "> 3. El punto de cruce de la línea de rampa con la meseta parece representar bastante bien la rampa. \n",
    "\n",
    "Ahora, podemos comparar fácilmente diferentes rampas para diferentes valores de la longitud de la cadena objetivo. Podemos considerar la pendiente de la línea de rampa y la coordenada X del punto de cruce como indicadores a comparar. \n",
    "\n",
    "**Ejercicio.** Encuentra el mejor modelo matemático de la rampa para un tamaño de población de 30. Evalúa el papel de la recombinación.  \n",
    ">\n",
    "> *Respuesta.* Prestamos atención a la rampa. Para cada caso, el punto inicial de la rampa es el dado por el AG y el punto final por un procedimiento de error mínimo, que también puede ser ejecutado por la Evolución. Con ese objetivo, reutilizamos un programa de la contribución anterior. El código sigue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ab050c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0:00:00.002703\n",
      "Lab: Simple Genetic Algorithm\n",
      "Purpose: compare diverse plateaus for different degrees of complexity\n",
      "Date of Experiment  = 2026-05-18 08:56:25.532188\n",
      "alphabet =  01\n",
      "population_size = 30\n",
      "include_clone_populationk4 = True\n",
      "include_mutate_allk4 = True\n",
      "include_crossover_allk4 = True\n",
      "mutation_rate = 0.01\n",
      "recombination_rate = 0.01\n",
      "print_best_fitness = False\n",
      "Total cost = population size * number of generations: 12000\n",
      "Expected height of the plateau: 426.20\n",
      "break index (end of growth): 191.00, best fitness at break point: 395.00\n",
      "Ramp parameters y = 0.86x + 262.45: b = 262.45, m = 0.86\n",
      "Cross index (intersection of ramp and plateau): 191.00, best fitness at cross point: 395.00\n",
      "The ramp goes from 0 to 191.00, and the plateau goes from 191.00 to 400.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 400\n",
      "slope: 0.8565565582805186\n",
      "intersection: 262.4543520942408\n",
      "cross_index: 191\n",
      "Total cost = population size * number of generations: 21000\n",
      "Expected height of the plateau: 505.93\n",
      "break index (end of growth): 214.00, best fitness at break point: 484.63\n",
      "Ramp parameters y = 0.88x + 329.58: b = 329.58, m = 0.88\n",
      "Cross index (intersection of ramp and plateau): 200.00, best fitness at cross point: 481.00\n",
      "The ramp goes from 0 to 200.00, and the plateau goes from 200.00 to 700.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 500\n",
      "slope: 0.8787550777387639\n",
      "intersection: 329.5761356226907\n",
      "cross_index: 200\n",
      "Total cost = population size * number of generations: 21000\n",
      "Expected height of the plateau: 875.59\n",
      "break index (end of growth): 211.00, best fitness at break point: 879.63\n",
      "Ramp parameters y = 1.56x + 598.45: b = 598.45, m = 1.56\n",
      "Cross index (intersection of ramp and plateau): 177.00, best fitness at cross point: 860.00\n",
      "The ramp goes from 0 to 177.00, and the plateau goes from 177.00 to 700.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 1000\n",
      "slope: 1.5576078486478186\n",
      "intersection: 598.4464365554862\n",
      "cross_index: 177\n",
      "Total cost = population size * number of generations: 21000\n",
      "Expected height of the plateau: 1551.19\n",
      "break index (end of growth): 302.00, best fitness at break point: 1598.53\n",
      "Ramp parameters y = 1.65x + 1184.78: b = 1184.78, m = 1.65\n",
      "Cross index (intersection of ramp and plateau): 221.00, best fitness at cross point: 1565.00\n",
      "The ramp goes from 0 to 221.00, and the plateau goes from 221.00 to 700.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 2000\n",
      "slope: 1.6541171201452727\n",
      "intersection: 1184.780538981051\n",
      "cross_index: 221\n",
      "Total cost = population size * number of generations: 27000\n",
      "Expected height of the plateau: 3415.21\n",
      "break index (end of growth): 233.00, best fitness at break point: 3438.50\n",
      "Ramp parameters y = 3.32x + 2761.50: b = 2761.50, m = 3.32\n",
      "Cross index (intersection of ramp and plateau): 196.00, best fitness at cross point: 3364.00\n",
      "The ramp goes from 0 to 196.00, and the plateau goes from 196.00 to 900.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 5000\n",
      "slope: 3.3201693969786312\n",
      "intersection: 2761.4955430835266\n",
      "cross_index: 196\n",
      "Total cost = population size * number of generations: 36000\n",
      "Expected height of the plateau: 11971.07\n",
      "break index (end of growth): 245.00, best fitness at break point: 11866.77\n",
      "Ramp parameters y = 6.68x + 10508.87: b = 10508.87, m = 6.68\n",
      "Cross index (intersection of ramp and plateau): 218.00, best fitness at cross point: 11829.00\n",
      "The ramp goes from 0 to 218.00, and the plateau goes from 218.00 to 1200.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 20000\n",
      "slope: 6.680851740936224\n",
      "intersection: 10508.87486311598\n",
      "cross_index: 218\n",
      "Total cost = population size * number of generations: 39000\n",
      "Expected height of the plateau: 22892.63\n",
      "break index (end of growth): 372.00, best fitness at break point: 22910.83\n",
      "Ramp parameters y = 5.98x + 21081.62: b = 21081.62, m = 5.98\n",
      "Cross index (intersection of ramp and plateau): 302.00, best fitness at cross point: 22769.00\n",
      "The ramp goes from 0 to 302.00, and the plateau goes from 302.00 to 1300.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 40000\n",
      "slope: 5.984696166996095\n",
      "intersection: 21081.61574274264\n",
      "cross_index: 302\n",
      "Total cost = population size * number of generations: 39000\n",
      "Expected height of the plateau: 54803.00\n",
      "break index (end of growth): 267.00, best fitness at break point: 54320.23\n",
      "Ramp parameters y = 13.45x + 51261.77: b = 51261.77, m = 13.45\n",
      "Cross index (intersection of ramp and plateau): 263.00, best fitness at cross point: 54247.00\n",
      "The ramp goes from 0 to 263.00, and the plateau goes from 263.00 to 1300.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length_of_target_sequence: 100000\n",
      "slope: 13.44877874713617\n",
      "intersection: 51261.769355469834\n",
      "cross_index: 263\n",
      "slopes:  [np.float64(0.8565565582805186), np.float64(0.8787550777387639), np.float64(1.5576078486478186), np.float64(1.6541171201452727), np.float64(3.3201693969786312), np.float64(6.680851740936224), np.float64(5.984696166996095), np.float64(13.44877874713617)]\n",
      "intersections:  [np.float64(262.4543520942408), np.float64(329.5761356226907), np.float64(598.4464365554862), np.float64(1184.780538981051), np.float64(2761.4955430835266), np.float64(10508.87486311598), np.float64(21081.61574274264), np.float64(51261.769355469834)]\n",
      "cross_indexes:  [191, 200, 177, 221, 196, 218, 302, 263]\n",
      "draw_all_historiesk4(normalized_best_fitness_histories, lengths_of_target_sequence)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "draw_slopes_intersections_and_cross_indexesk4(slopes, intersections, cross_indexes, lengths_of_target_sequence)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Power model for slopes: y = 0.0483 * x^0.4817\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear model for Y-intersects of the ramps: y = 0.5126 * x + 176.1369\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program comparingRamps.py\n",
    "# la Evolución battles against different degrees of complexity,\n",
    "# which are given by the lengths of target sequences.\n",
    "# Every la Evoluciónary Process is ascending to end in a plateau.\n",
    "# The expected height of the plateaus follows a power law: \n",
    "# $ y = 8.2132 * x ^{-0.4466} + 0.5 $ \n",
    "# The purpose is to compare the ramps that connect\n",
    "# the initial point of the simulation to the arisen plateaus. \n",
    "# As indicator of the ramp we choose the cross point, \n",
    "# which is the point where the ramp and the plateau intersect.\n",
    "# At last, we compare cross points for different lengths of target sequence,\n",
    "# and we test two fitting models for the tendency of the cross points:\n",
    "# a logarithmic model and a power model. ?????\n",
    "\n",
    "# With the assitance of the AI of Visual Studio Code.\n",
    "# Modified from a program made with the help of Gemini.\n",
    "\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt # Import matplotlib for plotting\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "\n",
    "start_time = time.time() # Record the start time\n",
    "\n",
    "#lengths_of_target_sequence = [10, 100, 200, 300, 400, 500, 1000, 2000,   5000,  20000, 100000]\n",
    "#numbersOfGenerations =      [100, 200, 200, 300, 400, 700,  700,  700,    900,   1200,  1300 ]\n",
    "lengths_of_target_sequence = [400, 500, 1000, 2000,   5000,  20000, 40000, 100000]\n",
    "numbersOfGenerations =      [ 400, 700,  700,  700,    900,   1200,  1300,   1300 ]\n",
    "\n",
    "population_size = 30\n",
    "alphabet = '01' # Binary alphabet, binary strings\n",
    "include_clone_populationk4 = True\n",
    "include_mutate_allk4 = True\n",
    "include_crossover_allk4 = True\n",
    "mutation_rate = 0.01\n",
    "recombination_rate = 0.01\n",
    "print_best_fitness = False\n",
    "show_graph = True\n",
    "\n",
    "\n",
    "def print_parametersk4():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: compare diverse plateaus for different degrees of complexity\")\n",
    "    print(f\"Date of Experiment  = {datetime.datetime.now()}\")\n",
    "    print(f\"alphabet =  {alphabet}\")\n",
    "    print(f\"population_size = {population_size}\")\n",
    "    print(f\"include_clone_populationk4 = {include_clone_populationk4}\")\n",
    "    print(f\"include_mutate_allk4 = {include_mutate_allk4}\")\n",
    "    print(f\"include_crossover_allk4 = {include_crossover_allk4}\")\n",
    "    if include_mutate_allk4:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk4:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    if include_clone_populationk4:\n",
    "        print(f\"print_best_fitness = {print_best_fitness}\")\n",
    "\n",
    "def maxk4(data):\n",
    "    max_value = data[0]\n",
    "    for x in data:\n",
    "        if x > max_value:\n",
    "            max_value = x\n",
    "    return max_value\n",
    "\n",
    "\n",
    "def mean_stddev_and_maxk4(data):\n",
    "    mean = sum(data) / len(data)\n",
    "    variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
    "    stddev = variance ** 0.5\n",
    "    max_value = maxk4(data)\n",
    "    return mean, stddev, max_value\n",
    "\n",
    "def create_populationk4(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with random chromosomes.\"\"\"\n",
    "    # print(\"create_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        random_string = ''.join(random.choice(alphabet) for _ in range(len(target_sequence)))\n",
    "        population.append(random_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def create_perfect_populationk4(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with perfect chromosomes.\"\"\"\n",
    "    # print(\"create_perfect_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        perfect_string = '1'* len(target_sequence)\n",
    "        population.append(perfect_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def calculate_fitnessk4(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome based on its similarity to the target sequence.\"\"\"\n",
    "    # print(\"\\n *calculate_fitness8(chromosome, target_sequence)\")\n",
    "    fitness = 0\n",
    "    for i in range(len(chromosome)):\n",
    "        if chromosome[i] == target_sequence[i]:\n",
    "            fitness += 1\n",
    "    # print(chromosome, \" fitness: \", fitness)\n",
    "    return fitness\n",
    "\n",
    "def calculate_all_fitnessk4(population, target_sequence):\n",
    "    # print(\"\\n*calculate_all_fitness8(population, target_sequence)\")\n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = [calculate_fitnessk4(chromosome, target_sequence) for chromosome in population]\n",
    "    # print(\"fitness_scores: \", fitness_scores)\n",
    "    return fitness_scores\n",
    "\n",
    "def select_the_bestk4(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome for reproduction.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk4(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome_index = fitness_scores.index(best_fitness)\n",
    "    best_chromosome = population[best_chromosome_index]\n",
    "    # print(\"\\n*select_the_bestk4(population)\")\n",
    "    # print(\"best_fitness = \", best_fitness)\n",
    "    # print(\"The chro = \", best_chromosome )\n",
    "    return best_fitness, best_chromosome\n",
    "\n",
    "def clone_populationk4(best_chromosome, population_size):\n",
    "    \"\"\"Creates a new population by cloning the best chromosome.\"\"\"\n",
    "    return [best_chromosome] * population_size\n",
    "\n",
    "def mutatek4(chromosome, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "            mutated_chromosome_list[i] = random.choice(alphabet)\n",
    "            counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "def mutate_allk4(population, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    touched_numbers = [] # List to store the number of touched sites for each chromosome\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek4(chromosome, mutation_rate, alphabet)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "        touched_numbers.append(counter)\n",
    "    return mutated_population, touched_numbers\n",
    "\n",
    "def crossoverk4(parent1, parent2, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, len(target_sequence))\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "def crossover_allk4(population, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 = population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk4(parent1, parent2, target_sequence, recombination_rate)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "def run_one_generationk4(population, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    # Calculate fitness scores for the current population\n",
    "    best_fitness, best_chromosome = select_the_bestk4(population, target_sequence)\n",
    "    # print(f\" best_chromosome {best_chromosome}, best_fitness {best_fitness}\")\n",
    "    # Clone the best to build the new population\n",
    "    new_population = []\n",
    "    if include_clone_populationk4:\n",
    "        new_population = clone_populationk4(best_chromosome, population_size)\n",
    "    if include_mutate_allk4:\n",
    "        new_population, touched_numbers = mutate_allk4(new_population, mutation_rate, alphabet)\n",
    "    if include_crossover_allk4:\n",
    "        new_population = crossover_allk4(new_population, target_sequence, recombination_rate)\n",
    "    return best_fitness, best_chromosome, new_population, touched_numbers\n",
    "\n",
    "def find_the_ramp(best_fitness_history, length_of_target_sequence, plateau_height):\n",
    "    '''# The ramp is expected to be formed when the best fitness reaches ceases to grow.\n",
    "    # The ramp is a line that best represents the graph of the fitness function\n",
    "    # in the interval [0, break_index], \n",
    "    # starting from the first point of the fitness graph and ending at the break point.\n",
    "    # This break point is found when the change in the smoothed best fitness history \n",
    "    # is less than a threshold (0.001) for the first time.\n",
    "    # The ramp parameters are calculated by fitting a line to the best fitness history, \n",
    "    # with the x-axis being the generations and the y-axis being the best fitness at each generation.\n",
    "    # The line is fitted using the least squares method, \n",
    "    # which minimizes the sum of the squared differences \n",
    "    # between the observed values and the values predicted by the line.\n",
    "    # the range is restricted to the interval [0, break_index] \n",
    "    # to focus on the ramp part of the graph.\n",
    "    # Our proposal is to replace the best fitness history \n",
    "    # with a broken line version of it. \n",
    "    #  We have a ramp in the initial part, and a plateau in the second part.\n",
    "    # The ramp goes from the first point of the best fitness history to the cross point,\n",
    "    # and the plateau goes from the cross point \n",
    "    # to the last point of the best fitness history.\n",
    "\n",
    "    # With the assitance of the AI of Visual Studio Code.\n",
    "    '''\n",
    "    \n",
    "    # Best_fitness_history is smoothed for a better assessment of the ramp.\n",
    "    width_of_window = 30\n",
    "    smoothed_best_fitness_history = []\n",
    "    for i in range(len(best_fitness_history) - width_of_window + 1):\n",
    "        window_average = sum(best_fitness_history[i : i + width_of_window]) / width_of_window\n",
    "        smoothed_best_fitness_history.append(window_average)\n",
    "\n",
    "    '''\n",
    "    # Draw the smoothed best fitness history\n",
    "    plt.plot(smoothed_best_fitness_history, label='Smoothed Best Fitness', color='purple')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Smoothed Best Fitness')\n",
    "    plt.title(f'Smoothed Best Fitness over Generations, length_of_target_sequence = {length_of_target_sequence}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show() \n",
    "    ''' \n",
    "\n",
    "    print(f\"Expected height of the plateau: {plateau_height:.2f}\") \n",
    "    # A break point is found when the change in the smoothed best fitness history \n",
    "    # is less than a threshold (0.001) for the first time.\n",
    "    break_index = None\n",
    "    for i in range(1, len(smoothed_best_fitness_history)):\n",
    "        change =  smoothed_best_fitness_history[i] - smoothed_best_fitness_history[i-1]\n",
    "        if change < 0.001:\n",
    "            break_index = i\n",
    "            print(f\"break index (end of growth): {break_index:.2f}, best fitness at break point: {smoothed_best_fitness_history[break_index]:.2f}\")\n",
    "            break\n",
    "    # The ramp is a line that best represents the graph of the fitness function\n",
    "    # in the interval [0, break_index], \n",
    "    # starting from the first point of the fitness graph and ending at the break point.\n",
    "    \n",
    "\n",
    "    # The ramp parameters are calculated by fitting a line to the best fitness history, \n",
    "    # with the x-axis being the generations and the y-axis being the best fitness at each generation.\n",
    "    # The line is fitted using the least squares method, \n",
    "    # which minimizes the sum of the squared differences \n",
    "    # between the observed values and the values predicted by the line.\n",
    "    # the range is restricted to the interval [0, break_index] \n",
    "    # to focus on the ramp part of the graph.\n",
    "    Initial_segment_of_the_best_fitness_history = best_fitness_history[:break_index]\n",
    "    x = np.array(range(len(Initial_segment_of_the_best_fitness_history)))\n",
    "    y = np.array(Initial_segment_of_the_best_fitness_history)\n",
    "    coefficients = np.polyfit(x, y, 1)\n",
    "    m = coefficients[0]\n",
    "    b = coefficients[1]\n",
    "    print(f\"Ramp parameters y = {m:.2f}x + {b:.2f}: b = {b:.2f}, m = {m:.2f}\")\n",
    "    # The cross_point is the point where the ramp and the plateau intersect,\n",
    "    # and it is calculated by finding the intersection of the ramp line \n",
    "    # and the horizontal line that represents the plateau. \n",
    "    # To find the cross point, \n",
    "    # we can set the equation of the ramp line equal \n",
    "    # to the equation of the horizontal line and solve for x.\n",
    "    # Equation of the ramp line: y = mx + b\n",
    "    # Equation of the horizontal line: y = plateau_height\n",
    "    # Setting them equal gives: mx + b = plateau_height\n",
    "    # Solving for x gives: x = (plateau_height - b) / m\n",
    "    cross_index = int((plateau_height - b) / m) if m != 0 else None\n",
    "    print(f\"Cross index (intersection of ramp and plateau): {cross_index:.2f}, best fitness at cross point: {best_fitness_history[cross_index]:.2f}\")\n",
    "    print(f\"The ramp goes from 0 to {cross_index:.2f}, and the plateau goes from {cross_index:.2f} to {len(best_fitness_history)-1:.2f}\")\n",
    "\n",
    "    return b, m, cross_index\n",
    "\n",
    "def plot_Best_Fitnessk4(best_fitness_history, length_of_target_sequence ):\n",
    "     # The plateau is expected to be formed when the best fitness reaches ceases to grow.\n",
    "    # The height of the plateau is expected to be less than the length of the target sequence, and it is expected to decrease as the length of the target sequence increases.\n",
    "    # The expected height of the plateau follows a power law: $ y = 8.2132 * x ^{-0.4466} + 0.5 $\n",
    "    normalized_height_of_plateau =  8.2132 * length_of_target_sequence ** (-0.4466) + 0.5\n",
    "    height_of_plateau = normalized_height_of_plateau * length_of_target_sequence\n",
    "    \n",
    "    b, m, cross_index = find_the_ramp(best_fitness_history, length_of_target_sequence, height_of_plateau)\n",
    "    # Plot the fitness history\n",
    "    plt.plot(best_fitness_history, label='Best Fitness', color='blue')\n",
    "    # The red dashed line represents the length of the target sequence, which is the maximum fitness.\n",
    "    plt.axhline(y=length_of_target_sequence, label='Target Sequence Length', color='red', linestyle='--', linewidth=2)\n",
    "    # The ramp parameters are calculated by fitting a line to the best fitness history, \n",
    "    # with the x-axis being the generations and the y-axis being the best fitness at each generation.\n",
    "    x = np.array(range(len(best_fitness_history[:cross_index])))\n",
    "    y = np.array(best_fitness_history[:cross_index])\n",
    "    plt.plot(x, m * x + b, label='Ramp', color='green', linestyle='-', linewidth=2)\n",
    "    plt.axhline(y = height_of_plateau, label='Expected Plateau Height', color='orange', linestyle='--', linewidth=2)\n",
    "    # The red dashed line serves as a reference point for the best fitness, indicating the optimal solution when the target sequence is fully matched.\n",
    "    # The blue line represents the best fitness at each generation, which is expected to increase over time and eventually reach the red dashed line when the target sequence is matched.\n",
    "    # The x-axis represents the generations, and the y-axis represents the best fitness at each generation.\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Genetic Algorithm - Fitness over Generations, length_of_target_sequence = {length_of_target_sequence}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    return b, m, cross_index\n",
    "\n",
    "def onela EvoluciónaryProcessk4( length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    population = create_populationk4(population_size, target_sequence, alphabet)\n",
    "    #population = create_perfect_populationk4(population_size, target_sequence, alphabet)\n",
    "    # print(population)\n",
    "    best_fitness, best_chromosome = select_the_bestk4(population, target_sequence)\n",
    "    generation = 0\n",
    "    best_fitness_history = [] # List to store best fitness of each generation\n",
    "    best_fitness_history.append(best_fitness)\n",
    "    if print_best_fitness:\n",
    "        print(\"Generation and best_fitness\")\n",
    "    #while best_fitness < len(target_sequence):\n",
    "    while generation < numberOfGenerations :\n",
    "        generation += 1\n",
    "        best_fitness, best_chromosome, population, touched_numbers = run_one_generationk4(population, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        if print_best_fitness:\n",
    "            #print(f\" {generation} {best_chromosome} {best_fitness}\")\n",
    "            print(f\" {generation} {best_fitness}\")\n",
    "        best_fitness_history.append(best_fitness)\n",
    "        #print(f\"\\nTarget sequence matched in generation {generation}\")\n",
    "        #print(f\"Final chromosome: {best_chromosome}\")\n",
    "    totalCost = population_size * generation\n",
    "   \n",
    "    print(f\"Total cost = population size * number of generations: {totalCost}\")\n",
    "    \n",
    "    b, m, cross_index =  plot_Best_Fitnessk4(best_fitness_history, length_of_target_sequence)\n",
    "    # Each best_fitness_history is normalized to the length of the target sequence,\n",
    "    # so it is between 0 and 1. Thus, it can be compared with other normalized histories. \n",
    "    normalized_best_fitness_history = [fitness / length_of_target_sequence for fitness in best_fitness_history]\n",
    "    print(f\"length_of_target_sequence: {length_of_target_sequence}\")\n",
    "    print(f\"slope: {m}\")\n",
    "    print(f\"intersection: {b}\")\n",
    "    print(f\"cross_index: {cross_index}\")\n",
    "    return normalized_best_fitness_history, b, m, cross_index\n",
    "   \n",
    "def draw_all_historiesk4(normalized_best_fitness_histories, lengths_of_target_sequence):\n",
    "    print(\"draw_all_historiesk4(normalized_best_fitness_histories, lengths_of_target_sequence)\")\n",
    "    # Plot all the normalized best fitness histories together\n",
    "    # By the AI of VSCode. \n",
    "    for i in range(len(normalized_best_fitness_histories)):\n",
    "        # x_range contains all numbers from 0 to the length of the normalized best fitness history for the current length of target sequence.\n",
    "        x_range = range(len(normalized_best_fitness_histories[i]))\n",
    "        plt.plot(x_range, normalized_best_fitness_histories[i], label=f'Length {lengths_of_target_sequence[i]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Normalized Best Fitness')\n",
    "    plt.title('Genetic Algorithm - Normalized Fitness over Generations for Different Lengths of Target Sequence')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def draw_slopes_intersections_and_cross_indexesk4(slopes, intersections, cross_indexes, lengths_of_target_sequence):\n",
    "    ''' \n",
    "    This function plots the slopes of the ramps, \n",
    "    the y-values of the intersections of the ramps with the Y-axis, \n",
    "    and the x-coordinates of the cross points of the ramps with the plateaus for each length of target sequence. \n",
    "    The x-axis represents the lengths of the target sequences, \n",
    "    and the y-axis represents the corresponding values of \n",
    "    the slopes, intersections, and cross indexes. \n",
    "    Each of the three plots is displayed separately with appropriate labels and titles for better visualization and comparison.\n",
    "    By the AI of VSCode. \n",
    "    '''\n",
    "\n",
    "    print(\"draw_slopes_intersections_and_cross_indexesk4(slopes, intersections, cross_indexes, lengths_of_target_sequence)\")\n",
    "    # Plot the slopes of the ramps for each length of target sequence\n",
    "    plt.plot(lengths_of_target_sequence, slopes, label='Slopes of Ramps', color='blue', marker='o')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Slope of Ramp')\n",
    "    plt.title('Slope of Ramp vs Length of Target Sequence')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    # Calculate and draw the normalized slopes by dividing the slopes by the lengths of the target sequences\n",
    "    normalized_slopes = [slope / length for slope, length in zip(slopes, lengths_of_target_sequence)]\n",
    "    plt.plot(lengths_of_target_sequence, normalized_slopes, label='Normalized Slopes', color='red', marker='o')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Normalized Slope')\n",
    "    plt.title('Normalized Slope vs Length of Target Sequence')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "   \n",
    "    # slopes are fitted to a power model, and the fitted curves are plotted together with the data points.\n",
    "     # Power model: y = a * x^b\n",
    "    x = np.array(lengths_of_target_sequence)\n",
    "    y = np.array(slopes) \n",
    "    # Fit the power model\n",
    "    log_x = np.log(x)\n",
    "    log_y = np.log(y)\n",
    "    coefficients = np.polyfit(log_x, log_y, 1)\n",
    "    a = np.exp(coefficients[1])\n",
    "    b = coefficients[0]\n",
    "    print(f\"Power model for slopes: y = {a:.4f} * x^{b:.4f}\")  \n",
    "    # Model and data are plotted together\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(x, y, 'o', label='Data')\n",
    "    plt.plot(x, a * x**b, label=f'Power model: y = {a:.4f} * x^{b:.4f}')\n",
    "    plt.xlabel('Length of Target Sequence 1')\n",
    "    plt.ylabel('Slope of Ramp')\n",
    "    plt.title('Power Model for Slope of Ramp vs Length of Target Sequence')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    ''' \n",
    "    # Plot the y-value of the intersection of each ramp with the Y-axis for each length of target sequence\n",
    "    plt.plot(lengths_of_target_sequence, intersections, label='Intersections with Y-axis', color='orange', marker='o')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Intersection with Y-axis')\n",
    "    plt.title('Intersection with Y-axis vs Length of Target Sequence')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    '''\n",
    "    # Intersections are fitted to a linear model, and the fitted line is plotted together with the data points.\n",
    "    coefficients = np.polyfit(x, intersections, 1)\n",
    "    m = coefficients[0]\n",
    "    b = coefficients[1]\n",
    "    print(f\"Linear model for Y-intersects of the ramps: y = {m:.4f} * x + {b:.4f}\")  \n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(x, intersections, 'o', label='Data')           \n",
    "    plt.plot(x, m * x + b, label=f'Linear model: y = {m:.4f} * x + {b:.4f}')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Intersection with Y-axis')\n",
    "    plt.title('Fitting Model for Intersection with Y-axis vs Length of Target Sequence')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    # Plot the x-coordinates of the cross points for each length of target sequence\n",
    "    plt.plot(lengths_of_target_sequence, cross_indexes, label='Cross Indexes', color='green', marker='o')\n",
    "    plt.xlabel('Length of Target Sequence')\n",
    "    plt.ylabel('Cross Index (Generation)')\n",
    "    plt.title('Cross Index vs Length of Target Sequence')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "def maink4(lengths_of_target_sequence, numbersOfGenerations,    mutation_rate, recombination_rate, alphabet):\n",
    "    ''' An la Evoluciónary process is triggered for each length of the target string.\n",
    "    The normalized best fitness history is stored in a list, \n",
    "    and then all the histories are plotted together.\n",
    "    A graphic is also plotted to show the tendency of the cross point\n",
    "    of the ramp line with the plateau. \n",
    "    We plot the x-coordinate of the cross point against the length of the target sequence,\n",
    "    Two fitting models are tested: a logarithmic model and a power model.\n",
    "    With the assitance of the AI of Visual Studio Code.\n",
    "    '''\n",
    "    print_parametersk4()\n",
    "    # List to store the normalized best fitness history for each length of target sequence   \n",
    "    normalized_best_fitness_histories = []\n",
    "    slopes = [] # List to store the slopes of the ramps for each length of target sequence\n",
    "    intersections = [] # List to store the y-value of the intersections of each ramp with the Y-axis. \n",
    "    cross_indexes = [] # List to store the x-coordinates of the cross points for each length of target sequence\n",
    "    for i in range(len(lengths_of_target_sequence)):\n",
    "        length_of_target_sequence = lengths_of_target_sequence[i]\n",
    "        target_sequence = '1'* length_of_target_sequence  \n",
    "        numberOfGenerations = numbersOfGenerations[i]\n",
    "        normalized_best_fitness_history, b, m, cross_index = onela EvoluciónaryProcessk4( length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        normalized_best_fitness_histories.append(normalized_best_fitness_history)\n",
    "        intersections.append(b)\n",
    "        slopes.append(m)\n",
    "        cross_indexes.append(cross_index)\n",
    "    \n",
    "    #print(\"normalized_best_fitness_histories: \", normalized_best_fitness_histories)\n",
    "    print(\"slopes: \", slopes)\n",
    "    print(\"intersections: \", intersections)\n",
    "    print(\"cross_indexes: \", cross_indexes) \n",
    "    draw_all_historiesk4(normalized_best_fitness_histories, lengths_of_target_sequence)\n",
    "    draw_slopes_intersections_and_cross_indexesk4(slopes, intersections, cross_indexes, lengths_of_target_sequence)\n",
    "\n",
    "    end_time = time.time()# Record the end time\n",
    "    elapsed_time = end_time - start_time\n",
    "    # Calculate the difference in seconds and convert it to a timedelta object for better formatting\n",
    "    elapsed_time = timedelta(seconds=elapsed_time)\n",
    "    print(f\"Execution time: {elapsed_time}\")\n",
    "\n",
    " \n",
    "maink4(lengths_of_target_sequence, numbersOfGenerations,    mutation_rate, recombination_rate, alphabet)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5e0c43",
   "metadata": {},
   "source": [
    "*Nuestras observaciones.*\n",
    "\n",
    "Sin recombinación:\n",
    "> 1. Los gráficos muestran que la pendiente de la rampa disminuye a medida que aumenta la complejidad. Pero el gráfico de la pendiente contra la longitud de la secuencia objetivo es una función creciente. La solución a esta contradicción es: si normalizamos la pendiente de la rampa, dividiendo por la longitud de la cadena objetivo, vemos una función que disminuye. Ahora, todos los gráficos tienen la misma medida, por lo que están normalizados. Y así, se ve una tendencia a la disminución. \n",
    "> 2. Un modelo de potencia o polinónico se ajusta a la función de la pendiente de la rampa frente a la longitud de la secuencia objetivo: $y = 0,0346 * x^{0,5112}  \\sim 0,03 *  \\sqrt{x}$. La variabilidad es baja. \n",
    "> 3. Los intersecciones crecen linealmente con la longitud de la cadena objetivo: $ y =  \\frac{1}{2} x + 200$. La variabilidad en la pendiente es baja y alta en el término independiente. \n",
    "\n",
    "Con tasa de recombinación = 0,01:\n",
    "> 1. Un modelo de potencia se ajusta a la función de la pendiente de la rampa frente a la longitud de la secuencia objetivo: $y = y = 0,0483 * x^{0,4817}  \\sim 0,04 *  \\sqrt{x}$.\n",
    "> 2. Los intersecciones crecen linealmente con la longitud de la cadena objetivo: $ y = 0,5126 * x + 176$.\n",
    "> 3. La generación en la que el punto de cruce de la rampa con la meseta tiene una tendencia difusa a crecer proporcionalmente a la raíz cuadrada de la longitud de la secuencia objetivo. \n",
    "\n",
    "*Nuestra interpretación. *\n",
    "1. El papel de la recombinación es, en nuestra opinión, insignificante.\n",
    "2. La intersección Y de la rampa está 200 unidades por encima de lo predicho por el azar, que es la mitad de la longitud de la cadena objetivo. Por lo tanto, 200 es el efecto inicial de MAX para un tamaño poblacional de 30.\n",
    "3. La generación en la que ocurre el punto de cruce de la rampa con la meseta es errática, bailando alrededor de 200. \n",
    "4. Las raíces cuadradas aparecen con frecuencia en estos modelos ajustables.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de85184f",
   "metadata": {},
   "source": [
    "# El efecto de los recursos\n",
    "\n",
    "Hemos visto cómo la Evolución se comporta bajo la presión de la complejidad: si la presión es baja, la Evolución funciona rápidamente. Si la presión aumenta, la Evolución se amortigua hasta que finalmente su trabajo es indistinguible del de la aleatoriedad. \n",
    "\n",
    "Investiguemos ahora el efecto de los recursos. Se espera que si el tamaño de la población crece, la meseta se alcance más rápido y sea mayor. Ahora, nos preguntamos: ¿están las tendencias descritas por un modelo de poder con una raíz cuadrada en alguna parte? \n",
    "\n",
    "**Ejemplo.** Vamos a trazar la tendencia del número de sitios resueltos cuando la longitud de la cadena objetivo es 10000, y el tamaño de la población asume valores variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b7ee9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lab: Simple Genetic Algorithm\n",
      "Purpose: compare diverse plateaus for different degrees of complexity\n",
      "Date of Experiment  = 2026-05-21 05:59:51.969847\n",
      "alphabet =  01\n",
      "length_of_target_sequence = 10000\n",
      "include_clone_populationk5 = True\n",
      "include_mutate_allk5 = True\n",
      "include_crossover_allk5 = True\n",
      "mutation_rate = 0.01\n",
      "recombination_rate = 0.01\n",
      "print_best_fitness = False\n",
      "\n",
      "Generation 400\n",
      "Population size: 10\n",
      "Best chromosome: 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\n",
      "Best fitness: 6032\n",
      "Total cost = population size * number of generations: 4000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 600\n",
      "Population size: 20\n",
      "Best chromosome: 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\n",
      "Best fitness: 6371\n",
      "Total cost = population size * number of generations: 12000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 600\n",
      "Population size: 50\n",
      "Best chromosome: 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\n",
      "Best fitness: 6566\n",
      "Total cost = population size * number of generations: 30000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 600\n",
      "Population size: 100\n",
      "Best chromosome: 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\n",
      "Best fitness: 6763\n",
      "Total cost = population size * number of generations: 60000\n"
     ]
    },
    {
     "data": {
      "image/png": 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W608++WToNd2tUfYhsl2K6ndre/T2he7du7s7w5nR9DmpbqN9UsuqO7geVe/WnUdlcfzlrrY6/rt8uuuk/cn/G8e6Tl6ZK+vi3+czou1G03v7skcZIf/xw59p1F13bd+R9eK9tkT+bHxu/2aZrZ+OZ5F3arUdaR/wt4nMrPpo5LFA2SxNq7v5frobrgylf71iXdecblPReNkyZQeUVfJ4NSN0rM7psUrZLr+rr77ava7P5EamMNr2ecUVV7jsi//clFH10Wjfn911jOU4mhntF16mNjMZZQI8qs2h+fi301j3icy+M6Pjb06zKFmdw/Werg2iUXZIyy46pkSur+fpp5927/3444+B7Io8lyqTpeW55557wqZTFl13/P2ve+X1yiuvhB2Lda5UrSrPI4884qbTscF/vKxXr166rEVWbQpVg8df60lNffR6tCx/RpTh0Wf8x2NdkykrpJpV/mOiV5shuzK6Hom2Dao2TGQ5evu/Mvj+mgBad+1vymT5r8tUe0bT+/eZ7Jx3clp9VNk/ZfUya6d81113ufn/9NNPYa/feuutblvzMkDeuePBBx8MTaP5tmnTJseZQq27PuuvzSReltr/ulcbQVlXj2r46XyoGjUeZf803QcffJDhdXZ2yj6ajGqBZZRJz41M4bv/f8+//h7VgNF+7dF1oK7vI6nWgOah9cyrecYi5t5HdbdQot3l050a3ZHxHk8//bR7fd26de6OrO6CeRkcPXS3WlmJpUuXujsgfroz4O9tTHcV1Bvh8uXLQ3dXlM3THRZvfnoo6ldGJPIOWix0Z0J3H5Sxi4zKJavez3QXUlG9lsnjLZ/u9Gfms88+c3dbLrvsstBrupvnZds8uiukaF93XpT58jRu3NjdjVNPq5F0xyg3RM5Hv4l+Q2+b8OiOl/9utZdR1t1t/11Y7/W86PlNdwW1jfgfuruaEx06dAi766ey1p0Yb7l13lO2W9uN/vZvj9q+dTdWGQHRHWLdCdPdzoxoGt21VdY6Vto3dKdbdzuVsfBUq1bNZSB1t837nc4++2x3N1/ZJY8+q/1J72V3nTy6K6jseE6PIcOHDw87fijj73n33XfdnUJl0P3LorvnErm/5+Zvltn6FStWLJR91m+g/UHrVbdu3XSfj5X2YR3HdEfW76abbnLLqsxNdtY1p9tUVrStqeaH/26k9mnvGJSTY1Xk8U6ZMIk2bU74fz/vXKTjmO4o//jjj9meX24cjzM6jma2DrpLrN7vlGXKCWVUdIdeWYXbb789x/tEpEceeSTdcTfaQzUZcosysxnVSlBGUu9703n7bLTp/NPsCx1XdS2h6x1/Ger8oxo7kccqHS/8tSa0LtqX/Puvrg+0rylz4l9m//VCrJTt8ZeXV3MhO+dhbddaRv8xWuuh6zZl3bR95RX/PqzzmPYd1VzTMS7a9qky0vHUM3v2bPcZvZ6a+m8lOWVLIzNG2T3v5ISWWxnMzHqI1HLod9Ly+ZdDx36dd6ZOnRr6XbROqm3m0bp7x9Gc0HeXLVvWHc/8363Mn37zyDJQTRRvmxKdy3U+9G9fOs40adIkXQ1B/3X2vpa9MtOxHItUky+3bM/iGOM/vujvWI5FeTHPWMRcfdS7qI/WFaqqTelEq6oF/oOc0sk6yag6hR7RqPqL/wJD6XE/b2f1ToIKJMXbQCJFpk9joSoeOjEfeeSRlhOvvfaaqzqqi3Kts/djKEDShnfKKadk+FkFu7qAj6z6oINd5HSinSySdiAFnzrAaDk8qqqUGzL7TfzlHTmdDigSmbb2Xs/swmbXrl3upoKfDjL+g3w0qnqjA2ZuiFwfb9295dZ2o4BqxIgR7pHR9i2qFqTqZjqh6rft1KmTC9pU1cyj6jwKQFReOvB27drVHeD8wV4kLYMubDPaLnSRsmLFCld9QwdjHWhVXVRVPER/q8qGtz9lZ52yu535jyHeNuDdNPD2PQVA/iEptL+rqom/Wm9my5Kbv1lm66dyVbVJVYtWlRr/MntV8rJL+7iqnUZWY9Pv6L2fnXXN6TaVFV3gRlLVFVVNzumxKnKeCnYVdOfWWIOqcqkgSDcpI4MwBT3ZlZN1jPU4mhGd9FXNTPuIqumquqeq5+v3jOXGl9ZbVRV1vlW1XO8iLCf7RCRtW/ubggSdJ6JRtWYviPD+V9XvaNP5p9kXOlbpeifa/iH+qnai6oqRN5y1TfiH9dJ2pn0hcrrI64NYZHVtFQstT7TmQ/5jVE6vo7KiC1tVvX7ppZdcMsHfJCbaPhx53Pb22ciyUzAVWfU6u+ednFD1Vh0zVVVY+6SuCXRDwT90h5ZD20NWy+FdR0becI12fIqVvlvlqirumX13ds5Hv/zyizvfZ/W9+1L2Orfty/ktJ7I6xviPL/o7lmNRXswzV4NCXcRpo1u4cGG697yDROQJXBdOojrsXnulSJE7aEYX/d4BwJun6nJHOxH67wDtD1outUvTBYDulETbgHURnFvtKLIjN050sfwmWU0X6+f9VG9eY8P46eJ7fw72Heu2qBshGdXdV+bAO2kuWbLEtcvV3V/dMVNAoczmnXfe6abRCUF32tQWQBk8tXXSRaDuQHttTPaVMoJq96I7bwo+VJdfWW1vv8nOOmV3O1NAKjqG+INhBSzejQPvjqhHy6NA/9FHH406z8gbDrn5m2W2fmqnqxtdyrqozaHXblVtr7zvyGux7Ff7Y5vKC5EXwRnV1ohlTEsFPGqXo8BLbTx1ka2bdsou6GZNfvq9sqLtSxk91WxR4KltUBfKCnabNWuW6WeV1VTGWO3X/UFoTvaJSLqBl1GAFrkv+W8I7Qtdj+j31znWf+Gq5VBGSDdYRPumAmpldyN5r3nT7guVo7ZTZfSj/daR1wC5sT1kx/7+vtymrJcCQu0DLVu2dNuRNw5xtH14X65/snveyQlts6ptoP1Y24weWj/d5FE7TW85lKnLKMOum3HZpTKL9ptHHkv13VrGjDJqkUFbbm1f+1r2ut6OlryKpOXNKPDMybFIMjrG+I8vmjayhqT/s960eTHPWGQrglLGS43jdVLxGjBmxovWdYcst7I3XnUpbaxZzTOrap8ebRg6SUYLeLOijjxULVAXG97dMo/ukKhahU7gGXWuUatWLZcOV7bHny30Mo7+6USBRSRVf1K2x39XOjtiLaf9SVmtyGoVOa0Gmlfrre1GgZUOprFs3/p9FJTpoQsX3bVXgKZG416aXzu37iDqoYsdNcTWNBldwGsZtN1ktF0oUPEfRPXdCkIVlCrboOyBTqo5XafsUFZDnR3oJOMPCrPa37///ntXNTk3ttPcWj91qqCbFho4OzIA8Totkuwss/ZxZZMjO73wqjd6x4Dsyu42lRWvtoafOgDwbtjk5Filefrv7Ov4p4sDb55eVkPl6xeZPY1G1S0VJCgQVicY/ptMkWL9vfLyeBzLPqFsoR4qN3U6oOqbqrGSEe13Og+pDLybM7m5T+hYpnNhVhR0quOK3KD19qoFKgPu0XNtO977OgbqIlOvR1LVal2n5EYnM/pddAGs7TgnF+sZbWeqkqn5+rfNyOuD/XUe1/JktM177++rjNZDx1xtP9rW/ZmQyGNCRrxlU9n5bzirMw8lNPw3PrJz3tmXcld1Xt3k0UPbrI7Rqnmnmz1KmGg5FOBktV9q3SZOnJguARHtt9KxNFqV4chjqb5b5yOdq3MrwaB5ZnWdva/n/Icffjh0oz2rMsutmihHHnmku7GuY4xuxHp0nafA3/+ajku65te1l//mnNfhk3fcyot5xiLmNoWiuxW6ANXdcVUVzeqOgAI3tTfURh4t2lW1lexSxlErrTv10Xo788/TOylnddDQSUPtZNQ7YrQTR2Z3Oryqo+ohUD0v+R+qu66qJJnVXdb6aD3UO6VHBwevXab/wk4/rO4g+ddHO5gyAP6TYnZp+WM9sO4vOnDpQOh/eIFTbol1+8jsTpOqQijAinag82+LuiiNPBkos6xtS7+/Lsgiq8Bo/9EdnmjVAvzLoGon6rHWf4DT/qle6tT2w3+Q0I0LXSCp2qge2q78F8rZWafs0slFdz1VRU3LG03kvqYDn+6A+fcPf3Uir1e5WOXW+mk+kcuqthCRd+uys41pH9Z2oN7d/NTrmk6O2Q3icrpNZUXBhX89dZNQJx9v+XJyrIo83qnHRvHmqW1YgZbXhsajbHtWvDvY/t9LJ9Zon9XvFUt10rw8HmdENw696kD+CygFNJn9nrqwU9VZ9VCp81xe7BPxaFOoKu/KAqpHST8913WKv9mGzsdqz+0/v+uCWRlW9SCcGxQYqyx1QRp5bNDzyHNALHR9oH1NNTo82gaiHQ9j3Xb3hbZr7e/Tp08PvaZjsI7puoETrbZUbl2PRDvm6jgRS20BUX8RqtqvslMg6NH1WWQV2uycd3J6/RS5Peg61AtMvf1Zy6GyjtY3hb7TWw/9Lvrbvy+oXLzjaOQxQ0G8f79WEKbeRP303ZqHasJE0nflZJ11nNF3qeZKJO+33ddzfjzaFJYtW9Zdoyoe0E1dj2o0KlD3H2N0LFK5+qvq6/dWlli1Lr2b+Hkxz1zPFCrA0YWmqpuprrIa6Cqjox9Td131njZsf9e+OtnrwlQXogqSdFdOF6za0JVh0waSHbo40Iav7o51x1tZDt3pVJfcn3zyibvw9C6qvHYO6rhBB1cdVPxZET8FmTqZq5qR1w2uAlld6KmzDn9X0v5C14lUF7oZBSxqIK62R5FVXDw6SSvrqru+uoOlu7g6AXjt6fx3SlT1SxdJqjqhNmFeF+jaeNQlek6pnHThoHS9Lhh1pzM7w44UVN72oYslbRfKaOuOXXbu8OsOvO7QqLy0feukqN9OVdNUpt7vqMBNmU5tn8rQqc68tlNduOiiTgdY7TfaubVP6W6fPq8LGf+d0Wjuvvtud5DTfqY7jbq7pBsx2j7VpiySsoWqtqptVttR5HAdsa5TTugApzYT2u61LeugpxsAa9ascfPWRb8/+NF+rnYX6qRDy6Ty08FPJzW9rpNltM6hMpMb66esp2oHqPMGddGujqZ0kolsy6ATsI4d6kxHv7O2LX1vtHaK2vZ0B1vbowJ8bQc6JimAVpWpWLs69+hEEss2pUyavlfDAMVyHNEdbG1r6tRA25i6RdfFlv+CP7vHKp0/dKzUtqFzg7YTtbnVcnvU9bt+O/2v31zbijKUWdHvo21MWQadC3RM1Yk12s0+HRN0s0RDVxx99NGuzPS7RJNXx+OMaF1191wXTdpmtZ/r4krn04zOa6Lztc6ROn9HZhN17tLxaF/3idxsU6iMhTeMlBfE6Rjn3d3XMUGUvdAFqzop0gWSzvEaFknrqEy4vwMgHRd1kanjrZqz6Fiv853WXedeP28on+xWe9P+qeVUzQ9vqAPt89q29TvpukLfnd0hAXSe0G+oYQp0M0LHGe96w399kJ1tN6c0rJiay2i7176kMtaNEa2jroUizyW5eT2iY662C+1f2j51nNB0sbbh1o1Y7ZeqhqobCtqP9Dspcx3ZbjM7552cXj/pOKb9Ssui47S2ex0/dLPJq3WmZIOuB7Xu3vAOCop0vlHmVMuvm2X6nbWM+n30mspHtQKi3SRQUkfLqv1Fxy1dm+r8pH4H/O2tdS2s7U/V05WZ0jWM9hvVTtB1sa5rdW7JDq2Pllv7q5ZD66My0DpqGXS839dzfm63Kbz7/8cetUsXbYOKCcTrrEt0zNG5xoshFN/oPKty87cT1bah9ddxQmWv86n2If1ukTWP8mKeWQrkgLpB16Cghx12mBsAVF2Uq4tkDUrtH7zZo67y1UW3ulBVl/garPLUU08NvPfee+m6EY4cEsLrTjmyG1g979y5s+suW8ugQS41qO/s2bPDuuS99tprXffU6tLdv7qR3Zh7A9BrOTW9utJVV/TqUjmjweu94SQyG+BXA6hqGnX/nNEAoeoy+7zzzgsNXq/1ULe7+txbb70VNu0XX3wROP74412ZqztjDWqb0WDJ/sFAI9/zU3fcGrpA8/R3G57RfKJ1+R6t+2n/YNfZHWg+rwev93f5rO1RXSBHDl8RrTtt/XaR3U//+eefbtqaNWu67VvbuYZiGTFiRNjwHCpjdYmtbUvbqwYc9QYb1Tam502aNAkNQK+/n3nmmZjWX4PXa3/QoKbqZv/EE090gzhHowGLvW6Zv/7666jTxLJOOf0d1aW6hmBp2bKl24bVXbvmr2OCurmO7KJ7165dblBndbusstOgyc2bNw/ceeedYYO15vZvltn6aRgDdbWtbvy132ifVPfo0br7/uijj9zgv1pPf/fg0Y4F6t5dQztoIGstl4btyWzw+szWNdZtasyYMTF1W+3fn9VVvspOv4e6PfeGjsjpsUqva+gNLad+32uuuSZs8HqvS3oNnaBjpKY766yzXLfnsQxJoePpcccdFxokfODAgYHPP/883blly5Yt7lisYVr83ZZnNCTGvhyPoy1nZjRchH5znWv1W6ocNHC4hpjwi9wGM+uW3b/usewT+0NmwwtF60pfy1e3bt3QwN4abDvaMFIrVqxw25h+Jx0ndbyJNni7ji3+7t6zO3i9rgs0HIJ+Iz30e6lclyxZkm7w+kjRjgm//vqrG25C25iuTXTc8a49ZsyYkeW2m9FxLLNhXmIZvF7fo2uvY445Jmzw+n0dkiKj6xENyXLJJZe4ob70++l8p2kjj+8ZXUt6nnjiCfcZHbu07Do26Dfv0qVLjs47GS1vVnT926lTJzesjbbdgw8+2A2Ts3r16nTnBA0npettTaf115BuDz/8sFtGj4bG0SDw3uD1+jva4PXy2muvuetbzU9D6+hYGG3b8/YvrbfWT8ddDb+m46eGvYocvD5StPOhllPHd113eQPT67v9w+HEWvb7g2Vy/IykYTT022i/0L6q7d8/HJtH57abb77ZHWe0fhomRUN9RJMX88xMyv9XGvmMqmip217dkYi1/RUAZJcyfLr7r5oK0bq19uiuo+6CK0OW3YxHRnTnXtXtVJXJ3xYTiAdl15X9UvY7cpiU/ETLp8HXlTnw996O7FNzHWXSVf03WpXFgsw7ZqsaoTKNQFb2PdePfRY5hohXF1xVZVVFFgDyiqroqGODzAJCIBmoSrKCrJyMA7i/rg/UplDNA1QdmIAwe1R2kXkQDc+iKoyqNgwku/07fgOiUh13HfjVNkVtdFQXXEMyqJ1jbvX6BADRqI0h4kttf7IaYDi3e19GempzmNm4wvGgDJbGgFNbM20najOp9lW5Pfh2Vp3UKIOqdnk5pXbj+2u4kozMmDHDZVjV/kptEdVmVm2u1NNjbnU4pJv6WXXOpPae8RimDMgKQWE+oIbGajyqMex0J0uNRJUpvOaaa+K9aACAPKZORLyxyTJCS4/kpA5BNBSYgkAFHOpE5K233nIdhuUWdVCjTrOyqlGwL9k0b9y1/TFcSUbUQ6p6YnziiSdcdlCBrnqrVEdL+xLw+q1YsSJqR2J+sXbqBexvtCkEACCONBadBpbPTG6PGwp41NO617tiRtRTpDdeaE6oh87MqOfO3BjSIt50Y9/rnXJ/9ZAJ5BaCQgAAAABIYnQ0AwAAAABJjDaFQIzdVqt6lwYj9g9yCwAA8i+1x9VwI6qiWqgQuRAgIwSFQAwUEKqBOgAAKHjUCUyNGjXivRhAvkVQCMRAGULvpKLxI3PL7t27bfz48dapUycrUqQIvwVlxbYVB+yHlBXbVeLug5s2bXI3db3zOIDoCAqBGHhVRhUQ5nZQWLJkSTdPgkLKKjexbVFWeYHtirIqqNsVTT+AzFG5GgAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAAAACQxAgKAQAAACCJERQCAAAAQBIjKAQAAACAJEZQCAAAAABJjKAQAAAAAJIYQSEAAAAAJDGCQuSZqVOnWrdu3ax69eqWkpJio0ePDns/EAjY4MGDrVq1alaiRAnr0KGDLV26NGyadevW2fnnn29lypSxcuXKWd++fW3Lli1h08yfP9/atGljxYsXt5o1a9qDDz6Yblneffddq1evnpumUaNGNm7cuDxaawAAAKBgIShEntm6das1adLEnn766ajvK3h74oknbPjw4fbtt99aqVKlrHPnzrZjx47QNAoIFy1aZBMmTLCxY8e6QPPyyy8Pvb9p0ybr1KmT1apVy+bMmWMPPfSQ3XHHHTZixIjQNNOmTbNzzz3XBZRz5861Hj16uMfChQv59QEAAJD0UpO+BJBnTj75ZPeIRlnCxx9/3G6//Xbr3r27e+2VV16xKlWquIziOeecY4sXL7bPPvvMZs2aZS1atHDTPPnkk9a1a1d7+OGHXQby9ddft127dtmLL75oRYsWtYYNG9q8efPs0UcfDQWPw4YNsy5dutiAAQPc87vuussFmU899ZQLSAEAAIBkRlCIuFi2bJmtWbPGVRn1lC1b1o499libPn26Cwr1v6qMegGhaPpChQq5zOLpp5/upmnbtq0LCD3KNj7wwAO2fv16K1++vJvmxhtvDPt+TRNZndVv586d7uHPSEqgXj0LFMo8wR5o2tT2fvhh2GuFTz/dUubNSzdt4UDAOu3caYWLFbNASoqlXX+9pfXv/+8EmzdbauPGFos9779vdtRRoecpn3xiha+5JusPlipleyKypoVuvdUKvf12lh9NO/lkS3vmmfB1Ou44S/nzzyw/u/feey1w7rn/vrBkiaV26ZLh9P6y2j19ulm1aqH3UkaOtML33JPldwYOP9z2jh8fPt+LLrKUr77K8rNpffpY2n//G/Zaau3aFou9o0ZZ4IQT/l3eL7+0whdfHNNn9yxbFva80F13WaEXX8zycynHH2923nm2e/fu0GuFO3WylIgq2lGX97bbLHDppf++sHq1pbZqFdvyfvaZWd26/y7Hm29a4f/8J8vPBapUsb0zZoS9Vujqq63Qp59m+dm0s8+2tPvvD3st9cgjVV0hy8/ufeop292pk/vbldV331lqz54Wiz3z55sdcMC/y/v441Zo2LAsP5edY0SkeB8jQtvTgAEWeO+9fHWM8NszbVrcjxEqq059+4aO7/ntGBFo08b2vvJK2GtxO0YMGhR2LszNY0QgLS2m5QKSHUEh4kIBoSgz6Kfn3nv6v3LlymHvp6amWoUKFcKmqR1xYe7NU+8pKNT/mX1PNPfdd5/deeed6V5PWb3aMj+1m60vXdq+imiz2Obnn63CypXp52dmJXzPl86ZY0t8n03dts1OifK5aKZPmWLrfetUfdo0OzqGz+4uUSJdG8um8+dbrRg+u+aHH2x2xGc7LV9uJf75J8vPLpg5034vWzb0/IDff7eTMvlOf1kp07ujUqXQe3VmzbIjY1jezYUK2eSI5W35449WOYbPLvv+e1sU8dnuMf42M7/+2v7yBSgHzp1rrWL8bORv0/D77+2wGD677qefQmXlOfGXX6xMDJ/9cdYs+6V69dDz4n//bZ1jXN6vJk+2zb/8Enp+8MyZ1iyGz6ra+PiIdW3xww92UAyf/WP+fJsX8dmuK1ZYke3bs/zs3GnTbNX/L0JVVuV//NHaxriu4z//3PaULBl6XnfOHKsXw2ezc4yIlF+OEasWLcp3xwi/SfnlGBHDesbrGPHXjz/a9IjPxu0YsWpV2LkwN48RWZ2zAQQRFAJRDBo0KCy7qEyhOrEJVKuWZaaw3GGHuSqufoWff94CER3keNVolZEsVqyY64zn8ObNrY7/s5s3W+Cgg2L6jVq2axeeBQgEYvpsaqlS6Za30NSpFli8OMvPVm3QIP261qplgeLFs/xso2OOsSP9n12yJNPl9ZfVSR07hmcBVq2yQMTd/WhK16mTfnnfessCf/2V5WdrN2litSI+G+tvc0zr1uFZgFKlYv5sut9mzhwLzJmT5ecqHHGE+79jx45WpEgR93fhxx+P6a55vaOPtrr+7129OublbXPiieFZgI0bLZBJVt5TrEqV9Os6dqwFli/P8rM1Gje26hGfTdX+GkOmsFmrVtawY0cXELqyqlo15nXt1LlzeKbwp58s8PXXWX4uO8eISPE+Rij7pbKq3rBhvjtG+OWHY4TKanvFiqHje347RlSqVy/9usbpGJH24Ydh58LcPEa49Vm9OqZlA5JaANgPtKl9+OGHoee//PKLe23u3Llh07Vt2zZw3XXXub9feOGFQLly5cLe3717d6Bw4cKBDz74wD2/8MILA927dw+bZtKkSW7e69atc89r1qwZeOyxx8KmGTx4cKBx48YxL//GjRvdPPV/btq1a1dg9OjR7n9QVmxb8cF+SFmxXSXuPphX528g0dD7KOJCVT6rVq1qEydODMvGqa1gy5Yt3XP9v2HDBterqGfSpEmWlpbm2h5606hHUn+7Kd3Frlu3rqs66k3j/x5vGu97AAAAgGRGUIg8o/EE1ROoHl7nMvr7999/d9VD+vfvb3fffbd9/PHHtmDBArvoootcj6IaLkLq16/veg297LLLbObMmfbNN9/YNddc4zqh0XRy3nnnuU5mNNyEhq54++23XW+j/qqf119/vevF9JFHHrEff/zRDVkxe/ZsNy8AAAAg2dGmEHlGgdeJajvwf16g1rt3bxs1apQNHDjQjWWooSOUEWzdurUL3jTAvEdDTih4a9++vet1tGfPnm5sQ3+PpePHj7d+/fpZ8+bNrVKlSjZ48OCwsQxbtWplb7zxhhv+4j//+Y8dfvjhrufRI9U7IQAAAJDkCAqRZ9q1a+c6B8mIsoVDhw51j4yop1EFdJlp3LixfZVFd+G9evVyDwAAAADhqD4KAAAAAEmMoBAAAAAAkhhBIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAAAgiREUAgAAAEASIygEAAAAgCRGUAgAAAAASYygEAAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAAAACQxAgKAQAAACCJERQCAAAAQBIjKAQAAACAJEZQCAAAAABJjKAQAAAAAJIYQSEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoBAAAAIIkRFAIAAABAEiMoBAAAAIAkRlAIAAAAAEmMoBAAAAAAkhhBIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAAAgiREUAgAAAEASIygEAAAAgCRGUAgAAAAASYygEAAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAAAACQxAgKAQAAACCJERQCAAAAQBIjKAQAAACAJEZQCAAAAABJjKAQAAAAAJIYQSEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoBAAAAIIkRFAIAAABAEiMoBAAAAIAkRlAIAAAAAEmMoBAAAAAAkhhBIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAAAgiREUAgAAAEASIygEAAAAgCRGUAgAAAAASYygEAAAAACSGEEh4mrz5s3Wv39/q1WrlpUoUcJatWpls2bNCr0fCARs8ODBVq1aNfd+hw4dbOnSpWHzWLdunZ1//vlWpkwZK1eunPXt29e2bNkSNs38+fOtTZs2Vrx4catZs6Y9+OCD+20dAQAAgPyMoBBxdemll9qECRPs1VdftQULFlinTp1c4Ldy5Ur3voK3J554woYPH27ffvutlSpVyjp37mw7duwIzUMB4aJFi9x8xo4da1OnTrXLL7889P6mTZvcfBV4zpkzxx566CG74447bMSIEXFZZwAAACA/IShE3Gzfvt3ef/99F/i1bdvWDjvsMBes6f9nn33WZQkff/xxu/3226179+7WuHFje+WVV2zVqlU2evRoN4/FixfbZ599ZiNHjrRjjz3WWrdubU8++aS99dZbbjp5/fXXbdeuXfbiiy9aw4YN7ZxzzrHrrrvOHn30UX59AAAAJL3UpC8BxM2ePXts7969rkqnn6qJfv3117Zs2TJbs2aNyxx6ypYt64K/6dOnu+BO/6vKaIsWLULTaPpChQq5zOLpp5/uplHQWbRo0dA0yjY+8MADtn79eitfvny6Zdu5c6d7+LONsnv3bvfILd68cnOeiYqyorzYtuKP/ZCyKmjbFedXIDYEhYibAw44wFq2bGl33XWX1a9f36pUqWJvvvmmC+KULVRAKHrdT8+99/R/5cqVw95PTU21ChUqhE1Tu3btdPPw3osWFN5333125513pnt9/PjxVrJkScttqvoKyiovsG1RVmxX8cU+GN+y2rZtW67PE0hEBIWIK7Ul7NOnjx100EFWuHBhO+qoo+zcc891bf/iadCgQXbjjTeGZQrVQY3aJqpDm9y8g6mTYMeOHa1IkSK5Nt9ERFlRXmxb8cd+SFkVtO3Kq+kDIHMEhYirOnXq2Jdffmlbt251B271Mnr22WfboYcealWrVnXT/Pnnn+51j543bdrU/a1p1q5dm65aqnok9T6v//UZP++5N02kYsWKuUcknazyInjLq/kmIsqK8mLbij/2Q8qqoGxXnFuB2NDRDPIF9SqqwE9t/D7//HPXsYyqfCpomzhxYmg6BY5qK6hqp6L/N2zYEJZZnDRpkqWlpbm2h9406pHU365AdyTr1q0bteooAAAAkEwIChFXCgDVe6g6lVGgduKJJ1q9evXskksusZSUFDeG4d13320ff/yxG7LioosusurVq1uPHj3c59UWsUuXLnbZZZfZzJkz7ZtvvrFrrrnGdUKj6eS8885zncxo/EINXfH222/bsGHDwqqHAgAAAMmK6qOIq40bN7r2e3/88YfrHKZnz552zz33hKp7DBw40FUt1biDyghqyAkFkf4eSzXkhALB9u3bu15HNQ+NbejvsVQdxPTr18+aN29ulSpVssGDB4eNZQgAAAAkK4JCxNVZZ53lHhlRtnDo0KHukREFk2+88Uam36MxDr/66qt9WlYAAAAgEVF9FAAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAAAACQxAgKAQAAACCJERQCAAAAQBIjKAQAAACAJEZQCAAAAABJjKAQAAAAAJIYQSEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoBAAAAIIkRFAIAAABAEiMoBAAAAIAkRlAIAAAAAEmMoBAAAAAAkhhBIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAAAgiREUAgAAAEASIygEAAAAgCRGUAgAAAAASYygEAAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAAAACQxAgKAQAAACCJERQCAAAAQBIjKAQAAACAJEZQCAAAAABJjKAQAAAAAJIYQSEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoBAAAAIIkRFAIAAABAEiMoBAAAAIAkRlAIAAAAAEmMoBAAAAAAkhhBIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAAAgiREUAgAAAEASIygEAAAAgCRGUAgAAAAASYygEAAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAAAACQxAgKkalNmzbZ6NGjbfHixZQUAAAAkIAIChHmrLPOsqeeesr9vX37dmvRooV7rXHjxvb+++9TWgAAAECCIShEmKlTp1qbNm3c3x9++KEFAgHbsGGDPfHEE3b33XdTWgAAAECCIShEmI0bN1qFChXc35999pn17NnTSpYsaaeccootXbqU0gIAAAASDEEhwtSsWdOmT59uW7dudUFhp06d3Ovr16+34sWLU1oAAABAgkmN9wIgf+nfv7+df/75Vrp0aatVq5a1a9cuVK20UaNG8V48AAAAALmMTCHCXH311S5T+OKLL9rXX39thQoFN5FDDz0019sU7t271/773/9a7dq1rUSJElanTh276667XDtGj/4ePHiwVatWzU3ToUOHdNVY161b5wLZMmXKWLly5axv3762ZcuWsGnmz5/v2koq26ls6IMPPsgvDwAAAJApRDTqcVQPL3BbsGCBtWrVysqXL5+rBfbAAw/Ys88+ay+//LI1bNjQZs+ebZdccomVLVvWrrvuOjeNgjd1cqNpFDwqiOzcubP98MMPoeqsCghXr15tEyZMsN27d7t5XH755fbGG2+EhtVQNVgFlMOHD3fr06dPHxdAajoAAAAgmZEpRLrqoy+88EIoIDzhhBPsqKOOctm1KVOm5GppTZs2zbp37+46sTnkkEPszDPPdMHbzJkzQ1nCxx9/3G6//XY3nYbFeOWVV2zVqlVu7ETR+Ilq+zhy5Eg79thjrXXr1vbkk0/aW2+95aaT119/3Xbt2uWynwo+zznnHBd0Pvroo/z6AAAASHq0KUSY9957zy644AL395gxY2zZsmX2448/2quvvmq33XabffPNN7lWYso+jhgxwn766Sc74ogj7Pvvv3dVVr1gTd+9Zs0al+HzKIuo4E9VXBXc6X9l/LzMpmh6VXv99ttv7fTTT3fTtG3b1ooWLRqaRtlGZSrVgU60DOjOnTvdw6NsoygTqUdu8eaVm/NMVJQV5cW2FX/sh5RVQduuOL8CsSEoRJi///7bqlat6v4eN26c9erVywVsqm45bNiwXC2tW2+91QVb9erVs8KFC7vM5D333OOqg4oCQqlSpUrY5/Tce0//V65cOXyjTk11w2r4p1HV08h5eO9FCwrvu+8+u/POO9O9Pn78eDdER25T1VdQVnmBbYuyYruKL/bB+JbVtm3bcn2eQCIiKES6YEnt9dSxi6plqs2fd1BV4Jab3nnnHVe1U23/VK1z3rx5rvpq9erVrXfv3nH9ZQYNGmQ33nhj6LmCV1WhVfVWdWiTm3cwdRLs2LGjFSlSJNfmm4goK8qLbSv+2A8pq4K2XXk1fQBkjqAQYdRJy1lnneWCwpSUlFDVTVXFVEYvNw0YMMBlC1UNVDTkxfLly12WTkGhl7H8888/3fJ49Lxp06bub02zdu3asPnu2bPH9UjqfV7/6zN+3nNvmkjFihVzj0g6WeVF8JZX801ElBXlxbYVf+yHlFVB2a44twKxoaMZhLnjjjtcpy3qlVPtB73ASFlCBXC5SdlHb8gLj74nLS3N/a0qnwraJk6cGHbHTwFqy5Yt3XP9v2HDBpszZ05omkmTJrl5qO2hN43GWfS3K9Adybp16+Z6j6oAAABAQUOmEOmoF1DZsWNH6LW8qM7ZrVs314bw4IMPdtVH586d6zqZUftFUaZS1Uk1PuLhhx8eGpJC1Ut79Ojhpqlfv7516dLFLrvsMjfchAK/a665xmUfNZ2cd955rn2gxi+85ZZbbOHCha595GOPPcavDwAAgKRHphBh1NmLBpA/6KCDrHTp0vbrr7+61xWMeUNV5BYNHaEA9Oqrr3bB3c0332xXXHGF+37PwIED7dprr3WZy6OPPtoNSq+2jt4YhaJ2iara2r59e+vatasblkK9mvp7LFUHMerNtHnz5nbTTTfZ4MGDGaMQAAAAIFOISMrcaaB4DRqv7JvnyCOPdGMGKtuWWw444AA3Tz0yomzh0KFD3SMj6mnUG6g+Ixrj8Kuvvtqn5QUAAAASEZlChNHg8MqyaVgIf2+jTZo0ceMVAgAAAEgsBIUIs3LlSjvssMPSlYo6bmEAWAAAACDxEBQiTIMGDaJWs3zvvfesWbNmlBYAAACQYOh9FGHUAYt6GlXGUNnBDz74wJYsWeKqlY4dO5bSAgAAABIMmUKE6d69u40ZM8a++OILK1WqlAsSFy9e7F7r2LEjpQUAAAAkGDKFSKdNmzZucHcAAAAAiY+gEFHt2rXL1q5d66qQ+mmgeQAAAACJg6AQYZYuXWp9+vSxadOmhb0eCATcmIEa3B4AAABA4iAoRJiLL77YUlNTXacy1apVc4EgAAAAgMRFUIgw8+bNszlz5li9evUoGQAAACAJ0Pso0o1T+Pfff1MqAAAAQJIgKESYBx54wAYOHGhTpkyxf/75xzZt2hT2AAAAAJBYqD6KMB06dHD/t2/fPux1OpoBAAAAEhNBIcJMnjyZEgEAAACSCEEhwtSuXdtq1qyZrtdRZQpXrFhBaQEAAAAJhjaFSBcU/vXXX+lKZd26de49AAAAAImFoBBR2w5G2rJlixUvXpzSAgAAABIM1Ufh3Hjjje5/BYT//e9/rWTJkqGS2bt3r3377bfWtGlTSgsAAABIMASFcObOnRvKFC5YsMCKFi0aKhn93aRJE7v55pspLQAAACDBEBQirNfRSy65xIYNG2ZlypShZAAAAIAkQFCIMC+99BIlAgAAACQRgkLYGWecYaNGjXLZQf2dmQ8++IASAwAAABIIQSGsbNmyoR5H9TcAAACA5EFQCFdldNKkSda2bVuqjwIAAABJhnEK4XTs2NENUO857rjjbOXKlZQOAAAAkOAIChEaisJv0aJFtnPnTkoHAAAASHAEhQAAAACQxAgK4aijGa+zmWjPAQAAACQmOppBqPpo+/btLTU1uEls27bNunXrZkWLFg0roe+++44SAwAAABIIQSGcIUOGhJVE9+7dKRkAAAAgCRAUImpQCAAAACA50KYQAAAAAJIYQSEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoR5pVXXrGdO3emK5Vdu3a59wAAAAAkFoJChLnkkkts48aN6Upl8+bN7j0AAAAAiYWgEOkGsU9JSUlXKn/88YeVLVuW0gIAAAASDOMUwmnWrJkLBvVo3769pab+u2ns3bvXli1bZl26dKG0AAAAgARDUAinR48e7v958+ZZ586drXTp0qGSKVq0qB1yyCHWs2dPSgsAAABIMASFcIYMGeL+V/B3zjnnWLFixSgZAAAAIAnQphBhTjrpJPvrr79Cz2fOnGn9+/e3ESNGUFIAAABAAiIoRJjzzjvPJk+e7P5es2aNdejQwQWGt912mw0dOpTSAgAAABIMQSHCLFy40I455hj39zvvvGONGjWyadOm2euvv26jRo2itAAAAIAEQ1CIMLt37w61J/ziiy/stNNOc3/Xq1fPVq9eTWkBAAAACYagEGEaNmxow4cPt6+++somTJgQGoZi1apVVrFiRUoLAAAASDAEhQjzwAMP2HPPPWft2rWzc88915o0aeJe//jjj0PVSgEAAAAkDoakQBgFg3///bdt2rTJypcvH3r98ssvt5IlS1JaAAAAQIIhU4h0AoGAzZkzx2UMN2/eHBrAnqAQAAAASDxkChFm+fLlrh3h77//bjt37rSOHTvaAQcc4KqV6rnaGwIAAABIHGQKEeb666+3Fi1a2Pr1661EiRKh108//XSbOHEipQUAAAAkGDKFCKNeRzUuoaqL+h1yyCG2cuVKSgsAAABIMGQKESYtLc327t2brlT++OMPV40UAAAAQGIhKESYTp062eOPPx56npKSYlu2bLEhQ4ZY165dKS0AAAAgwVB9FGEeeeQR69y5szVo0MB27Nhh5513ni1dutQqVapkb775JqUFAAAAJBiCQoSpUaOGff/99/b222+7/5Ul7Nu3r51//vlhHc8AAAAASAwEhUi/UaSmuiBQDwAAAACJjTaFCPPPP/+E/l6xYoUNHjzYBgwYYFOnTs31klKPpmqzGPno16+fe1/VV/V3xYoVrXTp0tazZ0/7888/w+ah8RRPOeUUK1mypFWuXNkt6549e8KmmTJlih111FFWrFgxO+yww2zUqFH86gAAAMD/ERTCWbBggQvSFFjVq1fP5s2bZ0cffbQ99thjNmLECDvppJNs9OjRuVpas2bNstWrV4ceEyZMcK/36tXL/X/DDTfYmDFj7N1337Uvv/zSVq1aZWeccUbo8+olVQHhrl273DAaL7/8sgv4FMh6li1b5qY58cQT3Tr179/fLr30Uvv888/55QEAAACCQngGDhxojRo1chnBdu3a2amnnuqCqY0bN7qB7K+44gq7//77c7XADjzwQKtatWroMXbsWKtTp46dcMIJ7ntfeOEFe/TRR11A2rx5c3vppZdc8Ddjxgz3+fHjx9sPP/xgr732mjVt2tROPvlku+uuu+zpp592gaIMHz7cateu7TrQqV+/vl1zzTV25plnumAXAAAAAJlC+LJ299xzjx1//PH28MMPu6zc1VdfbYUKFXKPa6+91n788cc8Ky8FcQru+vTp46qQzpkzx3bv3m0dOnQITaMM5sEHH2zTp093z/W/AtkqVaqEplHPqZs2bbJFixaFpvHPw5vGmwcAAACQ7OhoBs66detctk7Ufq9UqVJWvnz5UOno782bN+dZaalq6oYNG+ziiy92z9esWWNFixa1cuXKhU2nAFDvedP4A0Lvfe+9zKZR4Lh9+/YMe1TduXOne3g0vShQ1SO3ePPKzXkmKsqK8mLbij/2Q8qqoG1XnF+B2BAUIkQZusye5yVVFVX1z+rVq+eLX+S+++6zO++8M93rqrKqTm1ym9eeEpQV21b8sB9SVmxXibcPbtu2LdfnCSQigkKEKEunHjq9nj+vvPJKlzEUf9Ysty1fvty++OIL++CDD0KvKWupKqXKHvqzhep91Mto6v+ZM2eGzcvrndQ/TWSPpXpepkyZTMddHDRokN14441hmcKaNWtap06d3Gdz8w6mToIdO3a0IkWK5Np8ExFlRXmxbcUf+yFlVdC2K6+mD4DMERTC6d27d1hJXHDBBelK5qKLLsqT0lIHMur1VB3beNSxjE4MEydOdENRyJIlS9wQFC1btnTP9b/aQa5du9Z9XnRSUdDWoEGD0DTjxo0L+z5N480jIwqOvQDZT8uUF8FbXs03EVFWlBfbVvyxH1JWBWW74twKxIagEKHALB7S0tLcdysoTU39d3MsW7as9e3b12XrKlSo4AI9dXajYO64445z0yhrp+DvwgsvtAcffNC1H7z99tvd2IZeQKds51NPPeV6V1UnNpMmTbJ33nnHPvnkE355AAAAgKAQ8aZqo8r+KWCLpGEj1POpMoWqvqpeQ5955pnQ+4ULF3bDWFx11VUuWFRVVwWXQ4cODU2j4SgUAGrMw2HDhlmNGjVs5MiRbl4AAAAAyBQizpTtCwQCUd8rXry4G3NQj4zUqlUrXfXQSBp3ce7cufu8rAAAAEAiKhTvBQAAAAAAxA9BIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAAAgiREUAgAAAEASIygEAAAAgCSWGu8FAAAAwP6Rlma2YoVZ4cJmFSuaFStmVihKikBDCC9dapaSYlanTvRpACQOgkIAAIACZvNms/Hjzf76K/j3woXBAO6oo8wOOsjs11/NPv3UbNkys1atzNq1M6ta1eyee8y+/fbf+SjYO/304DQzZpht325Wq5bZRx+Z/fFHcJqyZc369TMrVcrsvfeC37dnj1mXLmannBKcvnRps9Wrg/PT3+vXB/8uV87siCPMihTJfH3Wry+WtwUGIFMEhQAAAPncb7+Z3Xuv2e+/B4OtyZPNduxIP93LL0f/7BtvZJw5fP/94COSsogKNDduDH53pOHDg4+sKEhs3drs+OPNSpY0++kns2++CQabClT//ruwTZvW0U46Kc0OPjjr+QHIfQSFAAAA+cDu3WZTppjNnWu2ZEmw2qaycj/8EAwC9bffYYeZNWpkVry4Wf36wQDvu+/M/vzTrHr14HsNG5otWhT8vLJ3yvb17292xhnB4Oyff8y++MLs++/N6tULzuOXX8y6djW74IJghk9Zw5EjzdauDWYVTzzRbM0as9GjzebNC76+aVOwyqkeqanBwFXLpczjli1mn30WfERXyAoVCtjkyQHr3Xs/FDSAdAgKAQBAwtm71+zzz80+/jiYnerePZitUrCiLJXay82cGax2qaqQzZubHXJIMHulv/Xatm1mjRsHM3Jbt5rVqGFWokRw/gqE9B3VqoV/r4IqBXa//ppi//xT3mW+VIVSGbcxY8x27QoGWkceGQzGFi82q1w5mI2bOjVYBTMjLVqYXXhhcB20Lk2aBOcbizvvTP+alkFOOCHzz/bsGXxEe92jYNDPWy6Vx4IFwTJRwKoyO/DAYNZQFMBu3rzXypWbbOedl8WCAMgzBIUAACQYBTGrVgU7ElGGSAHMMceYVaoUDIZUnU+ZJV2gK4tzwAHBR7QAQxkgBUfqmETBze7dKfbFFymhoEKBkubx9tvBDJHajylDpfnlNQUUqvaogGPdumA7Oi2v1lVVJhVweR57LLjOaicXmXETtafLStGiwQycgjK11VMgpOqP5cubHXposLqlMm7KtAUvsdrarbdmb51Uhi1bmrVtG/ytFEQqaDv8cLOOHYPLkB9lFJyqrBS86pGR3bvTbNy4rXm2bACyRlAIAEABo2BE1f7Gjg12NqIsloIwVdX78Uez5cuDr2V1Ee/P7iiAVOCh4EaZKwVWymy9+24waybNmqXar792so0b/718UECk4FLBmJ/mp4CgWbNgoFimTDDAUcCqaowbNgQ/q0xazZrBgE1ZOb3m0TooMFK1RgWAql6pLJMCvOefD2bVIjNUog5WRNUXzz03mIXzsnIefZeqX6oapYJmtdVTuWk95swJZhdFwbUCYs1Ly6Ng01+Gqkaph3/e+uyRR6bZb79tt7S0krZ1a4r7bapUCQZ8KgdlKFU9tFOn4O+mMlQmUBk0rSMA7E8EhQAAZEAX75llZpQhU7CgLv4VsKhqoTI9yh4psJg+3WzWrGDAoIBAwZHmp6BLWTcFIQrCNA9lsFS9TtURFTgpYNHros/ota++Cma0NK0XqGV4gk8NTqOeKBVUqaqkMmTqKVLzUpAlCtxUxU9Bph6ZZczmzlU6qLiVLBmwQw5Jce3eFOCJ1knt19RObeXK4LxEQasesVJApbLSMino02+QGQWvqhqqjGXt2sHXFHBpHdUurm7d4GtaTvWcqWXR9GovFwvNR+WvAFCZSK2XXtP36TfUb6ugTu/pf5W3evI88MC9Nm7cF9a1a1crUqSICzYzysYCQLwRFAIAEpaySO+8E3woG6RqkwrMFHQoI6UOOLZtK2xbt9azwoVTXBCi7Js6+lCQpwyQqlw2aBAMeBTAKeBTlmnoULMPPggGX5o2Fp98krvrpyp5p50WDFCUDdOyaDm1fqrCqcBPwZoyXVpGZee8LJQCWWWnFCSqeqjafek1BXXKzql6pIKsa68NZvsUiH799R6bNm2BDRhwpNWqVcQFR+oERYGb2uF5ww5oWlWjVJCszJr+VlCmstRrFSoEM4eq8qnv1DQK5BQka1n03R7NU8vuzVt/K6s4cKBZ587p2/RJmzbpX9PvfdllwUd2eIG5KLOnh5/XNi5S5Dah9QWA/IqgEABQIII7BTAKeiIpGzZhgtn8+cFsjS6+lf2aPTuYxYnsuCN9D4galbuuCxyj+fvvYAcgemR18a/ARcupLJeWVW3AlMlSgKbx5JRZ03sKepS1U7VJBVBaP31WY8wp26Rskve6eFUz1bmIAh6vOmZWgYY/YNL8/dUS9d0e9Uh53HHBR0ZUzbNXr4CVKvW7Va9+ZChg0jJHUpCqR3YpsFU5eePdKaBVxlSvKaD3qs1q2cm4AUDuISgEAORLCujUnk0BlNqDKVBQm7edO4NBlrJLyuQp25RZVUpV/bv66mCVPmULFTgq4FLwpaqFpUvvtalTV9rs2TWtSJEUu/56s5NOCgZ36lREgYqyYXooq6VORNQNv7Jxatem/xWkKAOmwEvLpx4vlU1UhhGxU8+feigQ9FNw6A9gAQC5i6AQAJBvKBOk4GvYsGDAFUmdqIi/eqGoWmK7dsG2cwr29FDVSgVm6iFTWabMej5s2nSunXRSNdf2y19d0KOqkdlpE3fOObFPDwBAvBEUAgD2K1Wf9MZ9UzZO2bfXXw9m8dQxi793x27dgtUTFZSprZyqiCorqB4h1aGHMofq7EPVJL02ZzmlYHBf5wEAQEFEUAgAyDUK2NRuTp2WaMw2dXSirJ86GVEGTb1SZtWbpD6n6ps33WTWvn34e+roBQAA5C6CQgBAjijD9/LLwXZ96sFS7exGjw72Hhkrb6y8M88M9nCpqp4KBGk3BgDA/kNQCACIiYI3b6gBDWr+2mvRA0CNxaYOVtS7pjoIUY+WHToEq2YqeFTGUFU1vcHBNQ99BgAAxAdBIQAg00BQVT7Vlu/++4ODp/upR08NkaBx/RT03Xpr8DUNJxArAkIAAOKLoBAAEAoAR44MtgPU2Hwaz0/DN0QOwn3IIWannGLWvXswA8h4cQAAFGwEhQCQRDQI+pYtwb/V+YsyfKoOqiEcVDX0o4/Sf6ZwYbM6dcxOPNFs0KBgFVAAAJA4CAoBIEFt2ZLqxvU78ECzWbPMPv/c7LnnMh/oXWrUCHb8okDw5JPNDj6YoRoAAEhkBIUAUEApuPv5Z7MvvzT7/ffgQ9U9d+40q1KlsE2Y0MV27y4c9bPK/unRqFEw86ehHv75J9gpzGWXUSUUAIBkQlAIAAWEgr2pU4NVPN991+yvv4LtAKML9vRSokTAtm9Psbp1zRo3DmYAzzprfy41AADI7wgKASCfSUsLBnyq8qmB4GfMMFu+3FxVUA3z4Jeaata2rVm9esFqnsr6qRfQn3/ea4UKfWPXX9/Sdu0q4sb9o0MYAAAQDUEhAOxn6uhFHbzIggXBIFAZv2nTzL75xmz16ozb/VWrZtakidkFF5h17GhWsWKwGmik3bvTbNy49S4QLF06b9cHAAAUbASFALAfqGdPZfs0zt+996bP+EWj7J8GgT/mGLP69YOZQFUBzc4YgAAAAFkhKASAPLJundknnwQHfn/kkfD2f1WqBAdtr13brEyZYHvBo48OVgU9/HCzypXp8RMAAOwfBIUAkEt27DBbtcps8uRghzAffhieESxRIhj0detmduWV0at9AgAA7G8EhQCwj774wmzIkGDHMLt3h7932GHBKqBnnGF20UV09gIAAPIfgkIAiIGqfs6caTZ6dLBaqKp/qodQVQ1VBzGhg2pqcOy/U04Jjvmnwd9pAwgAAPIzgkIAyIQ6hnnpJbPPPw9WDY1G1UA14PuAAcE2ggz9AAAAChKCQgCIsG2b2ccfm73wQrBqqKdkSbMePcyqVg0OK6H/1SNop05mNWtSjAAAoGAiKAQAM9u1y2zECLOnngoOGO/PAvbubXb++WbHHmtuEHgAAIBEQlAIwJK9x9D33ze74w6zn3/+93VlAC++OBgMHnFEPJcQAAAgbxEUAkhKf/9t9sQTZqNGma1YEXxNnccMHmx25plmBx5I20AAAJAcCAoBJF0vop99FhweQoGhVK8eHDewf//ggPIAAADJhKAQQFJ44w2zZ58NDiGxaVPwNQ0doR5DlRnUwPIAAADJiKAQQEJbuNDsllvMxo3797VixYKZwXvvDfYoCgAAkMwICgEkpLlzza65Jnxg+euuM7v0UrO6dc2KFo3n0gEAAOQfBIUAEsqaNcGqonfdZbZhg1mhQmann252zz3BYBAAAADhCAoBJIS0NLPnngtWFd28Ofja0UebjR4d7EgGAAAA0REUAijwfvzR7KqrzKZMCT5v2jRYTbRPHzqQAQAAyApBIYACaetWs08+MXv8cbPp04OvlSoV7DymXz+zwoXjvYQAAAAFA0EhgAJjz54Ue++9FBs/3uztt822bw++npISzA6+8orZkUfGeykBAAAKFoJCAAXCihVqL9jWfvnl38NW7dpm550X7GW0atW4Lh4AAECBRVAIIN978UWz//wn1f78s5yVLx+wiy5KsZ49zVq3DmYJAQAAkHMEhQDyrZ9/Dg4lMWqUnqVYrVobbcKEknb44UXivWgAAAAJg6AQQL4zZ47ZgAHB3kQDgeBrAwbstRYtptohh3SJ9+IBAAAklELxXgAA8OzaZTZsmFmrVmaTJwcDwi5dzD7+2Ozuu9OsWLE0CgsAACCXkSkEkC98/725doK//BJ83qNHcLiJWrWCz3fvjuviAQAAJCwyhQDiau9edSITHFJCAWGVKmbPPmv2/vv/BoQAAADIO2QKAcTFtm1mL71k9vDDZr/9Fnzt1FPNXnjBrHJlfhQAAID9hUwh4mrlypV2wQUXWMWKFa1EiRLWqFEjmz17duj9QCBggwcPtmrVqrn3O3ToYEuXLg2bx7p16+z888+3MmXKWLly5axv3762ZcuWsGnmz59vbdq0seLFi1vNmjXtwQcf3G/riH9psHkNMK/gr1y54PiCCgjLlzcbOdJszBgCQgAAgP2NoBBxs379ejv++OOtSJEi9umnn9oPP/xgjzzyiJVXhPB/Ct6eeOIJGz58uH377bdWqlQp69y5s+3YsSM0jQLCRYsW2YQJE2zs2LE2depUu/zyy0Pvb9q0yTp16mS1atWyOXPm2EMPPWR33HGHjRgxYr+vc7L3KHrEEWa9e5t98kmwjWC1asGOZVauNOvbN95LCAAAkJyoPoq4eeCBB1zW7iXVIfy/2rVrh2UJH3/8cbv99tute/fu7rVXXnnFqlSpYqNHj7ZzzjnHFi9ebJ999pnNmjXLWrRo4aZ58sknrWvXrvbwww9b9erV7fXXX7ddu3bZiy++aEWLFrWGDRvavHnz7NFHHw0LHpE31IPoa68Fs4KbNpnVrBkMAHv1MmvQgFIHAACINzKFiJuPP/7YBXK9evWyypUrW7Nmzez5558Pvb9s2TJbs2aNqzLqKVu2rB177LE2ffp091z/q8qoFxCKpi9UqJDLLHrTtG3b1gWEHmUblyxZ4rKVyNuA8PrrzS66KBgQtm1rtnCh2ZAhBIQAAAD5BZlCxM2vv/5qzz77rN144432n//8x2X7rrvuOhe89e7d2wWEosygn5577+l/BZR+qampVqFChbBp/BlI/zz1nr+6qmfnzp3u4a+CKrt373aP3OLNKzfnmZ8MHlzInnyysKWkBGzIkDQbODDNUlNzNrxEopdVbqO8KCu2K/bBgiIvj1ecM4DYEBQibtLS0lyG795773XPlSlcuHChaz+ooDCe7rvvPrvzzjvTvT5+/HgrWbJkrn+f2kMmkk2bitrbb9e1Tz451D2/4or51rTpbzZ+/L7PO9HKKq9RXpQV2xX7YDIfr7apq2sAWSIoRNyoR9EGEY3K6tevb+9rgDozq1q1qvv/zz//dNN69LypBrX7/zRr164Nm8eePXtcj6Te5/W/PuPnPfemiTRo0CCXwfRnCtX+UR3WqJfT3LyDqZNgx44dXYc7iWDs2BQbMKCw/flnint+11177ZZb9DvvWwPCRCyrvER5UVZsV+yDBUVeHq+8mj4AMkdQiLhRz6Nq1+f3008/uV5CRVU+FbRNnDgxFATq4K62gldddZV73rJlS9uwYYPrVbR58+butUmTJrkspNoeetPcdttt7qTjnWx08qlbt27UqqNSrFgx94ikz+dFQJJX892f5s0zu+MOs48+Cj5XvK/nZ56p6qOFc+17EqGs9ifKi7Jiu2IfTObjFecLIDZ0NIO4ueGGG2zGjBmu+ujPP/9sb7zxhhsmol+/fu79lJQU69+/v919992uU5oFCxbYRRdd5HoU7dGjRyiz2KVLF7vsssts5syZ9s0339g111zjeibVdHLeeee5dooav1BDV7z99ts2bNiwsEwg9m3sQcXozZoFA0K1GRw4MDgEhXoYTQkmDAEAAJBPkSlE3Bx99NH24YcfuqqaQ4cOdZlBDUGhcQc9AwcOtK1bt7qhI5QRbN26tRuCQoPQezTkhALB9u3bu15He/bs6cY29PdYqraACjaVTaxUqZINHjyY4ShywebN6u3VbObM4HMFgcoOMtQEAABAwUFQiLg69dRT3SMjyhYqYNQjI+ppVFnGzDRu3Ni++uqrfVpWhPvtNzMlbL//Xr+B2ZtvmnXqRCkBAAAUNASFALJNTUGVIfzjD7MDDzT75BNlfilIAACAgoigEEC2fPed2cknm6nT1/r11WmP2UEHUYgAAAAFFR3NAIhJIGD2wgtmrVoFA0J1LPPllwSEAAAABR1BIYAsrVpldu65ZpdearZzp9kpp2joj2DVUQAAABRsVB8FkKnZs826dDH75x+zQoXM7rknOOSE/gYAAEDBR1AIIEOLF5t16xYMCFVd9PnnzZo3p8AAAAASCUEhgKiWLTNr1y7YfrBRI7MpU8zKlKGwAAAAEg0VwACko8xg167BgLBJE7PJkwkIAQAAEhVBIYB0Q060bm32449mNWqYjRtnVrEihQQAAJCoCAoBhIwcadayZTAg1NiD48ebVa9OAQEAACQygkIAtmWL2RVXmF12mdmuXWbduwczhhqcHgAAAImNjmaAJPfbb2YnnxzMDqakBIecuPXW4N8AAABIfASFQBKbNs3s1FPN1q8PVhcdNcqsQ4d4LxUAAAD2J4JCIEn9/vu/AWGLFmYffhjsWAYAAADJhaAQSNI2hL17/xsQfvmlWcmS8V4qAAAAxAMdzQBJZvdus9NOCw5GX7y42csvExACAAAkM4JCIIkEAmb9+gUHoy9d2uyLL8waNIj3UgEAACCeCAqBJDJsmNnzzwd7Fn3zTbPjj4/3EgEAACDeCAqBJDFunNlNNwX/fvjhYCczAAAAAEEhkAQWLjQ75xyztDSzSy81u+GGeC8RAAAA8guCQiDBLV9u1q2b2ebNZiecYPb00wxMDwAAgH8RFAIJ7Icfgu0Gf/vN7LDDzN5/36xo0XgvFQAAAPITgkIgQa1ZY9ali9nKlcEeRjUERcWK8V4qAAAA5DcMXg8kIFUV7d7dbMUKs7p1zaZOJSAEAABAdGQKgQSzY4dZp05mM2ealS9vNnYsASEAAAAyRlAIJJjrrjObMSMYEI4fH2xLCAAAAGSEoBBIIK+8Ej44fYsW8V4iAAAA5HcEhUCC+Okns6uvDv59xx1mnTvHe4kAAABQEBAUAglgzx6z888327rVrF07s9tui/cSAQAAoKAgKAQKuEDA7PbbzWbPNitXzuy118wKF473UgEAAKCgICgECrhhw8weeCD492OPmR10ULyXCAAAAAUJQSFQgC1f/m9V0XvvNbv44ngvEQAAAAoagkKgAFcbvfZas23bzNq0MbvllngvEQAAAAoigkKggBo92mzMGLMiRcyGDzcrxN4MAACAHOAyEiiANm8OZgll4ECzBg3ivUQAAAAoqAgKgQI4/MSFF5qtXGlWpw7DTwAAAGDfEBQCBawd4VVXmX30kVmxYmYvv2xWokS8lwoAAAAFGUEhUIACwltvNRs5Mth+8K23zI4/Pt5LBQAAgIIuNd4LACC2gHDAALNHHgk+V8cyPXpQcgCSVyAQsD179tjevXstP9q9e7elpqbajh078u0yJkJZFS5c2H02JSUlz5YPSAYEhUABcPPNZo8+Gvz7qafMLrss3ksEAPGza9cuW716tW3TmDz5OGitWrWqrVixgoAlj8uqZMmSVq1aNStatGhOfy4g6REUAvncO+/8GxA+95zZ5ZfHe4kAIH7S0tJs2bJlLkNUvXp1FwjkxyyRlnPLli1WunRpK8SYQXlSVgomdYPgr7/+ctvE4YcfTlkDOURQCORjy5f/GwTedhsBIQAoCFAQUbNmTZchyq+0jFrW4sWLE6jkYVmVKFHCihQpYsuXLw/NA0D20dEMkE+pWYWGnti40ezYY82GDIn3EgFA/kH2DWwLQO4hKATyqfvuM/vqK7MDDjB74w2zIkXivUQAAABIRASFQD40fbrZHXcE/376abNDD433EgEAksHFF19sPejeGkg6BIVAPrN7t1nfvsHqo+edZ3bBBfFeIgBAbgRb6hDHe1SsWNG6dOli8+fPz7XCveOOO6xp06YxTedfFu/xxRdf2LBhw2zUqFGhadu1a2f9+/fPtWUEkD8RFAL5zP33my1ebFapUnD4iXzYqR4AIAcUBGooDT0mTpzoxtc79dRT41KWDRs2DC2L92jbtq2VLVvWypUrF5dlAhA/BIVAPjJlitngwcG/H37YrHz5eC8RACC3FCtWzI3Hp4cyerfeeqsbm09DKnj0/KyzznKBWYUKFax79+7222+/hd6fMmWKHXPMMVaqVCk3zfHHH+963lR2784777Tvv/8+lPnzZ/wiKSD1lsV7aHgPf/VR/f3ll1+67KE3Ty2LlkF/K7Bt0aKF6wW2VatWtmTJkrDv+Oijj+yoo45yPYIeeuihbvn27NkTGk5CGcuDDz7Y9SBav359u/7660OffeaZZ9wQE/pslSpV7Mwzz2RDBPIQQ1IA+cTOnWZXXBH8+9JLzXr3jvcSAUD+FwiYxWsMe42IkdPaHBqX77XXXrPDDjvMVSWV3bt3W+fOna1ly5b21VdfucDt7rvvDlUzVY+rCtguu+wye/PNN90QDDNnznQB2tlnn20LFy60zz77zFUDFWX99oWCwZ9++smOPPJIGzp0qHvtwAMPDAWpt912mz3yyCPutSuvvNL69Olj33zzjXtPy3/RRRfZE088YW3atLFffvnFLv//GEtDhgyx999/3x577DF76623XECo93/++Wf3/uzZs+26666zV1991QWb69atc/MDkHcICoF84t57zX76yaxqVbOHHor30gBAwaCAsHTp+Hz3li1mpUrFPv3YsWPdAO2ydetWq1atmnvNG17j7bffdmP2jRw50gV68tJLL7mMoLJzyspt3LjRVTmtU6eOe18BlUfz9jKAWVmwYEFoWaRBgwYuwPRTUKnsoTKB0eZ5zz332AknnOD+VtbzlFNOsR07drjsnrKCeq33/+9wKlN411132cCBA11Q+Pvvv7t5dujQwQoXLuzW8cQTT3TT6j1lQrWeBxxwgNWqVcuaNWsWe0EDyDaqjwL5wNSpKXb33cG/hw0zozkHACQeBT3z5s1zDwVgygqefPLJrvqnqOqnsmUKhBSw6aEqpAq0lEnT36rSqc9169bNZfLUFjAn6tatG1oWPZS5y67GjRuH/laAK2vXrg2ti7KL3nrooQynlnfbtm3Wq1cv2759uwsWlUFUcOxVLe3YsaMLBPXehRdeaK+//rr7DIC8Q6YQiLMtW4rYVVcVtrS0YJXRs86K9xIBQMGhKpzK2MXru7ND2S9VF/UoI6hs3PPPP++qiapKafPmzV0QFElVNL3MoapWqpqoMou33367TZgwwY477rhsLYsygP5lyYkivgF0vcymMp2idVG28Iwzzkj3OWUSa9as6dogqqrr+PHj7eabb3btCNWGUUHxd99957Kjem/w4MGu/eGsWbPoBAfIIwSFQBytWWP22mv1bfXqFKtbNzgmIQAgdopFslOFMz9RIKWqo8qYiTplUaBXuXJlK1OmTIafU1VKPQYNGuTaH77xxhsuKFSgt1fjGeWinM5T66KgL7PAUx3MKOOpaqdqf6gOdFStVZ9VNVhVLdVD1U1VvXTSpElRg0wA+46gEIijCy8sbF9+Wdv9fdttBffCBgCQtZ07d9oa3Q00s/Xr19tTTz3lMmoKjOT888+3hx56yPU4qqqXNWrUcFVLP/jgA9cWTx3RjBgxwk477TSrXr26C7qWLl3qAio55JBDbNmyZa46qD6rjJt6PN0Xmue3337rOpfxqrPGQtk9tQlU76LqOVTBr6qUqjMcZUXVM6qCzWOPPdZlDt955x0XJKraqKqS/vrrr26IjPLly9u4ceNcBlJVXgHkDdoUAnFUtKgee61VqzSqjQJAglOVT7W900PBkKpDvvvuu26AeFGHLlOnTnWBlDJi6kSmb9++rk2hMod6/8cff7SePXvaEUcc4dri9evXz674f9fVel09lartoqqbqofSfaVqneoIRh3RaJ7qBCYWaveo4E7VP48++miXyVRvowr6RJk/VZvVkBoankPVRjWEhXpi1XsKhE866SRXBsOHD3frorEVAeSNlIAGigGQqU2bNrl2H+r1LbMqPdmlu766A9q1a9ewthmgrNi29h/2w4JVVgqQlA2rXbu2yzDlV8ps6dyhc4bXuyjypqwy2yby6vwNJBqOUgAAAACQxAgKAQAAACCJERQibtS9tHpe8z/q1asXVh1EbSXUvkCN29VW4s8//wybh9o2qNcytbNQb20DBgwIjXPkUZfW6slMje3VC5oatwMAAAAIIihEXKnRuAay9R5ff/116L0bbrjBxowZ4xrhqwH6qlWrwrqiVq9lCgh37dpl06ZNs5dfftkFfOrxzKM2BprGGzC4f//+dumll9rnn3++39cVAAAAyI8YkgLx3QBTU61q1arpXleD8BdeeMGNvaTex7wBe9UL2YwZM1wvZurR7IcffnAD31apUsX1XnbXXXfZLbfc4rKQGltJPZap4fkjjzzi5qHPK/BUD2jqGQ0AAABIdmQKEVcaX0ljLR166KFufCavq+s5c+a4Xu40aK1HVUvVTff06dPdc/3fqFEjFxB6FOipp7FFixaFpvHPw5vGmwcAoGCi83SwLQC5h0wh4kZjNKm6pwajVdXRO++809q0aeMGttXgvsr0aawiPwWA3sC/+t8fEHrve+9lNo0Cx+3bt7uBcjMaYFgPj6YXBap65BZvXrk5z0RFWVFebFvxl1/2QwWEGvR9Xwdm3x9Bq/7XkAvIu7LStuDNI3LbjPe2ChQUBIWIm5NPPjn0d+PGjV2QqEFt33nnnQyDtf3lvvvuc0FqJFVZVac2uW3ChAm5Ps9ERVlRXmxb8Rfv/fCAAw5wN+7UIZluIKqjsvzqn3/+ifciFBjZLSsFgupX4O+//7b169e72keRtm3blotLCCQugkLkG8oKHnHEEfbzzz9bx44d3YF+w4YNYdlC9T7qtUHU/zNnzgybh9c7qX+ayB5L9VwD2GYWeA4aNMhuvPHGsExhzZo1rVOnTrk+eL0urrS+DF5PWeUmti3KKpG3KwUDa9euDdXiyI+0jApaNZh6fg5aE6GsDjzwQNdxXbTP5udtBMhPCAqRb6j6xy+//GIXXnihNW/e3F1wTJw40Q1FIUuWLHFtDlu2bOme6/977rnHXRhoOArRxYqCtgYNGoSmGTduXNj3aBpvHhlRlaRo1ZK0THlxIZRX801ElBXlxbYVf/lhP6xRo4brhTq/Vg/Uck2dOtXatm0b97LK7/alrDR94cKFM30fQNYIChE3N998s3Xr1s1VGdVwE0OGDHEH9nPPPdfKli1rffv2ddm6ChUquEDv2muvdcGceh4VZe0U/CmIfPDBB137wdtvv92NbegFdFdeeaU99dRTNnDgQOvTp49NmjTJVU/95JNP+OUBoIDTOSOzgCCetFwaN1fZLwITygrI7wgKETd//PGHCwDVhkBVP1q3bu2Gm9DfomEjChUq5DKFajuiXkOfeeaZsBPu2LFj7aqrrnLBYqlSpax37942dOjQ0DQajkIBoMY8HDZsmLuzPHLkSIajAAAAAP6PoBBx89Zbb2X6vu6uPv300+6REWUZI6uHRmrXrp3NnTs3x8sJAAAAJDLGKQQAAACAJEamEIiBN/5Rbvdipsb16i5b86XNCWXFthUf7IeUFdtV4u6D3nnbO48DiI6gEIjB5s2b3f8algIAABS887g6sQMQXUqAWydAltLS0lwPqRowOTfHm/LGP1yxYkWujn+YiCgryottK/7YDymrgrZd6TJXAWH16tVd53UAoiNTCMRAJxL1XJpXdBIkKKSs2Lbii/2QsmK7Ssx9kAwhkDVumQAAAABAEiMoBAAAAIAkRlAIxFGxYsVsyJAh7n9QVmxb7If5HccsyortCkhMdDQDAAAAAEmMTCEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoBAAAAIIkRFAJx9PTTT9shhxxixYsXt2OPPdZmzpyZdL/H1KlTrVu3bla9enVLSUmx0aNHh70fCARs8ODBVq1aNStRooR16NDBli5dGjbNunXr7Pzzz3eDHpcrV8769u1rW7ZssURy33332dFHH20HHHCAVa5c2Xr06GFLliwJm2bHjh3Wr18/q1ixopUuXdp69uxpf/75Z9g0v//+u51yyilWsmRJN58BAwbYnj17LNE8++yz1rhx49Bg2C1btrRPP/009D5lFd3999/v9sP+/ftTVlHccccdrnz8j3r16lFWGVi5cqVdcMEF7pik43ejRo1s9uzZofc5vgP5B0EhECdvv/223XjjjW5Iiu+++86aNGlinTt3trVr1ybVb7J161a37gqQo3nwwQftiSeesOHDh9u3335rpUqVcuWki3qPAsJFixbZhAkTbOzYsS7QvPzyyy2RfPnlly7gmzFjhlvP3bt3W6dOnVz5eW644QYbM2aMvfvuu276VatW2RlnnBF6f+/evS4g3LVrl02bNs1efvllGzVqlAu6E02NGjVcgDNnzhx3EXrSSSdZ9+7d3XYilFV6s2bNsueee84F036UVbiGDRva6tWrQ4+vv/6asopi/fr1dvzxx1uRIkXcDZkffvjBHnnkEStfvnxoGo7vQD4SABAXxxxzTKBfv36h53v37g1Ur149cN999yXtL6JD0ocffhh6npaWFqhatWrgoYceCr22YcOGQLFixQJvvvmme/7DDz+4z82aNSs0zaeffhpISUkJrFy5MpCo1q5d69b7yy+/DJVLkSJFAu+++25omsWLF7tppk+f7p6PGzcuUKhQocCaNWtC0zz77LOBMmXKBHbu3BlIdOXLlw+MHDmSsopi8+bNgcMPPzwwYcKEwAknnBC4/vrr3etsV+GGDBkSaNKkSdTti7IKd8sttwRat26d4f7I8R3IX8gUAnGgTI0yGKoK6SlUqJB7Pn36dH6T/1u2bJmtWbMmrJzKli3rqtp65aT/VWW0RYsWoWk0vcpTmcVEtXHjRvd/hQoV3P/anpQ99JeVqrUdfPDBYWWl6ltVqlQJTaOs66ZNm0IZtESkDOlbb73lsqqqRkpZpacstLLI/u1HKKv0VH1d1d0PPfRQV0tBVbIpq/Q+/vhjd1zu1auXq6rerFkze/7550Pvc3wH8heCQiAO/v77b3eh6r84Fz1XEIQgrywyKyf9rwsOv9TUVBcsJWpZpqWluTZfqpp15JFHute0rkWLFnUBcmZlFa0svfcSzYIFC1zbymLFitmVV15pH374oTVo0ICyiqCAWVXY1W41EttVON2QUpXrzz77zLVbVWDTpk0b27x5M2UV4ddff3VldPjhh9vnn39uV111lV133XWu2rq3bQnHdyB/SI33AgAAsp/VWbhwYVhbJqRXt25dmzdvnsuqvvfee9a7d2/X1hL/WrFihV1//fWunao6vELmTj755NDfanupILFWrVr2zjvvuI5UEH7zSpnCe++91z1XplDHLbUP174IIH8hUwjEQaVKlaxw4cLpeobU86pVq/Kb/J9XFpmVk/6P7JxHvWmqR9JELMtrrrnGdaYzefJk15mKR+uqaskbNmzItKyilaX3XqJR5vSwww6z5s2buyyYOjQaNmwYZRVRPVT7z1FHHeUy7HoocFbnTvpbWRy2q4wpM3/EEUfYzz//zHYVQT1GKzPvV79+/VB1W47vQP5CUAjE6WJVF6oTJ04Mu6uq52rzhKDatWu7Cwd/Oan9m9oKeuWk/xUI6eLWM2nSJFeeuoufKNQPjwJCVYHU+qls/LQ9qZc/f1lpyApdgPnLSlUq/UG0MkQasiHy4i0RaZvYuXMnZeXTvn17t00oo+o9lN1RWznvb7arjGnom19++cUFQOyD4VS9PXLYnJ9++sllVoXjO5DPxLunGyBZvfXWW64XzVGjRrkeNC+//PJAuXLlwnqGTAbq9XDu3LnuoUPSo48+6v5evny5e//+++935fLRRx8F5s+fH+jevXugdu3age3bt4fm0aVLl0CzZs0C3377beDrr792vSiee+65gURy1VVXBcqWLRuYMmVKYPXq1aHHtm3bQtNceeWVgYMPPjgwadKkwOzZswMtW7Z0D8+ePXsCRx55ZKBTp06BefPmBT777LPAgQceGBg0aFAg0dx6662uZ9Zly5a57UbP1SPt+PHj3fuUVcb8vY9SVuFuuukmtw9qu/rmm28CHTp0CFSqVMn1BkxZhZs5c2YgNTU1cM899wSWLl0aeP311wMlS5YMvPbaa6FpOL4D+QdBIRBHTz75pLuIL1q0qBuiYsaMGUn3e0yePNkFg5GP3r17h7ot/+9//xuoUqWKC6Lbt28fWLJkSdg8/vnnHxcEli5d2g2vcMkll7hgM5FEKyM9XnrppdA0CpSvvvpqN/SCLr5OP/10Fzj6/fbbb4GTTz45UKJECXcxq4vc3bt3BxJNnz59ArVq1XL7lgJfbTdeQCiUVexBIWX1r7PPPjtQrVo1t10ddNBB7vnPP/9MWWVgzJgx7kaUjt316tULjBgxIux9ju9A/pGif+KdrQQAAAAAxAdtCgEAAAAgiREUAgAAAEASIygEAAAAgCRGUAgAAAAASYygEAAAAACSGEEhAAAAACQxgkIAAAAASGIEhQAA5JJRo0ZZuXLlKE8AQIFCUAgA2O/WrFlj119/vR122GFWvHhxq1Klih1//PH27LPP2rZt2wrEL3LIIYfY448/Hvba2WefbT/99FPclgkAgJxIzdGnAADIoV9//dUFgMqo3XvvvdaoUSMrVqyYLViwwEaMGGEHHXSQnXbaaXEp30AgYHv37rXU1JydHkuUKOEeAAAUJGQKAQD71dVXX+2CrtmzZ9tZZ51l9evXt0MPPdS6d+9un3zyiXXr1s1Nt2HDBrv00kvtwAMPtDJlythJJ51k33//fWg+d9xxhzVt2tReffVVl7UrW7asnXPOObZ58+bQNGlpaXbfffdZ7dq1XbDWpEkTe++990LvT5kyxVJSUuzTTz+15s2bu+D066+/tl9++cUtjzKYpUuXtqOPPtq++OKL0OfatWtny5cvtxtuuMF9Xo+Mqo8q+1mnTh0rWrSo1a1b1y2vnz47cuRIO/30061kyZJ2+OGH28cff5wHJQ8AQHQEhQCA/eaff/6x8ePHW79+/axUqVJRp/ECrF69etnatWtdwDZnzhw76qijrH379rZu3brQtAreRo8ebWPHjnWPL7/80u6///7Q+woIX3nlFRs+fLgtWrTIBXEXXHCBm87v1ltvdZ9bvHixNW7c2LZs2WJdu3a1iRMn2ty5c61Lly4uWP3999/d9B988IHVqFHDhg4daqtXr3aPaD788ENXTfamm26yhQsX2hVXXGGXXHKJTZ48OWy6O++80wXI8+fPd997/vnnh60nAAB5KgAAwH4yY8aMgE49H3zwQdjrFStWDJQqVco9Bg4cGPjqq68CZcqUCezYsSNsujp16gSee+459/eQIUMCJUuWDGzatCn0/oABAwLHHnus+1uf1fvTpk0Lm0ffvn0D5557rvt78uTJbnlGjx6d5bI3bNgw8OSTT4ae16pVK/DYY4+FTfPSSy8FypYtG3reqlWrwGWXXRY2Ta9evQJdu3YNPdf333777aHnW7Zsca99+umnWS4TAAC5gTaFAIC4mzlzpqvqqQzZzp07XTVRZesqVqwYNt327dtddtCjaqMHHHBA6Hm1atVcdlF+/vln12lNx44dw+axa9cua9asWdhrLVq0CHuu71b1VFVnVRZwz5497ru9TGGslHm8/PLLw15Te8phw4aFvabspEcZVFWX9dYDAIC8RlAIANhv1NuoqocuWbIk7HW1KRSvkxYFZQrw1OYvkr/NXpEiRcLe07wVXHrzEAV26rzGT20H/SKrst588802YcIEe/jhh90ya7nOPPNMF1DmhczWAwCAvEZQCADYb5T5U+buqaeesmuvvTbDdoVqP6hhK9QhjbKBOdGgQQMX/Cm7d8IJJ2Trs998841dfPHFrvMXL8D87bffwqZRxzHqqTQz6kRH8+rdu3fYvLVsAADkFwSFAID96plnnnFVKFVlU1U0VXWyUKFCNmvWLPvxxx9dL6AdOnSwli1bWo8ePezBBx+0I444wlatWuWyfgrUIqt7RqNqpcr4qXMZZd1at25tGzdudEGZqmf6A7VI6gFUncmocxll7f773/+my9wpWJ06darr8VTBZ6VKldLNZ8CAAa4DGVVX1TqNGTPGzdffkykAAPFGUAgA2K80PIN69NQYhYMGDbI//vjDBVXKnimI05AVCsTGjRtnt912m+ut86+//rKqVata27Zt3TARsbrrrrvckBbqhVTjI6rqqbKQ//nPfzL93KOPPmp9+vSxVq1auWDvlltusU2bNoVNo55H1Zuo1kftIIN9xoRTUKv2g6qGql5INTTGSy+95Ia0AAAgv0hRbzPxXggAAAAAQHwwTiEAAAAAJDGCQgAAAABIYgSFAAAAAJDECAoBAAAAIIkRFAIAAABAEiMoBAAAAIAkRlAIAAAAAEmMoBAAAAAAkhhBIQAAAAAkMYJCAAAAAEhiBIUAAAAAkMQICgEAAADAktf/ABixWuqaHt1MAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 600\n",
      "Population size: 300\n",
      "Best chromosome: 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\n",
      "Best fitness: 6964\n",
      "Total cost = population size * number of generations: 180000\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 600\n",
      "Population size: 500\n",
      "Best chromosome: 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\n",
      "Best fitness: 7091\n",
      "Total cost = population size * number of generations: 300000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 600\n",
      "Population size: 2000\n",
      "Best chromosome: 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\n",
      "Best fitness: 7348\n",
      "Total cost = population size * number of generations: 1200000\n"
     ]
    },
    {
     "data": {
      "image/png": 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bTe/tyx5lmMKPH+F3pnWnX9t3ZL19r11SeHY/q3+z9NZPx7PI6qvajrQPhLfZTK/aauSxQNkxTasMQ7jzzjvPZTzD1yvWdc3sNhWNd8dZd3eVNfF4NS10rM7ssUp30cMpQ6H39Z2syDxG2z6vu+46d9c7/NyUVrXVaH8/o+sYy3E0PdovvMxveiIzj5FUO0TzCd9OY90n0vubaR1/o9VqyShdFyizEH5s0Hata4NolCnSsouOKZHrG54Z02dLlizJ0DwzKnIbzegxSJmz8BpQah6k972Mvz7TtqRsY/g1h2qaaLrw7cGvzaM+e/PNN0Pvad46hyurFCtlkXR8UmY3/DipzKjmr8zgwTaPSe/8HW17VXZKx/JwXuZTGcVwTz31lHtfx+Tw81TNmjVTZeN0bajrWM07/DpRf18Z4NatW8e0vAe732fkuljHLW174efh8ePHp6ppkdHMY1Zf5+t/Xb80btz4gD46vO9lpOzTEssxK619JbOZR32m6/tIyvbrb+naRlSLTq9VeyCSaulon8zOefqJubdV3Q2TaHcNleHQHRTv8eKLL7r3N2zY4O4M6O6VlxHSQ3c6dHdk2bJl7o56ON0NCx9KQnfG1PviqlWr3GvdbVF2UHeKvPnpkT9/fpdhiXZnyI/uRimLoAxgZEQufkNb6K6mInotk8dbPmUO0qO7KLoDpzteHt09ibw7pjtgunugXsmUSfPoLl3r1q1dz7KRlHXICpHz0W+i39DbJjwtW7ZMdffby1DrLpfu3ES+n1ZPdwdDd1S1jYQ/dNcwM3THNDwLq7LWnR1vuXXs0V01bTd6Hr49avvWHWJlGER3zZTR0F3XtGga3UlWFjxW2jd0J1F3+JQB8VSoUMHdnVOWyfudLrzwQpcdULbKo+9qf9JnGV0nj+52Ktue2WPIkCFDUh0/dKfU88EHH7i75crIhy+L7ixK5P6elb9Zeuunu5BeNlu/gfYHrZfuWkd+P1bah3UcU4Y63B133OGWVZmJjKxrZrcpP9rWdIc5/O6m9mnvGJSZY1Xk8U53piXatJkR/vt55yIdx3R3Xr09ZlRWHI/TOo6mtw66Q6yepZUdygxlxXU3Xz2N3n///ZneJyI99dRTBxx3oz1UMyKjVHNGWV5l38KHcVCmN61aDspw6nNvOomWOdB04dPEOs+DkZnyVmYofLm8jKG3r3/77bduW9J1hDJtHmUnleHJCB3HwrOr+rvaxzNyvtbvtXv3bpdBDa/1o+XTMeqzzz6z7BS+v6s8VbaqFaJ10Otwqg3g1boJvy7TMU6Zy/DfP/w6TXQM0HWszrUqf+93VOZS10NTp05115cHuy5++32s18XecUvntFKlSoW+r2OWMpGZkV3X+ZqXspjePurxvpcVZR/LMUuPyO3jYOgYEuuxSNKaNvxYlB3z9BNztVXv4j9ad66qrqUfWlUxwg86y5cvdwfHBx54wD2iUfo9/EKkSpUqqT73DnzeTqONRbyLx0iRadtY/Pnnn+4Entnxjd5++22XptfFu9bZ+yEUSKnq6tlnn53md7Wz6EJfXeaGU/XgyOlEF6eRdHGtIFU7jZYj/KCYFdL7TcLLO3I67+CkdHm099O7ANKJRwelcAosdDBMj6rW6KI6K0Suj7fu3nJru9EBW12Lp9W9uLZvUbUfnVB1EtZv26ZNG3fQU1UPj6oi6aCu8lI1jA4dOtjll1+eKiiMpGXQBXBa24UOnr/99pur+lG/fn0XhKlqiKpHiZ6rCo23P2VknTK6nYUfQ8JPXLq54O17CpTCh+rQ/q4qlmlV74lclqz8zdJbP5WrqmuqOraGLgpfZq8qYEZpH1d11/AbLd7v6H2ekXXN7DblR1XhI6nKjKopZfZYFTlPBcW66MyqsRpVrUrBki5yIoO1yIvJWGRmHWM9jqZFJ3xVR9M+ourBqvaoZgH6PWO5Qab1VvU5nW9VVdC7CMvMPhFJ21Z20PFJv5uOV6r6GHlRrfNENKpO7QUQ3v+qch5tuvBpYp3nwchMeftdF3nbY+R1gwLJjI4lq+qLkTfM9fcyMgxbWvuHgiAdeyKPZVlN1WJVjXHmzJnu/Bi5v4eff6Id37V8OgZFlkNk+XrXo9GaGYX/vYwG8Bnd72O9LvbKPdoxPLM3PrPjOv/nn392/6d3TZ4VZZ9V14oZoWNIrMciSWva8GNRdswzy4JH7WwKchYtWnTAZ14mKfJE70X9qqefVuQeuTOmFRx4bfa8eap+d7QTZvhdt0NBy6V67LpQiHbnRjuNLpYz087jYGXFiS6W38Rvuli/HzkOzZlnnpnqPV2kH8pB1WPdFnXDJK0DmNd+QxeUaluhdsO6q6k7zwo8lClVuxDRnTvdgVP7D2UE1RZLJw1lCjPS1iQ9yjA+/PDD7g6dghS1UdXdSm+/ycg6ZXQ7U+AqOoaEB80KbLwbDDrQh7dD0PLohsDTTz8ddZ6RNyay8jdLb/2UDdGJUlkctYn02tXqTvvB3mmOVSz71aHYprJD5EVbWrU/YhkTVBfqyjroAkrtvXRRqJt7ulDSTZ1E+r38aPtSxko1ZRSgahtUuyUFxQ0bNkz3u8qSKgOt9vXhwWpm9olIutGXVtAVuS+FX7inR3f8dYGsm6+qnRBJ1yP6/XWOVdtRj5ZDmQjdiBHtm7oAV9YlkveeN22s8zwYmSnvrNh2YnUo/1Z2UOChzJPONzpv6ByhoFW1AdTeMHJ/P5jrJG9eOq6qzVg0WXHt57ffZ8d1cazH3Oy4zj9UZe+1pfajY1ZWXU9XqFDhgExsWsei8Pcjpw0/FmXHPP1kaIvSQVyN/HXy8WuIKt7dbVXLzKoI36umpQO73zz9qpt6lNXQyTRaYOxHHZKoOqIuSrwMgUd3UZSe1w6fViP7qlWruioFujsWnn30Mpjh04kCkEiqdqXsUfhd7oyItZwOJWXJIgc4zWz10+xab203CsB0MI1l+9bvo+BND12MKAugQE6Nwb3qBdq51bBfD13AqFMTTZPWhb6WQdtNWtuFAprwAEt/W8GqglfdxVQ24qKLLsr0OmWE7paq6pmy8eHBo9/+/v3337uLgazYTrNq/dSJhG5uaDDxyEAlvFOPjCyz9nFlp73OOzxetUrvGJBRGd2m/Hh3fMOpMwjvxk5mjlWaZ3gGQMc/XRx48/TuHkcOsB1LBkPVvXThr4C5efPmqW5GRYr198rO43Es+4SyEHqo3HThpGqjqgGTFu13Og+pDLybOFm5T+hYpnOhHwVL6gjDj6paq5MNNSNRRjvaxa93wagqm8qoe/Ra2473uY6BugGl96P9HV2nePtbrPM8GNlxjPW2R+034Tdd1TmGbuqHB6OH4nwfvn+E13LQeU/7XVasd1rroc5xlFnRjdHwDFdGmjRp+VXFW8FM+N+JvC7zrkd1/ZhV16OZ2e9jvS72fpdox/DIY1msx9zsvM7XNXlk4JmZsk+LF0z5USc8uvmWFRo0aOC2RV17hd/E8zq/8o4xyrrquKdjj24Ch+9DqrIb/l52zNNPzG0eRe0VdKGqu+2qoup310AbstpDqlprtEhX1TcySnc2VDi68x+td7fweXon78iNP5JOLmrHo4NOtBNMendDvCqr6hFRPWmGP1Q/XtUDdLGc3vpoPdQbp0cnKa/daPhGrg3gf//7X6r10c6ljEL4iS6jtPx+ZXSo6cClA0L4I7Lu+8GKdftIi+6eqcqlArFoNx7Ct0VdvIbTnVBlqrVt6ffXhURk9TntP7oTFK2KQfgyqAqseugNz/xr/1TvcGo/GH4w0Q0OXUipOpge2q7CL6gzsk4ZpYBRbStUVUvLG03kvqaDme6ohe8fHtXPV8Y/I7Jq/TSfyGVV+8zIu38Z2ca0D2s7eOGFF1K9r7vluvDIaLCX2W3Kj4KQ8PXUzUSdpLzly8yxKvJ4p15LxZuntmEFZGrHEk7Zez/eXe7w30sny2jf1e8VSzXW7Dwep0U3GL1qSOEXUApE0vs9dUNCVT/Vm6zOc9mxT2Rlm0dVU9eNat04UE2NtO74q4qesorqUTKcXus6Jby5iM7Ham8efn7XxbIyN+oxOTPzzKzsOMYqyFZ1eR0nFTB6dO0R2TzkYM97sdD5Wuc49Zocvt/pZpv2r6wox7TWI9r+rr+pACBWui7TMU4BqEf7XuR5SNW1tQ8++eSTUZt0ZeZ6NDP7fazXxeHHrfDjnPZNBcuRgabK0u+Ymx3X+bqm0fopuxq57t7vmpGyT6Q2j+edd547N4dXWdfvqO1TtTi9m/3Kdmo/UoyhG8oeZZe1vuHHreyYZ5ZmHhUI6YJU1dxUP1qNsZUh0o+pu0n6TIFYeJe/uijQBawuWBVM6S6FLmxVF10ZO2UVMkI7iA7kl112mbuDrqyJ7uSpy2I1wtYFqnfx5bXDUAcU+vG1I4RnWcJpp9NJX9WbvCEBtCPoglCdjoQ31A//cXQC0AVxWoGNGlyrbVRkNRiPTubK4upuku5q6a6wDlhee7/wu1VKz+tiqkmTJq4NiNc1vDYIdYudWSonXWCoiocuLJUByMhwLDmVt33ookrbhe6cqWpIRjIGuqOvOz4qL23fCgj126lKnMrU+x11MFTmVNunMn66QNJ2qpOoDpI6oWi/0UFA+5SqW+j7uuDRhVl6HnroIXeA036m7JLuLOlAru1Tbd4iKfuo6rLaZrUdRQ5jEus6ZYYOWuoGXtu9tmUdyHSjQNVHNG+dqMKDJO3nyjyosxEtk8pPB0lld/S+qvBE6+QqPVmxfsqiqraBOrJQN/fqMEsXapFtCXVy07FD1e70O2vb0t+N1s5G256yBtoedSNA24GOSQq0VW0pcggdPzo5xLJNKTOnv6s2QrEcR3QnWNua2qBpG1M367pwDQ8MMnqs0vlDx0ptGzo3aDtRm2Att0dDCum30//6zbWtKOPpR7+PtjFlvXQu0DFVJ8toNwV1TNBNFQ3pcfLJJ7sy0+8STXYdj9OidVUGXjdUtM1qP1d1ZJ1P0zqvic7XOkfq/B2ZndS5S8ejg90nsqrNo7ZZnasV8OiGbGTHKtoHVN6ioFJVxtXZki569D11z691VGY9vCMjHRd14a/jrarX6Viv853WXedeT0bmmdH9JjuPsQrUtAzqaEoBsLYRHUOU5Y1su5eRY1JmaXtTjRrVctE+rX1bwboCD+1XWTHcSVrnb51rVR56ruE3dFGs317XX9GCm2j0PZ2fte9oqA4FXTq+e9d5Xnnq3KnaeDoOqF8BnQ/Utk+Bp35fXa96Q1Rl9nojlv0+I9fFCsi0H+gYrkSQtjUdt7T84UGYjmPaB/SZ1lfbjW7mRGv/nB3X+bppqmO9thedC3QM13wUTCv4zUjZpyUra1dNnTo1FGgrcNWNbV2biW7Qezfpta+pXLV/qCx1PtX6aH+NrMmkY47OX15corLUuVvbuPYrT3bM01cgE9Q9/PXXXx849thj3WC06rpdXRhr8O/wQbI9GkJAXZerG1gNFaCBTs8555zAhx9+eEAXvpFDZaTVXbBeq4tedUOsZdBAvxpE9dtvvw1NowGgb775ZtfFtrq6D1/dyK6zZdWqVW45Nb263Fe3zuq6O7yL7HDeMBvpDaQ8ZcoUN4261k5rYHB1UX3JJZe4rom1PloPDUGg77333nuppv3yyy8Dp59+uitzdV2uQWzTGpQ6WtfX0YbqUDflGtJB8wzvTj2t+UTrCl+vI4cECB9UPNpvqm6Ds0os84z2mw8cONBtj+peP3JYj2hDHOi3i+xuft26dW7aypUru+1b27m6rh46dGiqYUtUxupOXduWtlcN1OoN6qptTK/r16/vtgMNn6Ln0QY2jua7775z+4MGetXwAxogWQNbR6PBpb0uqNMaTDeWdcrs76iutzU0TZMmTdw2rEFrNX8dEzQAcPjA7bJ79243SLU3qLEGIdYAxP379081KG5W/2bprZ+6JL/jjjtcl+fab7RPzpw5M+owCRooWQNlaz3Du/2OdixQl+vqtlwDc2u51BW59p/I4YJiWddYt6nRo0en6s47LeH7s7qyV9np92jWrFloSI3MHqv0voYk0XLq973pppsO6KJdXbBriAMdIzXdBRdc4IYEiGWoDh1PTz311NCg57179w588cUXB5xbtm3b5o7F6vY+vOv6tIYKOZjjcbTlTI+GddBvrnOtfkuVg7qy19Ab4SK3wfS6oA9f91j2iezmlXNGhvrQ8tWoUcMNDaHjqgZ5jza81m+//ea2Mf1OOk7qeKNjYTSxzDPW/Satc8/BHIPS2h6fe+45t81qvzzllFPcdq9jZbt27WI6Jmm7iTakVLRjVSw0NIe2V61fuXLl3HVj+HArBzNUR3rn708//TRQr149d21YrVo1d/7Q8CCR+5vWScPzRLNixQr3mfZtXRPqeO9d82lg+nDz5s0LnHvuuaHzu+ar49PEiRNjWt6s2O9jvS4WrYeGf9Gyajv4+OOP07w21RAtuqbQcVnDGy1atCjqtpcd1/n6HTXch3d81Tb97rvvZqrss9uD/27H0R6R+77ObXfeeacrKy2zhtiJHC7Go2HKVAb6PbUdalsIH/ouO+eZniT9czDRNrKHqoapzYeynrG2DwOAjFLGUJ1+qeZDegMh6y6mshPKuCl7kxW8gcR1pza8rSiQW/abeFITGGWg1CY1WtV/ZIxqWdx2220uWxPee2huoDZ9yqZnVQ/XyN0y1OYR2SNybBVVy1NVAaXdVQUBALKLqvio975EvQAGElGi7TdqGxaZC9CQLKqWqDZpOLjrMpWvmoOo+nduCxyBjDq041ogKrVT0IFKbTnUhkg94mmoCrXDzKrugQEgGrWBRHyp8wq/AZqzurdp5K795ptvvnFZMbV9UhtktZ9Ueyf1sJiRjjD8qJZAekPkqL1heNvQjFK7u2gdoGR0zOeDpWytemtVBzPaP9XuVW3t0+sAMaPY75FTETwmADVwV4NVNUbW3S01dlXm8aabbor3ogEAspk65VAHB+mhhQnSo95p1auiejhVtlEBnMbJVOc8CuiyijowSW+IHHXEoeqPmaXeM72xj9NyKMZ8VmdJ6pBFwaKCZXVW895777kO57IK+z1yKto8AgAQR+omf/Xq1elOk9XjrgKZMX369HSz5OoV82B6312xYoV7pEc9e2b10F3xwH6PnIrgEQAAAADgiw5zAAAAAAC+aPMIHEQ36KpqpoGWwwdhBgAAiUttiLdu3WoVK1Z0A84DiB3BI5BJChzVQQEAAMh5fvvtN6tUqVK8FwPIUQgegUxSxtE7+WhMzqyUkpJi48ePtzZt2liBAgWydN65DWVFWbFdsQ/mFByvEqOstmzZ4m7+eudxALEjeAQyyauqqsAxO4LHokWLuvkSPFJWbFeHHvsgZcV2lfv3QZqcABlHRW8AAAAAgC+CRwAAAACAL4JHAAAAAIAvgkcAAAAAgC+CRwAAAACAL4JHAAAAAIAvgkcAAAAAgC+CRwAAAACAL4JHAAAAAIAvgkcAAAAAgC+CRxxSU6dOtY4dO1rFihUtKSnJRo0alerzQCBgffv2tQoVKliRIkWsVatWtmzZslTTbNiwwbp162YlS5a00qVLW48ePWzbtm2pplmwYIE1a9bMChcubJUrV7bHH3/8gGX54IMPrGbNmm6aunXr2tixY7NprQEAAICcj+ARh9Q///xj9evXtxdffDHq5wrynnvuORsyZIjNmjXLihUrZm3btrWdO3eGplHguHjxYpswYYKNGTPGBaTXXntt6PMtW7ZYmzZtrGrVqjZ37lx74oknrF+/fjZ06NDQNDNmzLCLL77YBZ7z5s2zzp07u8eiRYuyuQQAAACAnCk53guAvKV9+/buEY2yjs8++6zdf//91qlTJ/fem2++aeXKlXMZyosuush+/PFHGzdunM2ZM8dOOukkN83zzz9vHTp0sCeffNJlNIcPH267d++2119/3QoWLGh16tSx+fPn29NPPx0KMgcPHmzt2rWzu+66y70eOHCgC0ZfeOEFF7gCAAAASI3gEQlj5cqVtnbtWldV1VOqVClr3LixzZw50wWP+l9VVb3AUTR9vnz5XKayS5cubprmzZu7wNGj7OVjjz1mGzdutMMOO8xNc/vtt6f6+5omshptuF27drlHeIZTAjVrWiBf+kn8QIMGtnfkyFTv5e/SxZLmz486ff5AwNrs2mX5CxWyvb162b5evfZ/uHWrJderZ7HY89FHZieeGHqd9Nlnlv+mm/y/WKyY7YnIwua75x7LN2KE71f3tW9v+156KfX6nHqqJa1b5/vdvY88YoGLL97/xtKlltyuXbrf8coqZdYssypVQu8nvfqq5X/4Yd+/GTjuONs7fnzqeV5+uSV9/bXvd/dddZXte+CBVO8lV69usdj7xhsWOOOM/cv71VeW/4orYvrunpUrU73ON3Cg5Xv9dd/vJZ1+utkll1hKSkrovfxt2lhSRNXwqMt7330WuPrq/W+sWWPJp50W2/KOG2dWo8b+5Xj3Xct/772+3wuUK2d7v/km1Xv5brjB8n3+ue939114oe179NFU7yWfcIKqP/h+d+8LL1hKmzbuuSur776z5K5dLRZ7FiwwK1Fi//I++6zlGzzY93sZPUaE23frrXE9RoS2p7vussCHHybcMcKzZ8YMswoV4nqMUFm16dHDHdsDSUkJd4wINGtme998M9V7cTtG9OkTOg+mVVaZPUYE9u2LabkAHIjgEQlDgaMo0xhOr73P9H/ZsmVTfZ6cnGyHH354qmmqR1zAe/PUZwoe9X96fyeaQYMGWf/+/Q94P2nNGkv/EsBsY/Hi9nVEm8pmy5fb4X/8EXV6za/Iv8+XzJ1rS8O+m7x9u52dxvcizZwyxTaGrVPFGTPs5Bi+m1KkyAFtQBssWGBVY/ju2h9+sG8jvttm1Sor8vffvt9dOHu2/VqqVOh1iV9/tbN8/qZXVl9MmWI7y5QJvX/MnDl2QgzLuzVfPpscsbxNliyxsjF8d+X339viiO92ivG3mT1tmv0ZFsgcOW+enRbjdyN/mzrff2/HxvDdDT/95P5Xlt1z5s8/W8kYvrtkzhz7uWLF0OvCf/1lbWNc3q8nT7atP/8cel1l9mxrGMN3VV19fMS6nvTDD3ZUDN/9fcECmx/x3Q6//WYFduzw/e68GTNs9b8Xqyqrw5YsseYxruv4L76wPUWLhl7XmDvXasbw3YweI8ItS5BjxOrFixPyGOGZNGFCYhwjYljPeB0j/lyyxGZGfDdux4jVq0Pnwaw+RvidswGkjeARiFGfPn1SZSuVeVRnPIEKFXwzj6WPPdZVrQ2X/5VXLBDR0U94FV5lOQsVKmTHNWpkx4R/d+tWCxx1VEzL3KRFi9RZhUAgpu8mFyt2wPLmmzrVAj/+6Pvd8rVrH7iuVataoHBh3+/WPeUUOyH8u0uX+i6vV1bNWrSwAuGZx9WrLRCRLYim+DHHHLi8771ngT//9P1u9fr1rWrEd2P9bU5p2jR1VqFYsZi/e8BvM3euBebO9f3e4ccf7/5v3bq1FShQwD3P/+yzMd2Fr3nyyVYj/O+uWRPz8jY788zUWYXNmy2QTpbfU6hcuQPXdcwYC6xa5fvdSvXqWcWI7yZrf40h89jwtNOsTuvWLnB0ZVW+fMzr2qZt29SZx59+ssC0ab7fy+gxIly8jxHKpqmsKtapk5DHCM9ZrVunzjzG4RihstpxxBHu2K5O4xLtGFGmZs0D1zVOx4h9I0eGzoNplVVmjxFufdasiWnZAEQIAHGizW/kyJGh1z///LN7b968eamma968eeCWW25xz1977bVA6dKlU32ekpISyJ8/f+Djjz92ry+77LJAp06dUk0zadIkN+8NGza415UrVw4888wzqabp27dvoF69ejEv/+bNm9089X9W2717d2DUqFHuf1BWbFeHHvsgZcV2lXv3wew8fwO5Hb2tImGoqmn58uVt4sSJqbJ7asvYpEkT91r/b9q0yfWi6pk0aZLt27fPtY30plEPrOHtunRXvEaNGq7KqjdN+N/xpvH+DgAAAIDUCB5xSGk8RvV8qofXSY6e//rrr65aSq9eveyhhx6yTz/91BYuXGiXX36560FVw2hIrVq1XC+p11xzjc2ePdumT59uN910k+tMR9PJJZdc4jrL0TAcGtJjxIgRrnfV8Cqnt956q+u19amnnrIlS5a4oTy+/fZbNy8AAAAAB6LNIw4pBWhnqm3Dv7yArnv37vbGG29Y79693ViQGlJDGcamTZu6IK9wWFsYDcWhIK9ly5aul9WuXbu6sSHDe2gdP3683XjjjdaoUSMrU6aM9e3bN9VYkKeddpq98847bliQe++914477jjX0+oJ6o0RAAAAwAEIHnFItWjRwnVwkhZlHwcMGOAeaVHPqgr80lOvXj372qcb9fPPP989AAAAAPij2ioAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIxLO1q1brVevXla1alUrUqSInXbaaTZnzpzQ54FAwPr27WsVKlRwn7dq1cqWLVuWah4bNmywbt26WcmSJa106dLWo0cP27ZtW6ppFixYYM2aNbPChQtb5cqV7fHHHz9k6wgAAADkNASPSDhXX321TZgwwd566y1buHChtWnTxgWIf/zxh/tcQd5zzz1nQ4YMsVmzZlmxYsWsbdu2tnPnztA8FDguXrzYzWfMmDE2depUu/baa0Ofb9myxc1XAercuXPtiSeesH79+tnQoUPjss4AAABAoiN4RELZsWOHffTRRy5AbN68uR177LEuqNP/L7/8sss6Pvvss3b//fdbp06drF69evbmm2/a6tWrbdSoUW4eP/74o40bN85effVVa9y4sTVt2tSef/55e++999x0Mnz4cNu9e7e9/vrrVqdOHbvooovslltusaeffjrOJQAAAAAkpuR4LwAQbs+ePbZ3715XlTScqqdOmzbNVq5caWvXrnWZSE+pUqVckDhz5kwXBOp/VVU96aSTQtNo+nz58rlMZZcuXdw0Ck4LFiwYmkbZy8cee8w2btxohx122AE/zK5du9wjPHspKSkp7pGVvPll9XxzI8qKsmK7Yh/MKTheJUZZcW4FMo/gEQmlRIkS1qRJExs4cKDVqlXLypUrZ++++64L9pR9VOAoej+cXnuf6f+yZcum+jw5OdkOP/zwVNNUr179gHl4n0ULHgcNGmT9+/c/4P3x48db0aJFLTuo2i0oK7ar+GEfpKzYrnLfPrh9+/YsnyeQVxA8IuGoreNVV11lRx11lOXPn99OPPFEu/jii13bxHjq06eP3X777akyj+poR20n1TFPVt8V1QmzdevWVqBAgSydd25DWVFWbFfsgzkFx6vEKCuv5hCAjCN4RMI55phj7KuvvrJ//vnHHeDVq+qFF15oRx99tJUvX95Ns27dOve+R68bNGjgnmua9evXH1AdVj2wet/X//pOOO+1N02kQoUKuUckndSyK8DLznnnNpQVZcV2xT6YU3C8im9ZcV4FMo8Oc5Cw1IuqAkS1Qfziiy9cBzmqaqrgbuLEiaHpFGCqLaOqu4r+37RpU6pM5aRJk2zfvn2ubaQ3jXpgDW/3oDucNWrUiFplFQAAAMjrCB6RcBQoqrdUdY6jgO7MM8+0mjVr2pVXXmlJSUluDMiHHnrIPv30UzeUx+WXX24VK1a0zp07u++rrWS7du3smmuusdmzZ9v06dPtpptucp3paDq55JJLXGc5Gv9RQ3qMGDHCBg8enKpaKgAAAID9qLaKhLN582bXvvD33393ndx07drVHn744VA1k969e7sqrRq3URlGDcWhYDO8h1YNxaGAsWXLlq6XVc1DY0OG99Cqjm5uvPFGa9SokZUpU8b69u2baixIAAAAAPsRPCLhXHDBBe6RFmUfBwwY4B5pUdD5zjvvpPt3NEbk119/fVDLCgAAAOQVVFsFAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieESGbdmyxUaNGmU//vgjpQcAAADkEQSP8HXBBRfYCy+84J7v2LHDTjrpJPdevXr17KOPPqIEAQAAgDyA4BG+pk6das2aNXPPR44caYFAwDZt2mTPPfecPfTQQ5QgAAAAkAcQPMLX5s2b7fDDD3fPx40bZ127drWiRYva2WefbcuWLaMEAQAAgDyA4BG+KleubDNnzrR//vnHBY9t2rRx72/cuNEKFy5MCQIAAAB5QHK8FwCJr1evXtatWzcrXry4Va1a1Vq0aBGqzlq3bt14Lx4AAACAQ4DgEb5uuOEGO+WUU+y3336z1q1bW758wYT10UcfTZtHAAAAII+g2ipioh5Wu3Tp4rKPe/futfnz59tpp51mp59+epaWoOb9wAMPWPXq1a1IkSJ2zDHH2MCBA10nPR4979u3r1WoUMFN06pVqwPaXm7YsMFlS0uWLGmlS5e2Hj162LZt21JNs2DBAtcRkKreqmru448/nqXrAgAAAOQmBI+Iqdrqa6+9FgruzjjjDDvxxBNdwDVlypQsLcHHHnvMXn75ZTc0iMaR1GsFdc8//3xoGr1WT69DhgyxWbNmWbFixaxt27a2c+fO0DQKHBcvXmwTJkywMWPGuCq21157baqxKtV2U9Vw586da0888YT169fPhg4dyhYBAAAAREHwCF8ffvih1a9f3z0fPXq0rVy50pYsWWK33Xab3XfffVlagjNmzLBOnTq5nlyrVatm5513ngvyZs+eHco6Pvvss3b//fe76TTW5JtvvmmrV6+2UaNGuWkUdKpjn1dffdUaN25sTZs2dcHne++956aT4cOH2+7du+3111+3OnXq2EUXXWS33HKLPf3002wRAAAAQBS0eYSvv/76y8qXL++ejx071s4//3w7/vjj7aqrrrLBgwdnaQmqKqyyfz/99JP7G99//71NmzYtFNQpcF27dq2rquopVaqUCxLVI6yCQP2vqqqqauvR9GqrqUylqt9qmubNm1vBggVD0yh7qUynepE97LDDDli2Xbt2uUd49lJSUlLcIyt588vq+eZGlBVlxXbFPphTcLxKjLLi3ApkHsEjfJUrV85++OEH18ZQGT1VK5Xt27db/vz5s7QE77nnHheU1axZ081b1WQffvhhVw1VFDh6yxS5jN5n+r9s2bKpPk9OTnZjVYZPo3aVkfPwPosWPA4aNMj69+9/wPvjx493415mB1W7BWXFdhU/7IOUFdtVfGXHPqjrFwCZQ/AIX1deeaVdcMEFLnhMSkoKZf2UxVOQl5Xef/99V6X0nXfecdVJ1TGP2lxWrFjRunfvHtdfq0+fPnb77beHXivIVbtPVatVxzxZfVdUJ0z1blugQIEsnXduQ1lRVmxX7IM5BcerxCgrr+YQgIwjeIQvdSRzwgknuKE6VGW1UKFC7n1lBpUpzEp33XWXm6eqn4rGkVy1apXL+il49KrPrlu3zgWzHr1u0KCBe65p1q9fn2q+e/bscT2wet/X//pOOO+1N00krbe37uF0UsuuAC87553bUFaUFdsV+2BOwfEqvmXFeRXIPDrMQUzUcY06yClTpkzoPQVz6rQmq6uSeONIehSk7tu3zz1XVVMFdxMnTkx1B1FZ0CZNmrjX+n/Tpk2uF1XPpEmT3DzUNtKbRj2whrd70B3OGjVqRK2yCgAAAOR1BI/wpXaHGmvxqKOOcuM8rlixwr2v8Ri9ITyySseOHV0bx88++8x++eUXGzlypOssR53ciKrNqhrrQw89ZJ9++qktXLjQLr/8clettXPnzm6aWrVqWbt27eyaa65xvbROnz7dbrrpJpfN1HRyySWXuM5yNP6jhvQYMWKE6/wnvFoqAAAAgP0IHuFLwdwbb7zhxlcM751UVVk1HEZW0pAaynLecMMNLgi888477brrrnPBq6d379528803u3EbTz75ZNu2bZvryKdw4cKhadRuUu0xW7ZsaR06dHDDdYSP4ageWtXRjXpvbdSokd1xxx3Wt2/fVGNBAgAAANiPNo/wpXEUFXgpEOvZs2fofY39qPEes1KJEiXcOI56pEXZxwEDBrhHWtSzqjrdSY/GiPz6668PankBAACAvILMI3z98ccfduyxxx7wvtoQMlYSAAAAkDcQPMJX7dq1o2boPvzwQ2vYsCElCAAAAOQBVFuFL7UFVM+qykAq2/jxxx/b0qVLXXXWMWPGUIIAAABAHkDmEb40HMfo0aPtyy+/tGLFirlg8scff3TvafBeAAAAALkfmUfEpFmzZm4cRAAAAAB5E8EjYrZ7925bv369q7oarkqVKpQiAAAAkMsRPMLXsmXL7KqrrrIZM2akej8QCLhhM/bu3UspAgAAALkcwSN8XXHFFZacnOw6x6lQoYILGAEAAADkLQSP8DV//nybO3eu1axZk9ICAAAA8ih6W0VM4zz+9ddflBQAAACQhxE8wtdjjz1mvXv3tilTptjff/9tW7ZsSfUAAAAAkPtRbRW+WrVq5f5v2bJlqvfpMAcAAADIOwge4Wvy5MmUEgAAAJDHETzCV/Xq1a1y5coH9LKqzONvv/1GCQIAAAB5AG0eEVPw+Oeffx7w/oYNG9xnAAAAAHI/gkf48to2Rtq2bZsVLlyYEgQAAADyAKqtIk233367+1+B4wMPPGBFixYNfbZ3716bNWuWNWjQgBIEAAAA8gCCR6Rp3rx5oczjwoULrWDBgqHP9Lx+/fp25513UoIAAABAHkDwCN9eVq+88kobPHiwlSxZktICAAAA8iiCR/gaNmwYpQQAAADkcQSPiOrcc8+1N954w2Ub9Tw9H3/8MaUIAAAA5HIEj4iqVKlSoR5W9RwAAABA3kbwiDSrqk6aNMmaN29OtVUAAAAAjPOItLVu3do2bNgQen3qqafaH3/8QZEBAAAAeVC+eC8AEpeG6Ai3ePFi27VrV9yWBwAAAED8EDwCAAAAAHwRPCJN6jDH6zQn2msAAAAAeQcd5iDdaqstW7a05OTgZrJ9+3br2LGjFSxYMNV03333HaUIAAAA5HIEj0jTgw8+mOp1p06dKC0AAAAgjyJ4RMzBIwAAAIC8izaPAAAAAABfBI8AAAAAAF8EjwAAAAAAXwSPAAAAAABfBI/w9eabb9quXbsOeH/37t3uMwAAAAC5H8EjfF155ZW2efPmA97funWr+wwAAATt22e2eLHZ2LFm8+eb/f138PHbb2Z//WW2ZUtBCwSifw8AEh1DdcBXIBCwpKSkA97//fffrVSpUpQgACDXS0kxGzXKbNUqs02bzEqWDD4qVTL75Rez1avN1qwx++wzsz//TGsuBcysvV1zTcAKFTIrWNCseHEznWI1jypVzGrVMmvf3uzII82OO87ssMPMJk0yUwWg7dvNChQIPj/66OC0GzYEv58vn9mOHcHnU6YEA9bSpc3y5w8ur4LTBg3MdC9Y82zb1qxyZYsLLafWXcsGIGcheESaGjZs6IJGPVq2bGnJyfs3l71799rKlSutXbt2lCAAIFdYuzYYxCkoVPA1bZrZt9+a/fST2YoVweAtFkWLmh17bHB+69cH31NQ52Ucd+1KcgGgKBvpUWCqx7hxdkhUrGi2Z49ZhQrBwLVECbNt23SOD66DgkzdI9ayatm//joYvJYrF3yt6RXkaj0LFw4Go5pP1arKsJqNGWM2c6bZP/8EA0bNp2zZ4DqqPPTd8uWD89O8VL5btwanT0lJtr/+amV3353Pbrvt0JQHAH8Ej0hT586d3f/z58+3tm3bWnHdHv1XwYIFrVq1ata1a1dKEAByQVZNQYGsXGn2889mf/xhtmTJ/syWql2ecIJZ7drB5wo6lP2qVi3Jtm3798sZoGyYMk8KGrKagg9l2hTQKFBR9dHRo4MBz86dWuZgIDN1ajBg0TqqDKK00EjliCPMzjprf0Cl6RVsKsOnrN7hh5udempwGq889ff00Cl0z54U+/TTL+zEE9u6LKQ3Dz0UbK5bFwzQJk9WvwJmCxYEg7n69YNBl+avwE7zXrrUbOHC4HsK9PR+sWLBv6WArGnT4Lrpd9Ly6bm+o/LW7zdjRjBbKgpwv//eDgn9HqLfQn/XC64PpBpPxWzjxr2HZsEAxITgEWl68MEH3f8KEi+66CIrpDo2AIAcSwGVskG//x4MTt5/32z58mA1S2V/FMx5AUU0asd3oGRLSmpvvXsHM1UKXJRlUmUVBTbK4Clg0d9WIKO/o+ySF8zVqBGswqjPa9YMZq/0Wg8FQwqoFARqOgWuCoSUCdQ66G8ddZTZokVmEycGg0RlCBVARWtX6EdBsqqhKvhU+bRubXbyyWYnnhgMOKtXDy5XRmjZ9RAtU6FCe11mzgsuw6maqoK+Pn32v6fvRGk5ctD0myuQ0++k6ra//mq2cWPwN9R7+g0VVCuw1bIqOK1XL5iV1W+iZdLvqPad+k2URVRQqmBa89K2VKeO+k0wK1MmWAaaXsFio0bB7ytjqYfmpyBZv7cuNbTOycl7bMWKGXb++U3MjPqtQKIgeISvs846y/7880+rpDOqmc2ePdveeecdq127tl177bWUIAAcIrqYV3XAb74xmz49eOGuC3JddCuQUnZKGT1dqOviX1UilUHUBbqCAGW/NI9odAEvChyOPz44PwVzmqcyZAoOlIlUtuuYY8yKFAkGnsuXB+y335Jc0KmHlikjlA3zZPS7sVBZaNmbNw8GP6JgUwGN1lPrqIyi2gzqNJdoTfmzI3AUlYkeoqxpPOhGgrKq0aSkBGzs2I1xa5cJIDqCR/i65JJLXJB42WWX2dq1a61Vq1Z2wgkn2PDhw93rvn37UooAEANlVJTtUfCiIE0ZOWXJlPlREKXMjQI1Zb2UWVMGR4GNgkCvPdjBUOCooO/MM4NBidqnqfWBqkwq66RlUXZP2aVYpaTssXfemWi1arW0DRsKuGVVMKvgVYGsll9ZJa8aqQJZPVdWT39/1qzgfFQeKhd9X+uvhwJlBa+a148/Bh8KgpWhU1ChgFflpLZ7Z59tdsopwQyl1lHLoHkoSFRgCAA4eASP8LVo0SI7RWdkUxWn961u3bo2ffp0Gz9+vPXs2ZPgEUCup4BObcQUlHjZPbUNnDs3GHQp6FPA5QWDChIVKKnKplctT/8rmFHbuozQ98KpOqCyNaefHqxSqSqDqmapv+FV8dTyKoOmKojKLilIU3VABY9qt6j3I6kqZWaVLr3LGjaMXhXTj4I+j3oABQAkLoJH+EpJSQm1d/zyyy/tP//5j3tes2ZNW6Pb5QCQCyiwU5u+L79U8JfP5sw5xR55JL/LhCnbpUAwKyjDpgDOy64p2FN2ThkztalT5ydqV6fgT9UqFRAqYPWqVSrwy0yQBgDAwSJ4hK86derYkCFD7Oyzz7YJEybYwIED3furV6+2I3TFAwA5iDKDGoJBQwgoKFTwpuqhChz3B4jqoKNCqu+pvaA6gFGHHwrs1IGKxtlTtUj1Oqr5qlqpspPe39EhUu26vOEIlDVUIKgqlQAA5DQEj/D12GOPWZcuXeyJJ56w7t27W/1/W7d/+umnoeqsAJBIli0LDnmgAM4b7kBVOdUTpDplURu6aBTkXXCBAsq99tdfP1ibNrWsYsVkly1UpyJeYAgAQF5E8AhfLVq0sL/++su2bNlih4U1lFEnOkVVvwoA4kCdrigwVLvDKVP2D8iu7J7e19ACadGg6BqLT/+rzaI6XDnjjOBDgWJKyj4bO3aFdehQkyqiAAD8i+ARMQkEAjZ37lz7+eefXe+rJUqUsIIFCxI8Ajgk1AHNZ58Fe9ucPdvs22+DQV96g7qrYoSqinpVS9V0W53CqKdOvZddQyAAAJBbETzC16pVq6xdu3b266+/2q5du6x169YueFR1Vr1We0gAyCoaSH7OnGBHMcoCqrrpc8+ZTZ164LRqO3jqqcFB3DXEhDKJ3nATChIBAEDWIXiEr1tvvdVOOukk+/7771N1kKN2kNdccw0lCCBdGkZi9OhgxzTqdEYZQD3XQ0NeqNrphAnBnkcVOHpj/UULFNu1CwaGLVsGs4kan/DfzqABAEA2I3iEr6+//tpmzJjhqqmGq1atmv3xxx+UIJDHq5Oq51JVI1UwqHaGGkZCHdKo59LJk83mzQsOcJ8R6tVUgaGCSVUvVcD4wANkEwEAiCeCR/jat2+f7Y3S88Tvv//uqq8CyHvUa+mHH5r973/BTKEfZQnV3tAb21DZSFUv1QD3Grqia9fg/wo8VfX0tNOCVVYBAEDi4NQMX23atLFnn33Whg4d6l4nJSXZtm3b7MEHH7QOHTpQgkAup0Dvq6+CYyF+/32wmqkyjco6iqqitm0bzC4q+FOmUENaqDqpgsDTTzerWTPYCyoAAMi5CB7h66mnnrK2bdta7dq1befOna631WXLllmZMmXs3XffpQSBXETVRNWb6cqVZp9/brZ8ebCH0y1bDpz2zDPNOnY0u/LKYAYRAADkbgSP8FWpUiXXWc6IESPc/8o69ujRw7p162ZFGDEbyPEUGL7zTrCH07Fjg+MlRlKV0zp1zE44Ifh/q1bBsREBAEDeQfCI2DaU5GQXLOoBIHfYsMHs9dfNBg0KPveoU2UFiV6vpuXLB4fDoNopAAB5G8EjfP3999+hITp+++03e+WVV2zHjh3WsWNHa968OSUI5CDr1xexBx7I5zKMCxfuf79GDbPzzzdr3FjtnM0iOlcGAAAgeETaFi5c6AJEBYzHHXecvffee9auXTv7559/LF++fPbMM8/Yhx9+aJ07d6YYgQSkTpLVftFrt/jFF/lt3rzWFggkhaZRFdQ77zS79FJ6NwUAAOnL5/M58rDevXtb3bp1berUqdaiRQs755xz7Oyzz7bNmzfbxo0b7brrrrNHH300S/+mxo5Ub66RjxtvvNF9rg579FyZ0OLFi1vXrl1t3bp1qebx66+/uuUsWrSolS1b1u666y7bo15AwkyZMsVOPPFEK1SokB177LH2xhtvZOl6APGiHlCnTDHr2dPsqKOCvZ1efnmwaup33+VzgeNZZ+1zbRzVtlHDbFxxBYEjAADwR7VVpGnOnDk2adIkq1evntWvX98N1XHDDTe4rKPcfPPNdqoaQmXx3wwfU3LRokXWunVrO1/16czstttus88++8w++OADK1WqlN1000127rnn2vTp093n+q4Cx/Lly9uMGTNszZo1dvnll1uBAgXskUcecdOsXLnSTdOzZ08bPny4TZw40a6++mqrUKGC61UWyEn+/NPso4+CPaOqh9Rdu8x++mn/5+oFtVEjsypVzJo23WP58n1p3bq1tAIFuHcIAAAyhuARadqwYYMLwkRZvmLFitlhhx0W+lzPt27dmqUleOSRR6Z6rczmMcccY2eccYbLeL722mv2zjvv2FlnneU+HzZsmNWqVcu++eYbF8iOHz/efvjhB/vyyy+tXLly1qBBAxs4cKDdfffd1q9fPytYsKANGTLEqlev7oYgEX1/2rRprhouwSNyQmZx8WKzyZODGcYJE8wid0O1V1S2UfdcNJyGxl6UlJSAjR27Ky7LDQAAcj6CR6RLVUbTe52ddu/ebW+//bbdfvvt7u/OnTvXUlJSrJXGCPhXzZo1rUqVKjZz5kwXPOp/VbVV4OhRQHj99dfb4sWLrWHDhm6a8Hl40/Tq1Svd5dm1a5d7eLb8O/CdlkmPrOTNL6vnmxvllbLS5vbRR0n2yCP5bdWq1PthrVoBO++8fda4ccAFlw0bBqxs2f2fe0WTV8oqK1BWlBXbVe7dBzkGAplH8Ih0XXHFFa5doNfeUFU9lYGU8EAqO4waNco2bdrklkHWrl3rMoelI0YjV6Coz7xpwgNH73Pvs/SmUTCoXmTTGrty0KBB1r9//wPeV7ZT7SuzwwSllZBny2rz5oL2/fdH2tKlh9vkyZVt+/bgIbtgwT1Wq9YGq1v3L6tZc4N7nj9/wLymvd9+m/fKKrtQVpQV21Xu2we3b9+e5fME8gqCR6Spe/fuqV5fqu4YI6g9YXZRFdX27dtbxQQZibxPnz4uC+pRsFm5cmVr06aNlSxZMsvviuqEqfaeaq+JvFNWyjB++mmSvfBCPps/P8n27dufZTz++IBdeuk+u+WWgBUtqirk+6uR58Wyyk6UFWXFdpV790Gv5hCAjCN4RJrUnjBeVq1a5dotfvzxx6H31P5SVVmVjQzPPqq3Va9tpv6frbEJwni9sYZPE9lDq14rAEwr6yjKwHpZ2HA6qWXXhXh2zju3yclltW+f2SefKGg0++ADs3/+2f9Zw4bBsRc7dDA7++wky5cvv5npkTfL6lCjrCgrtqvctw9y/AMyj+72kLCBq4bZUK+onkaNGrkDvnpH9SxdutQNzdGkSRP3Wv9rfMr169eHptGdSwWGtWvXDk0TPg9vGm8ewKGijoVHjw72hnruuWYaMUaBY40aZv36mf3+u4bXMHv5ZbOOHc3+7egYAAAgLsg8IuHs27fPBY+qNpucvH8T1dAcPXr0cFVHDz/8cBcQargQBX3ekCGqQqog8bLLLrPHH3/ctW+8//773diQXtZQ7TZfeOEFN47lVVdd5YYjef/9990QIMChovsXN9ywf1iNEiXMrrrKrFMnsxYt1DkVvwUAAEgsBI9IOKquqmyiArtIGk5D40x27drVddijXlJfeuml0Of58+e3MWPGuN5VFVSqcx8FoQMGDAhNo2E6FChqzMjBgwdbpUqV7NVXX2WYDmS7RYvM3n/fbNq04FAbohrY11xjdvfdZkccwY8AAAASF8EjEo6yhwGNNxBF4cKF7cUXX3SPtFStWtXGjh2b7t9o0aKFzZs376CXFfCzc6fZkCHqACoYPHpUBfXGG80GDlRWnXIEAACJj+ARALKYeoF/4QW1pTVT/01ex37q86F9e7NmzcxatzarX5+iBwAAOQfBIwBkkd9+M1PSW7WkV6/e/36VKmb33GN20UVmh2VsdA0AAICEQfAIAAdpxQqz6683Gz9+/3vVqpnddVdwmA0Nt0FPqQAAIKcjeASATI7NqI5vFDA+80ywqqoCxAYNzLp1C7ZnjDIsKAAAQI5F8AgAGTR3bjA4nDVr/3tnnGH2yitmxx1HcQIAgNyJ4BEAYrR+vVm/fsHeU9UhcPHiwQ5w/vMfs0suoWoqAADI3QgeAcDHqlVmTzwRHG5DQ2+IgsUnnzSrUIHiAwAAeQPBIwCk4YcfzB57zOydd8z27Am+d8opwfdatKDYAABA3kLwCABhVB119GizYcPMRo3a/36rVmb33hsMGpOSKDIAAJD3EDwCwL+9p06dGuw59dNPg0WiILFLl+AYjSefTDEBAIC8jeARQJ72999mQ4ea/fe/wbaNkj+/Wc+ewR5Va9WK9xICAAAkBoJHAHnS3r1mL75oNnCg2V9/Bd8rWdLswgvNrr/erGHDeC8hAABAYiF4BJDnLFkSrIr6ySfB13XqmN11l9kFF5gVKRLvpQMAAEhMBI8A8owtW8zuvjtYTVVtHFU99amngpnGggXjvXQAAACJjeARQJ6gqqlNm5otXRp8fc45wezj6afHe8kAAAByhnzxXgAAyE7KMKr3VAWJChwrVTKbPDk4HAeBIwAAQOzIPALItRYuDHaA8+OPwddly5qNGxds4wgAAICMIfMIINdJSTG74w6z+vWDgaN6UVUV1Z9+InAEAADILDKPAHKVL78069PH7Ntvg6+7dDEbMiSYdQQAAEDmETwCyBW2bTMbNMjsySfNAgGz4sXN/vc/s3PPjfeSAQAA5A4EjwByvHXritqJJybbL78EX195pdmAAcHOcQAAAJA1CB4B5OieVN98M8l6925mmzcnWdWqZi+8EByGAwAAAFmL4BFAjrRzp9kVV5iNGKHDWLLVrRuwL75IsgoV4r1kAAAAuRO9rQLIcaZONatdW4GjWXJywC699AebNm0PgSMAAEA2IvMIIEfR0BudO5tt3GhWurTZe+/ttZ07l1mRIsfFe9EAAAByNTKPAHKM6dPNTj89GDg2amS2cqXZWWcF4r1YAAAAeQLBI4CEp6E3hg0za9UqGDg2bmw2blww8wgAAIBDg+ARQMJ79FGzq64KdpKjnlQnTTIrUybeSwUAAJC3EDwCSFi7dpl17252773B1/ffbzZypFnRovFeMgAAgLyHDnMAJKS//w52jDNtmln+/GYPPmj2wAPxXioAAIC8i+ARQMKZP9/s/PPNli83K1XK7KOPzFq2jPdSAQAA5G1UWwWQUB3jvPhisEMcBY5Vq5rNmEHgCAAAkAjIPAJICCkpZt26mX3wQfB1x47BHlaPOCLeSwYAAAAh8wgg7vbsMbv55mDgWKCA2TPPmH3yCYEjAABAIiHzCCCuNmwItm/U8Bvy7rtmXbvyowAAACQagkcAcfPHH2Znnmm2bJlZ8eJmr71G4AgAAJCoCB4BxMWff5q1ahUMHKtUMRszxqxuXX4MAACAREXwCOCQW7fOrF07syVLzCpVMvvqK7Nq1fghAAAAEhnBI4BDasUKs2bNzFavNitXzmziRAJHAACAnIDeVgEcMv/8Y3b22cHAsWZNsylTzI4/nh8AAAAgJyDzCOCQ6dUrWFW1YsVg76oVKlD4AAAAOQWZRwDZLiXF7JZbzF591SwpyezttwkcAQAAchoyjwCyVSBgdtllZiNGBF8PGBAcngMAAAA5C8EjgGz15pvBwLFAAbMPPjDr1IkCBwAAyImotgog24webXbVVcHn991H4AgAAJCTETwCyBazZpldeqnZvn1m3bub3X8/BQ0AAJCTETwCyHKTJ5u1bGm2ZYtZ8+ZmQ4ea5c9PQQMAAORkBI8Asryqavv2wTEdW7Uy++wzs4IFKWQAAICcjuARQJZRxzjnnmu2a1ewfaMCyeLFKWAAAIDcgOARQJb46qvgkBx79gTbOqpn1cKFKVwAAIDcgqE6ABy02bPNOnY0S0kxu/BCs//9zywft6YAAAByFS7vAByUefPM2rY127rV7MwzzYYNI3AEAADIjQgeAWTaokVmrVubbdpkdvrpwTaORYpQoAAAALkRwSOATPn552Bvqn//bXbyyWZjx5oVK0ZhAgAA5FYEjwAyTMNwdOlitm6dWf36ZuPGmZUsSUECAADkZgSPADIkEDC75hqzhQvNypULZhwPP5xCBAAAyO0IHgFkyHPPmb37rllycnA4jooVKUAAAIC8gOARQMzGjze7447g86eeMmvWjMIDAADIKwgeAcRk0iSzTp3M9u4169bN7OabKTgAAIC8hOARCeePP/6wSy+91I444ggrUqSI1a1b17799tvQ54FAwPr27WsVKlRwn7dq1cqWLVuWah4bNmywbt26WcmSJa106dLWo0cP27ZtW6ppFixYYM2aNbPChQtb5cqV7fHHHz9k65jTqHg7dzbbudOsY0ez1183S0qK91IBAADgUCJ4RELZuHGjnX766VagQAH7/PPP7YcffrCnnnrKDjvssNA0CvKee+45GzJkiM2aNcuKFStmbdu2tZ2KbP6lwHHx4sU2YcIEGzNmjE2dOtWuvfba0OdbtmyxNm3aWNWqVW3u3Ln2xBNPWL9+/Wzo0KGHfJ1zQgc5V19ttnWrWdOmwXaOBQvGe6kAAABwqCUf8r8IpOOxxx5zWcBhw4aF3qtevXqqrOOzzz5r999/v3VSHUoze/PNN61cuXI2atQou+iii+zHH3+0cePG2Zw5c+ykk05y0zz//PPWoUMHe/LJJ61ixYo2fPhw2717t73++utWsGBBq1Onjs2fP9+efvrpVEEmzN57z2zqVLPChc2GDzcrVIhSAQAAyIsIHpFQPv30U5dFPP/88+2rr76yo446ym644Qa7RmNDmNnKlStt7dq1rqqqp1SpUta4cWObOXOmCx71v6qqeoGjaPp8+fK5TGWXLl3cNM2bN3eBo0d/V8Grsp/hmU7Prl273CM8eykpKSnukZW8+WX1fDPqu+/MevTQYSLJbrttr1WosM/ivEgJW1Y5AWVFWbFdsQ/mFNl5vOJ8AWQewSMSyooVK+zll1+222+/3e69916XPbzllltckNe9e3cXOIoyjeH02vtM/5ctWzbV58nJyXb44YenmiY8oxk+T30WLXgcNGiQ9e/f/4D3x48fb0WLFrXsoGq38bJ9e7LdeuuZtmNHAWvYcJ2ddNIsGzs2YIkqnmWV01BWlBXbFftgXj5ebd++PcvnCeQVBI9IKPv27XMZw0ceecS9btiwoS1atMi1b1TwGE99+vRxQW145lFVbNV2Uh3zZPVdUZ0wW7du7dp/xsN11+W3P//MZ0cfHbDx4w+3UqXaWyJKhLLKKSgryortin0wp8jO45VXcwhAxhE8IqGoB9XatWuneq9WrVr20Ucfuefly5d3/69bt85N69HrBg0ahKZZv359qnns2bPH9cDqfV//6zvhvNfeNJEKFSrkHpF0UsuuoCU7552ezz4zU7NT9aj6xhtJVqZM4gdl8SqrnIiyoqzYrtgH8/LxinMFkHn0toqEop5Wly5dmuq9n376yfWKKqpqquBu4sSJqe4gqi1jkyZN3Gv9v2nTJteLqmfSpEkuq6m2kd406oE1vN2D7nDWqFEjapXVvGTDBrN/m5har15mzZrFe4kAAACQCAgekVBuu+02++abb1y11eXLl9s777zjhs+48cYb3edJSUnWq1cve+ihh1znOgsXLrTLL7/c9aDaWQMR/pupbNeunetkZ/bs2TZ9+nS76aabXGc6mk4uueQS145S4z9qSI8RI0bY4MGDU1VLzasUMK5ZY1ajhtnDD8d7aQAAAJAoqLaKhHLyySfbyJEjXfvCAQMGuEyjhubQuI2e3r172z///OOG1FCGsWnTpm5ojsIaS+JfGopDAWPLli1dL6tdu3Z1Y0OG99Cqjm4UlDZq1MjKlCljffv2zfPDdMyaZfbWW8Hqqv/7n1mRIod8EwAAAECCInhEwjnnnHPcIy3KPiqw1CMt6llVWcv01KtXz77++uuDWtbcZOdOs549g8+vuMLs3xq+AAAAgEO1VQCh6qrz55sdeSTVVQEAAHAggkcA9u67Zv/9b7C66vDh6vWWQgEAAEBqBI9AHrd8udm11waf33+/WevW8V4iAAAAJCKCRyAP271bPc+abdtm1ry52YMPxnuJAAAAkKgIHoE8bNAgszlzzDS05dtvm+XPH+8lAgAAQKIieATyqO++298xzssvm1WuHO8lAgAAQCIjeATyoO3bzS64wCwlxaxz5+BzAAAAID0Ej0Ae1K+f2c8/m1WqZPb668FeVgEAAID0EDwCeczcuWZPP72/uqraOwIAAAB+CB6BPGTLlmAV1b17g/+fc068lwgAAAA5BcEjkIfcfbfZihVm1aqZDRkS76UBAABATkLwCOQRX321P2AcNozqqgAAAMgYgkcgj/SuevXVwefXXWfWokW8lwgAAAA5DcEjkAc8+6zZ8uVmRx1l9thj8V4aAAAA5EQEj0Au988/weBRHn3UrFSpeC8RAAAAciKCRyCXe+QRsz//NKte3eyii+K9NAAAAMipCB6BXGzpUrMnngg+f+ops+TkeC8RAAAAciqCRyCXCgTMbr7ZLCXFrH17s86d471EAAAAyMkIHoFc6sMPzSZMMCtUyOz5582SkuK9RAAAAMjJCB6BXGjrVrPbbgs+v+ces2OOifcSAQAAIKcjeARyoQEDzP74w+zoo83uvjveSwMAAIDcgOARyGUWL94/NIeqqxYpEu8lAgAAQG5A8Ajksk5ybrjBbM+eYAc5HTrEe4kAAACQWxA8ArnI8OFmU6cGs41e9hEAAADICgSPQC6xZYvZnXcGnz/wgFnVqvFeIgAAAOQmBI9ALvHCC2br1pkdd5zZHXfEe2kAAACQ2xA8ArnA+vVmTz4ZfN6vn1nBgvFeIgAAAOQ2BI9ALnDXXWYbN5rVr2924YXxXhoAAADkRgSPQA43f77ZW28Fnw8dapY/f7yXCAAAALkRwSOQw91zT3CIjosuMjvllHgvDQAAAHIrgkcgBxs/3uyLL8wKFDB76KF4Lw0AAAByM4JHIIdKSTG79dbg8xtvNDvmmHgvEQAAAHIzgkcgBw/NsWSJ2ZFHmj34YLyXBgAAALkdwSOQA/39t9mAAcHnjzxiVrp0vJcIAAAAuR3BI5ADKXDctMmsXj2zK6+M99IAAAAgL0iO9wIAyJhly8xeein4/KmnGJoDADyBQMD27Nlje/fuTchCSUlJseTkZNu5c2fCLmNuKKv8+fO77yYlJWXb8gF5FcEjkMPcfbfZnj1mHTqYtWoV76UBgMSwe/duW7NmjW3fvt0SObgtX768/fbbbwQ22VxWRYsWtQoVKljBggUz+3MBiILgEchBpk41GznSLF8+s8cfj/fSAEBi2Ldvn61cudJlnCpWrOgChkTMOmk5t23bZsWLF7d8OpAjy8tKQaduJPz5559umzjuuOMoayALETwCOcS+fWZ33BF8fs01ZnXqxHuJACAxKFhQsFG5cmWXcUpUWkYta+HChQlosrGsihQpYgUKFLBVq1aF5gEga3DbC8ghRoww+/Zbs+LFzfr3j/fSAEDiIZsHtgUgexE8AjmA2jj267e/zWO5cvFeIgAAAOQ1BI9ADvDuu2Y//WR2xBFmt94a76UBAOQlV1xxhXXu3DneiwEgARA8Ajkg66hxHeXOO81KlIj3EgEAsiooU8c+3uOII46wdu3a2YIFC7KsgPv162cNGjSIabrwZfEeX375pQ0ePNjeeOON0LQtWrSwXr16ZdkyAsg5CB6BBPf222bLl5uVKWN2003xXhoAQFZSsKghRvSYOHGiG5/wnHPOiUsh16lTJ7Qs3qN58+ZWqlQpK126dFyWCUBiIXgEElhKitnAgcHnvXsHO8sBAOQehQoVcuMZ6qEM4T333OPGNtRQEx69vuCCC1wAd/jhh1unTp3sl19+CX0+ZcoUO+WUU6xYsWJumtNPP931NKpsYf/+/e37778PZRLDM4iRFLh6y+I9NOxJeLVVPf/qq69cNtKbp5ZFy6DnCoBPOukk1+vtaaedZkuXLk31Nz755BM78cQTXQ+oRx99tFu+Papi8+8wG8qAVqlSxfWYWqtWLbs1rK3GSy+95Ibe0HfLlStn5513Xpb+FgD8MVQHkMCGD0+yFSvMypY1u+GGeC8NAOQcgYDZ9u2H/u9qpJDMDjGpcQ3ffvttO/bYY10VVklJSbG2bdtakyZN7Ouvv3YB3kMPPRSq3qoeZhXYXXPNNfbuu++6oSlmz57tArkLL7zQFi1aZOPGjXPVT0VZxIOhoPGnn36yE044wQb826biyCOPDAWz9913nz311FPuvZ49e9pVV11l06dPd59p+S+//HJ77rnnrFmzZvbzzz/btdde6z578MEH7aOPPrJnnnnG3nvvPRc46vPlqnpj6m38W7vlllvsrbfeckHphg0b3PwAHFoEj0CCSklJskceyR/qYbVYsXgvEQDkHAoc41FbY9u2jB2vx4wZY8X/XdB//vnHKlSo4N7zhh0ZMWKEG/Pw1VdfdQGhDBs2zGUYle1Tlm/z5s2uqusxxxzjPlfg5dG8vYyin4ULF4aWRWrXru0C0XAKPpWNVGYx2jwffvhhO+OMM9xzZVHPPvts27lzp8sWKsuo97p37+4+V+Zx4MCB1rt3bxc8/vrrr26erVq1svz587t1PPPMM920+kyZVa1niRIlrGrVqtawYcPYCxpAlqDaKpCgvvmmov3yS5IblqNnz3gvDQAgOyg4mj9/vnsoUFOWsX379q7aqajKqbJvCpgU2OmhqqsKyJSZ03NVJdX3Onbs6DKDaquYGTVq1Agtix7KBGZUvXr1Qs8VCMv69etD66JspbceeihjquXdvn27nX/++bZjxw4XVCojqSDaq9LaunVrFzDqs8suu8yGDx/uvgPg0CLzCCSoL76o6v5X4KhqUACA2Om4qSzgoZbR47Wyaaqm6lGGUdm9V155xVVPVVXWRo0auWApkqqGeplIVelU9VRlKu+//36bMGGCnXrqqRlaFmUUw5clMwoUKBB67mVKlTkVrYuyj+eee+4B31NmsnLlyq6NpKrYjh8/3u68807XzlFtLBU8f/fddy7bqs/69u3r2kfOmTOHznyAQ4jgEUgwmzaZffllki1adKTlyxewHj0y2XgGAPIwxS05sbq/Ai5VWVUGTtS5jALCsmXLWsmSJdP8nqpw6tGnTx/XPvKdd95xwaMCwr1792bpMmZ2nloXBYfpBajqKEcZVFV3VftIdQSk6rT6rqrfqkqrHqrmqmqtkyZNihqMAsgeBI9Aghk8WONtBXfNCy4IWOXKBI8AkFvt2rXL1q5d655v3LjRXnjhBZehUwAl3bp1syeeeML1sKoqn5UqVXJVWj/++GPXVlAd6gwdOtT+85//WMWKFV1wtmzZMhd4SbVq1WzlypWuGqq+qwyeeng9GJrnrFmzXCc5XjXaWChbqDaL6k1VPaUqSFZVVnXqoyyreoJVUNq4cWOXiXz//fddMKnqqqrCumLFCjd0yGGHHWZjx451GU1VtQVw6NDmEUgwycmqvhOwI47YYffdl7V3iwEAiUVVTdU2UA8FTaqG+cEHH1iLFi3c5+qYZurUqS7gUoZNneH06NHDtXlUJlKfL1myxLp27WrHH3+8ayt444032nXXXee+r/fVM6vaVqqaq3pkPViqTqoObdShjuapzmxioXaZCgJV7fTkk092mVH1rqrgUJRJVHVdDTWiYUtUXVVDe6jnWX2mgPmss85yZTBkyBC3LhqbEsChkxTQoDoAMmzLli2uXYp6uUuvKlFm6E6y7qp26NAhVfsRUFZsV4cG+2DOKisFUsquVa9e3WWsEpUyZTp36Jzh9aaK7Cmr9LaJ7Dx/A7kdRy4AAAAAgC+CRwAAAACAL4JHAAAAAIAvgkcAAAAAgC+CRwAAAACAL4JHJJR+/fq5AZLDHzVr1kzVe5q6IFe33RpbSl2Qr1u3LtU81GW4BhdW9+UaVPmuu+6yPXv2pJpmypQpbsBhjXWlwYo1thQAIGejA3mwLQDZi+ARCUdjNq1Zsyb0mDZtWuiz2267zUaPHu3GwNL4T6tXr3bjXnk0uLACx927d9uMGTPsf//7nwsMNTCxR113axqNeaVBk3v16mVXX321ffHFF4d8XQEAB88bImT79u0UJ1JtCwx3BWSt5CyeH3DQkpOTrXz58ge8r/GYXnvtNXvnnXfcIMEybNgwN1jwN9984wYb1sDDP/zwg3355ZdWrlw5N8jwwIED7e6773ZZzYIFC7qBhTXu01NPPeXmoe8rQNVAxRrAGACQs2jAeg0iv379evdaNU9UcyURxy7UzU3VomGcx+wpK2WfFThqW9A2oW0DQNYheETCWbZsmVWsWNEN6tukSRMbNGiQValSxebOnesGo27VqlVoWlVp1WczZ850waP+r1u3rgscPQoIr7/+elu8eLE1bNjQTRM+D28aZSDTs2vXLvcIH2RYtEx6ZCVvflk939yIsqKs2K7YB0XNGVT7JLIpQyJRYKNgSOe3RAxuc1NZlSxZ0m0T0c6jnFuBzCN4REJp3Lixq2Zao0YNV2W1f//+1qxZM1u0aJGtXbvWZQ51JzGcAkV9Jvo/PHD0Pvc+S28aBYM7duywIkWKRF02BbFankjKduoud3aYMGFCtsw3N6KsKCu2K/ZBUaBBtilv002E9Nq/Ur0ZyDyCRySU9u3bh57Xq1fPBZNVq1a1999/P82g7lDp06eP3X777aHXCjYrV65sbdq0cXc4s5LuiioYat26Ne01KCu2qzhgH6Ss2K5y7z7o1RwCkHEEj0hoyjIef/zxtnz5cncCUfuHTZs2pco+qoqS10ZS/8+ePTvVPLwqTOHTRFZr0msFgOkFqOqZVY9IOqllV4P87Jx3bkNZUVZsV+yDOQXHq/iWFedVIPPobRUJbdu2bfbzzz9bhQoVrFGjRu6AP3HixNDnS5cudUNzqG2k6P+FCxeGOk0Q3blUYFi7du3QNOHz8Kbx5gEAAADgQASPSCh33nmnG4Ljl19+cUNtdOnSxbVdufjii61UqVLWo0cPV3V08uTJrgOdK6+80gV96ixHVIVUQeJll11m33//vRt+4/7773djQ3pZw549e9qKFSusd+/etmTJEnvppZdctVgNAwIAAAAgOqqtIqH8/vvvLlD8+++/7cgjj7SmTZu6YTj0XDSchrrs7tq1q+v5VL2kKvjzKNAcM2aM611VQWWxYsWse/fuNmDAgNA0Gqbjs88+c8Hi4MGDrVKlSvbqq69meJgOrzF+drSdUFsPNejXvKleQ1mxXR167IOUFdtV7t0HvfN2ep3qAIguKcCeA2Q60FWHOQAAIOf57bff3A1kALEjeAQOYgDj1atXW4kSJbJ8vC6vJ1ed2LK6J9fchrKirNiu2AdzCo5XiVFWypts3brVjSmt2kwAYke1VSCTdMLJ7juWOmESPFJWbFfxwz5IWbFd5c59UP0oAMg4brcAAAAAAHwRPAIAAAAAfBE8AglIw4o8+OCDoeFFQFmxXbEPJiqOV5QV2xWQd9BhDgAAAADAF5lHAAAAAIAvgkcAAAAAgC+CRwAAAACAL4JHAAAAAIAvgkcgwbz44otWrVo1K1y4sDVu3Nhmz55tec3UqVOtY8eOVrFiRUtKSrJRo0al+jwQCFjfvn2tQoUKVqRIEWvVqpUtW7Ys1TQbNmywbt26ucGlS5cubT169LBt27ZZbjNo0CA7+eSTrUSJEla2bFnr3LmzLV26NNU0O3futBtvvNGOOOIIK168uHXt2tXWrVuXappff/3Vzj77bCtatKibz1133WV79uw5xGuTvV5++WWrV69eaNDxJk2a2Oeffx76nHJK26OPPur2xV69elFeEfr16+fKJvxRs2ZNyikNf/zxh1166aXueKTjd926de3bb78Nfc7xHUhsBI9AAhkxYoTdfvvtbpiO7777zurXr29t27a19evXW17yzz//uHVXIB3N448/bs8995wNGTLEZs2aZcWKFXPlpIt/jwLHxYsX24QJE2zMmDEuIL322mstt/nqq69cYPjNN9+4dU1JSbE2bdq4MvTcdtttNnr0aPvggw/c9KtXr7Zzzz039PnevXtd4Lh7926bMWOG/e9//7M33njDBei5SaVKlVwQNHfuXHexetZZZ1mnTp3cdiKUU3Rz5syx//73vy7wDkd57VenTh1bs2ZN6DFt2jTKKYqNGzfa6aefbgUKFHA3bn744Qd76qmn7LDDDgtNw/EdSHABAAnjlFNOCdx4442h13v37g1UrFgxMGjQoEBepcPUyJEjQ6/37dsXKF++fOCJJ54Ivbdp06ZAoUKFAu+++657/cMPP7jvzZkzJzTN559/HkhKSgr88ccfgdxs/fr1bt2/+uqrUNkUKFAg8MEHH4Sm+fHHH900M2fOdK/Hjh0byJcvX2Dt2rWhaV5++eVAyZIlA7t27QrkZocddljg1VdfpZzSsHXr1sBxxx0XmDBhQuCMM84I3Hrrre59tqv9HnzwwUD9+vWjlh/llNrdd98daNq0aZr7I8d3IPGReQQShLI+yoioCqYnX7587vXMmTPjumyJZOXKlbZ27dpU5VSqVClXxdcrJ/2vqqonnXRSaBpNr/JUpjI327x5s/v/8MMPd/9rm1I2Mry8VKWuSpUqqcpLVcfKlSsXmkaZ3C1btoSycrmNsq3vvfeey9Cq+irlFJ2y2spKh28/QnmlpmrzqmZ/9NFHu1oPqgZOOR3o008/dcfl888/31WPb9iwob3yyiuhzzm+A4mP4BFIEH/99Ze7oA2/gBe9VrCEIK8s0isn/a8Lk3DJyckuoMrNZblv3z7XJk3Vwk444QT3nta3YMGCLphOr7yilaf3WW6ycOFC1+6zUKFC1rNnTxs5cqTVrl2bcopCwbWqz6tdbSS2q/1040rVvMeNG+fa1SoAatasmW3dupVyirBixQpXRscdd5x98cUXdv3119stt9ziqsp725VwfAcSV3K8FwAAkHVZokWLFqVqb4XUatSoYfPnz3cZ2g8//NC6d+/u2oEitd9++81uvfVW145WnXchbe3btw89V7tQBZNVq1a1999/33UIg9Q3uJR5fOSRR9xrZR51zFL7de2LABIfmUcgQZQpU8by589/QC+Yel2+fPm4LVei8coivXLS/5GdDKnnUPXAmlvL8qabbnIdA02ePNl1DOPR+qpK9KZNm9Itr2jl6X2WmygLe+yxx1qjRo1cRk0dMw0ePJhyiqBqqdqHTjzxRJe110NBtjqq0nNlhtiuolOW//jjj7fly5ezXUVQD9nK9IerVatWqJovx3cg8RE8Agl0UasL2okTJ6a6S6vXapOFoOrVq7sLjPByUts8tWX0ykn/K1jSBbBn0qRJrjyVFchN1KeQAkdVv9Q6qnzCaZtSz4bh5aWhPHSxFl5eqs4ZHnAr46ThLCIv9HIbbRO7du2inCK0bNnSbRPK0noPZYzUns97znYVnYYE+vnnn12gxP6XmqrURw4l9NNPP7lMrXB8B3KAePfYA2C/9957z/Ua+sYbb7geQ6+99tpA6dKlU/WCmReoh8d58+a5hw5TTz/9tHu+atUq9/mjjz7qyuWTTz4JLFiwINCpU6dA9erVAzt27AjNo127doGGDRsGZs2aFZg2bZrrMfLiiy8O5DbXX399oFSpUoEpU6YE1qxZE3ps3749NE3Pnj0DVapUCUyaNCnw7bffBpo0aeIenj179gROOOGEQJs2bQLz588PjBs3LnDkkUcG+vTpE8hN7rnnHtcL7cqVK912o9fqgXf8+PHuc8opfeG9rVJe+91xxx1u/9N2NX369ECrVq0CZcqUcT0fU06pzZ49O5CcnBx4+OGHA8uWLQsMHz48ULRo0cDbb78dmobjO5DYCB6BBPP888+7C/2CBQu6oTu++eabQF4zefJkFzRGPrp37x7qzv2BBx4IlCtXzgXbLVu2DCxdujTVPP7++28XLBYvXtwNOXHllVe6oDS3iVZOegwbNiw0jYLqG264wQ1LoQu1Ll26uAAz3C+//BJo3759oEiRIu7CVxfEKSkpcVij7HPVVVcFqlat6vYtBcfabrzAUSinjAWPlFfQhRdeGKhQoYLbro466ij3evny5ZRTGkaPHu1uVunYXbNmzcDQoUNTfc7xHUhsSfon3tlPAAAAAEBio80jAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAAAAAwBfBIwAAAADAF8EjAAAAAMAXwSMAANnojTfesNKlS1PGAIAcj+ARAJAQ1q5da7feeqsde+yxVrhwYStXrpydfvrp9vLLL9v27dstJ6hWrZo9++yzqd678MIL7aefforbMgEAkFWSs2xOAABk0ooVK1ygqAzdI488YnXr1rVChQrZwoULbejQoXbUUUfZf/7zn7iUbyAQsL1791pycuZOmUWKFHEPAAByOjKPAIC4u+GGG1xw9u2339oFF1xgtWrVsqOPPto6depkn332mXXs2NFNt2nTJrv66qvtyCOPtJIlS9pZZ51l33//fWg+/fr1swYNGthbb73lsoClSpWyiy66yLZu3RqaZt++fTZo0CCrXr26C+rq169vH374YejzKVOmWFJSkn3++efWqFEjF8ROmzbNfv75Z7c8yogWL17cTj75ZPvyyy9D32vRooWtWrXKbrvtNvd9PdKqtqps6jHHHGMFCxa0GjVquOUNp++++uqr1qVLFytatKgdd9xx9umnn2ZDyQMAEDuCRwBAXP399982fvx4u/HGG61YsWJRp/ECsfPPP9/Wr1/vAru5c+faiSeeaC1btrQNGzaEplWQN2rUKBszZox7fPXVV/boo4+GPlfg+Oabb9qQIUNs8eLFLti79NJL3XTh7rnnHve9H3/80erVq2fbtm2zDh062MSJE23evHnWrl07F9T++uuvbvqPP/7YKlWqZAMGDLA1a9a4RzQjR4501XPvuOMOW7RokV133XV25ZVX2uTJk1NN179/fxdIL1iwwP3dbt26pVpPAAAOuQAAAHH0zTffBHQ6+vjjj1O9f8QRRwSKFSvmHr179w58/fXXgZIlSwZ27tyZarpjjjkm8N///tc9f/DBBwNFixYNbNmyJfT5XXfdFWjcuLF7ru/q8xkzZqSaR48ePQIXX3yxez558mS3PKNGjfJd9jp16gSef/750OuqVasGnnnmmVTTDBs2LFCqVKnQ69NOOy1wzTXXpJrm/PPPD3To0CH0Wn///vvvD73etm2be+/zzz/3XSYAALILbR4BAAlp9uzZroqpMm67du1y1VOV/TviiCNSTbdjxw6XbfSoumqJEiVCrytUqOCylbJ8+XLX+U7r1q1TzWP37t3WsGHDVO+ddNJJqV7rb6tarKrRKqu4Z88e97e9zGOslMm89tprU72n9p6DBw9O9Z6ynR5lZFVN11sPAADigeARABBX6l1V1VKXLl2a6n21eRSvsxkFbwoE1SYxUnibwgIFCqT6TPNWEOrNQxQAqhOecGrbGC6yCu2dd95pEyZMsCeffNIts5brvPPOc4FndkhvPQAAiAeCRwBAXCmTqEzgCy+8YDfffHOa7R7VvlHDeahjHWUXM6N27douSFS28IwzzsjQd6dPn25XXHGF68TGC0R/+eWXVNOoAxz1zJoedQakeXXv3j3VvLVsAAAkMoJHAEDcvfTSS67qpqqKqmqoqmzmy5fP5syZY0uWLHG9nrZq1cqaNGlinTt3tscff9yOP/54W716tcsiKqCLrGYajaqzKoOoTnKUxWvatKlt3rzZBW+qFhoe0EVSj6fqFEed5CgL+MADDxyQCVRQO3XqVNfDq4LUMmXKHDCfu+66y3WEo2qyWqfRo0e7+Yb33AoAQCIieAQAxJ2GrVAPphrjsU+fPvb777+74EvZOAV7GspDAdvYsWPtvvvuc72T/vnnn1a+fHlr3ry5Gz4jVgMHDnRDfajXVY0vqSqvymree++96X7v6aeftquuuspOO+00FxTefffdtmXLllTTqKdV9Z6q9VE7zWDfN6kp+FX7RlV/Va+rGjJk2LBhbqgPAAASWZJ6zYn3QgAAAAAAEhvjPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAMzP/wEV6waoFevX8wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 500\n",
      "Population size: 3000\n",
      "Best chromosome: 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\n",
      "Best fitness: 7394\n",
      "Total cost = population size * number of generations: 1500000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 500\n",
      "Population size: 4000\n",
      "Best chromosome: 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\n",
      "Best fitness: 7468\n",
      "Total cost = population size * number of generations: 2000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generation 400\n",
      "Population size: 5000\n",
      "Best chromosome: 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\n",
      "Best fitness: 7467\n",
      "Total cost = population size * number of generations: 2000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Population sizes and normalized realized plateau height\n",
      "population size 10: 0.6032\n",
      "population size 20: 0.6371\n",
      "population size 50: 0.6566\n",
      "population size 100: 0.6763\n",
      "population size 300: 0.6964\n",
      "population size 500: 0.7091\n",
      "population size 2000: 0.7348\n",
      "population size 3000: 0.7394\n",
      "population size 4000: 0.7468\n",
      "population size 5000: 0.7467\n"
     ]
    },
    {
     "data": {
      "image/png": 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o4sLOmTB3wXZxFXasNYDL//9Kj5X+scJ5iCsA6yBzBcDy9ARegwsdCqPzZnr37m3+aq5/wdUTNZ3TodmEktD1kHSon64tpUO87KXY9a/Sv//+u2M/Day0TLIGOR07djQn13pSrGWQtTCEZtPyBw/non/J1+fT4Effg7ZBT850CJvep6XI7XTYj5YW1/ep82m0Deei7Rs7dqw5cdTn1mFxmsXSYXEXX3yxxxdn1oyHtjs/Hcqk2//5z3+agEazMVqAQctW61An+wl1adJAcuLEieY4aBZQ5wLpSbAOW3P+nItDAyEdiqVDIPMXPNDhm1pwRd+TzoHS46/vWYfe6YmwZqO05L0OI9ThdNp/dT/t15q5GjhwoNlHM2olmXN1PnTOjmattOy3Hh99XX1vWkxEA3g7zRLrNp33p9kdzbJqX9SLHkNdikDXr9JsqrZZ5wNqgHbgwAHHOlb6Ws8884xZQkFLzmsBEP0DRv73qEsk6HwiPW72jK6WzNc/npSG4n5O7ui+OrdLs0iardJS7Lq+l7b7fOfyFXas9f1fc8015g9Mer8OQ9b26bHW7yIAFuTtcoUAcK5S7M5lqrV8cWhoqC08PNyUgn7sscdshw4dKrQceGHlqNXvv/9utmnJ87i4ONu///1v2/vvv19oSWgtB67l13X/xo0b2+68807br7/+6lJ2WcukF1bC3JmWw9byyi1atDBl02vWrGm79tprbWvXri3w+Jdeesk8/vnnny9RZ9HS6/r8gYGBpsz2iBEjTGlnd20raSn2/PQ46n35j72Wsn/wwQdNuXJth5bp1vftXFZd6WP1cy+sxPYvv/xSrLa4+wz0M9XX1fLfejz0Od19JucqxW5/jLtL/r6l5bovu+wy0xa96Ovq+9u6davLfv/5z39sDRs2NG3r3LmzWULAXV91p7C+nl/+59Njr31JH6+vq2XBf/jhhwIlx9WKFSvM/znto/lLhe/cudM2ZMgQW0xMjPls9f/Q9ddfb/v6669dSrHrEgixsbHm/2337t1tK1eudPse9fl69uxp2qT99R//+IdtwYIFJSrFnr+fOB8D51LsJfmc3B0X7XeDBg0y30P6naDfBT///LNpwxdffHFe3wnujnVSUpJpj7ZLn0dfq2vXrrYvv/yyyOMBwHv89B9vB3gAgMJpZk6r8elf2N1VQgTgfTrUVLOBy5cvNxltABUTwRUAWJj+/UuH02nZ6qLW3gFQdnRtN+fiOjo/TIcr6/plCQkJHiu8A8D3MOcKACwoLS3NzOPQgErnqeg8JQDWMHr0aBNgdevWzRSW0DlsK1asMIVvCKyAio3MFQBYkA4B1DLRuiDsfffdJ88995y3mwTgDF0jTUuha0ELreanBVJGjBjhUowGQMVEcAUAAAAAHsA6VwAAAADgAQRXAAAAAOABFLRwQxfzPHTokFlQ8HwWAwQAAABQfir3pqSkSO3atcXfv+jcFMGVGxpY1a1bt7Q+HwAAAAA+Zv/+/VKnTp0i9yG4ckMzVvYDGBERId6SnZ0t8+fPN2tnBAYGeq0d8B30GdBnwHcNrIjfT/DlPnPy5EmTeLHHCEUhuHLDPhRQAytvB1dhYWGmDd7uVPAN9BnQZ8B3DayI308oD32mONOFKGgBAAAAAB5AcAUAAAAAHkBwBQAAAAAeQHAFAAAAAB5AcAUAAAAAHkBwBQAAAAAeQHAFAAAAAB5AcAUAAAAAHkBwBQAAAAAeQHAFAAAAAB5AcAUAAAAAHkBwBQAAAAAeQHAFAAAAAB5AcAUAAIBSk5tnk9W7j8naJD/zU28D5bXPVPJ2AwAAAFA+zd0YL+O/3yzxJzJEJEA+3v6rxEaGyLj+LaVv61hvNw8WNNfH+wyZKwAAAJTKSfKIT9adOUk+K+FEhtmu9wPlrc+QuQIAAIBH6TAuzT64G8xl3zb22z/EZhMJ8Pczt/38zvw010/v4/hptp658+wPl8fY97fve/ax9jtdn8vPzXMV3Ob6YHePKdDmfPs6nsHN/YU9pjiv7+69F+v19X2e4zFFHbOijr3bz6qYj9E+8/SsTYX2Gd1V+1SvljGOPmNFBFcAAAC4IMfTsmRXUqrsPJImO4+kyq97jhfIPhR4THq2jPh0HUcexaIBlvapNbuPSbfGNcSqCK4AAABwTtm5ebL3aLrsOpIqu5LSTv88E0xpoHQ+6tcIk+qVg0wGSzmyFmc26L9n7zuzzX4732Ns9g2uT+HyuPz7nn09989lc/Nc+V9fivEY9+09e6uw91Lgudy0uzhtdvcaUoLjk/+9elNiStFBu7cRXAEAAMBxUn3MZKFOB0+aibIHUfuOpUtOEVXbtOhAo5qVpXHNKmYI10cr957zqL5wc1tLZyHgniP4KiIotBUS3J19Dtf7V+86JsM+/OWchzw6PMTSHwvBFQAAQAWTlZMn+45p1ul05kmDJ3swdeJU4Vmo0MAAE0A1qllFGkVVlsbRp3/qtrCgs6eVOn9m/ubDphCBu3BMg6+YyBDp0rB6Kb1DlCbH3KkCU5/8zvs5r2hW0wTovt5nCK4AAADKIc0iHE3Lkp2JBYfx7T9+qsi1g+KqhjqyUCaYijr9MyYiRPyLUUxACw5o6Wyt8KZ7O7+S/dF6v5ULE6BsBZSTPkNwBQAA4MMyc3Idc6HyZ6JOZuQU+rjKQZqFOhs8NY4+/bNhVGUJDQq44HbpmkRTbu/otGbRaTE+tGYRylbfctBnCK4AAAB8IAt1JDVTdiammap8zsP4DhxPl8KSUDps63QWqoo0PjOcr7EZxldFakUEO4Z3lRY9GdbS2St3JMr8n1ZL78u7Srcm0ZbPPsB7+vp4nyG4AgAAsIiM7FzZc1QDp4IFJVIyC89CVQmu5AieTs+BOp2JalCjsoQEXngW6kLoSXHXhtXl6J8289NXTpLhPQE+3GcIrgAAAMo4C5WYkmmG7zkHT5qROnD8VKFlr/X8sk61sALD+DSoqhle+lkoAOdGcAUAAFAKTmXlyu6kgsP4dFtqEVmo8BDNQp2eC9XYqSpfvephXs9CASgawRUAAMAFZKESTma4BE/2ghKHThSdhdJgqWBJ8yoSVSWILBTgowiuAAAAziE9K+fM0D3XkuaahUrPyi30cZGhgQVKmuswvno1wiS4ElkooLwhuAIAABCRvDybxJss1NngyZ6ROuRUFjo/nWxf32ShzhSScCosUb0yWSigIiG4AgAAFUpaZo7JOOUvKKHbTmUXnoWqFhZYIHjSnzq8L6iSf5m+BwDWRHAFAADKZRbqYPKpAsP49KfOkSpMJc1C1TgzF0qH8zlV5atWOahM3wMA30NwBQAAfJZW3csfPOlPXSsqIzuv0MfVqBxUYC6U/qxbPUwCA8hCATg/BFcAAMDScvNscij5lOxwmgNlD6J0vajCBAb4mUV07XOh7FX5NBsVGRZYpu8BQMVgieBq8uTJ8vLLL0tCQoK0a9dO3nzzTenSpYvbfa+88kpZunRpge3XXXedzJ49u8D2e++9V95++2157bXX5IEHHiiV9gMAUJECndW7j8naJD+psfuYdGsSbQo6eMLJjOwCwZOZC3U0TbJyCs9CRVUJdmShTs+HOp2JqlMtVCqRhQJQkYKr6dOny0MPPSRTp06Vrl27yqRJk6RPnz6ydetWiY6OLrD/t99+K1lZWY7bR48eNQHZLbfcUmDf7777TlatWiW1a9cu9fcBAEB5N3djvIz/frPEm8p5AfLx9l8lNjJExvVvKX1bxxY7ODtwPN0RPDkKSiSlyZEislBBAf7S0BSQOBs8aRZKt2m5cwCwAq8HVxMnTpThw4fLsGHDzG0NsjQDNW3aNHniiScK7F+9enWX21988YWEhYUVCK4OHjwoo0ePlnnz5km/fv1K+V0AAFD+A6sRn6yT/GviJpzIMNun3N7RJcA6kZ4tO5MKDuPbezRdsnILz0JFh5/OQp2uyne2qERctVCPZcgAoFwGV5qBWrt2rYwdO9axzd/fX3r27CkrV64s1nO8//77ctttt0nlypUd2/Ly8uSOO+6QRx99VFq1anXO58jMzDQXu5MnT5qf2dnZ5uIt9tf2ZhvgW+gzoM+gNGi26elZmwoEVsq+7dGvf5f/bU6QPUfTZXdSuhxNOzvKJL/gSv7SsEaYyTqZbFTU2evhIe5PTfJycySv8CrpsDh+P8GX+0xJ2uDV4CopKUlyc3OlVq1aLtv19pYtW875+DVr1sjGjRtNgOXsxRdflEqVKsn9999frHZMmDBBxo8fX2D7/PnzTVbM2xYsWODtJsDH0GdAn8GF0uRSSrbIyWyRLcl+knAyoMj9UzJy5Ot1h1y2RQbZJDrEJtGhIrVCT//U29WCRfz9NPhKFtEfh0QO6IWPrdzj9xN8sc+kp6f7zrDAC6FBVZs2bVyKX2gm7PXXX5d169aJn1/xhg9o5kznfTlnrurWrSu9e/eWiIgI8WaUrB2qV69eEhjIeHLQZ8D3DC6MzWYzpcsTU7IkKTXTzHE6kpp1+ueZ67pdK/AdTy/5X4t7XxQtfVrVMlX5GkSFSZVgnz7NgAdxTgNf7jP2UW3F4dVvvaioKAkICJDDhw+7bNfbMTExRT42LS3NzLd65plnXLb/9NNPkpiYKPXq1XNs0+zYww8/bIpl7Nmzp8BzBQcHm0t++kF6+8O0UjvgO+gzoM9ULNm5eXI0NUsSUzIcgVKi4+eZbRo0ncyUzCKq7rlbUFcr8YUFBZiCE+cy7LJG0q1xjQt8NyjP+P0EX+wzJXl9rwZXQUFB0qlTJ1m4cKEMGDDAMV9Kb48aNarIx3711VdmntTtt9/usl3nWumcLWdafVC324tmAABgdZplSsnMOR0gnbQHRxnmpyPTdOZyLD1LbO4mRBVC5zXVDA82xSNqhodIzSrBEh0RfPanbq8SLNXCgsTf38/MubrsxUWmeIW7l9FxIjGRIdKloWvRKQCoaLyer9fheEOHDpXOnTub4X2aXdKslD0QGjJkiMTFxZl5UfmHBGpAVqOG61/I9Hb+bRptaiasefPmZfCOAAA4d5bJOatkzzI5tp0JoDKyS55lOhs05b8eYq7rPqFBRc+fyk+r9Gm5da0KqIGUc4BlH4Cv91PND0BF5/XgauDAgXLkyBF56qmnzCLC7du3l7lz5zqKXOzbt89UEHSma2AtX77cFJwAAMCKWSaXIXkeyDKdziqdzjI5B032jJM9y1RatMy6lls/u87VaTElXOcKAMozrwdXSocAFjYMcMmSJQW2aQZKf5EVl7t5VgAAnE+WqcB8pvPIMmmGxx4kuQRKLhmnkPPKMpUmDaB6tYyRlTsSZf5Pq6X35V2lW5NoMlYAYKXgCgAAb2SZ3BZ+uJAsU3AlqXkmk2QPkNwN0yvtLFNp0sCwa8PqcvRPm/nJUEAAOIvgCgBQLuTk5kmSvax4asbp4XnO85lSzwZQJc0yRVUJcgRKzkUfHEFTldP3WSnLBAAoewRXAAAfWJcpX6DkJtN0XlmmfMPwChaBCJbqPpxlAgCULYIrAKigtLz26t3HZG2Sn9TYfaxM585oluloWtaZ4g9ns0z2tZguNMvkCJbylRY/XfyBLBMAoHQQXAFABTR3Y7xT1bcA+Xj7rxJ7gVXf8meZiso0nW+WKcql+EPBTBNZJgCANxFcAUAFDKx0vaL8sY0uEKvbtdy2c4CVP8vkuqita9nx880ymaySvfhDvoIQUeFBEhbErysAgPXx2woAKthQQM1YuUsa2bc9OH2DfLJqrykOkZSaaQKrkmSZqgRXOr1YrZvS4s5ZJq2YR6U5AEB5QnAFABXImt3HXBaAdedUdq4s33HUZZtOxdI1l+xZJXdD8sgyAQAqOoIrAKgAdD7UhgMnZMqSHcXaf1CXutKndayjCARZJgAAzo3gCgDKsV1HUmXm+kMyc/1B2XM0vdiP698uTro1rlGqbQMAoLwhuAKAckYLS3y/Id4EVL8fOOHYHhoYIL1aRstP25MkOT3b7bwrLcQeExkiXRpWL9M2AwBQHhBcAUA5kJKRLfM2HTYB1c87kiTvTOSkBSMubxolA9rHSa+WtaRycCVHtUANpJwDLPsKV1qOnUITAACUHMEVAPiorJw8WbI10Qz7+9+fhyUz52wZ9I71qsqN7eOkX9tYU4jCmZZZ13LrZ9e5Oi3mAte5AgCgoiO4AgAfkpdnk1/2HJMZ6w/JnD/i5cSpbMd9jWtWNhkqDarq1Qgr8nk0gOrVMkZW7kiU+T+tlt6Xd5VuTaLJWAEAcAEIrgDAB/wZf9JkqL7fcEgOJp9ybNcy6De0qy0DOsRJq9oR4udnH9x3bjr0r2vD6nL0T5v5yVBAAAAuDMEVAFiUBlE6h2rmb4dk6+EUx/bw4EpybZsYk6Xq2qgGQREAABZBcAUAFpKcniWz/4g3AdWaPccc24MC/OWqFjVNQHVVi2gJCQzwajsBAEBBBFcA4GWnsnJNQQod9rd0W6Jk556u4acj/C5pWEMGdKht5khFhgZ6u6kAAKAIBFcA4AU5uXmyYudRmbH+oMzbmCBpWbmO+1rGRpiAqn+72hIbGcrnAwCAjyC4AoAyYrPZzKK+GlDpIr9JqZmO++pUC5Ub29c2w/6a1grnMwEAwAcRXAFAKdudlCYzfjsoszYcMtftqoUFyvVttdJfbelYr1qJKv0BAADrIbgCgFKQmJIhP2yIN9X+Nhw44dgeGhggvVrWMgHV5U1rSmCAP8cfAIByguAKADwkJSNb5m3SwhQH5ecdSZJ3ui6FKZV+edMoM+yvd8sYqRzMVy8AAOURv+EB4AJk5eTJ0m1HzDyq/20+LJk5eY77OtSrauZQ9WsbK1FVgjnOAACUcwRXAFBCeXk2+WXPMZmx/pDM+SNeTpzKdtzXqGZlE1Bplqp+jcocWwAAKhCCKwAopi0JJ2XGb4fk+w2H5GDyKcf26PBguaGdFqaIk1a1IyhMAQBABUVwBQBF0CBq1vpDZh7VloQUx/bw4ErSt3WMCaguaVTDzKsCAAAVG8EVAOSTnJ4ls/+Il5m/HZI1e445tgcF+MtVLWrKje3j5OoW0RISGMCxAwAADgRXACAiGdm58r8/D5thf0u3JUp27ulSf7r0VNeG1c08qmtbx0pkWCDHCwAAuEVwBaDCysnNkxU7j8rM9Ydk3qYESc3Mcdx3UWyEDGhfW25oX1tiI0O92k4AAOAbCK4AVCg2m01+P3DClE7/fkO8JKVmOu6rUy3UVPnTYX/NaoV7tZ0AAMD3EFwBqBB2J6WZohSapdLrdtXCAs06VDrsr1P9alT6AwAA543gCkC5lZiSIT9siDdB1YYDJxzbQwL9pXfLGJOluqJZTQkM8PdqOwEAQPlAcAWgXNF5U/M2Jphhfz/vSJK803UpTKn0y5pEyYAOtU1gVTmYrz8AAOBZnF0A8HlZOXmydNsRE1D9b/NhyczJc9zXoV5VubFdbbm+XW2JqhLs1XYCAIDyjeAKgE/Ky7PJr3uPm4Bqzh/xkpye7bivUc3KZg6VDvurX6OyV9sJAAAqDoIrAD5lS8JJsxbV9xsOycHkU47t0eHB0r9dbRNUtY6LoDAFAAAocwRXACxPg6hZ6w+ZwhRbElIc28ODK0nf1lqYIk66Na5h5lUBAAB4C8EVAEtKTs+SOX+cLkyxZvcxx/agAH+5snlNGdAhTq5uES0hgQFebScAAIAdwRUAy8jIzpX//XnYDPtbui1RsnPPlPoTka4Nq5uA6rrWsRIZFujVdgIAALhDcAXAq3LzbLJiZ5IJqOZtSjCl1O0uio2QAe1rm7lUtauGerWdAAAA50JwBaDM2Ww2+f3ACZm5/pB8//shOZKS6bgvrmqoqfKnWapmtcL5dAAAgM8guAJQZvYkpZk5VFqcYldSmmN7tbBA6dc21hSm6FSvmvhTmAIAAPgggisApUqzUj/8fkhmrD8kG/YnO7aHBPpLr5YxZtjf5U1rSlAlfz4JAADg0wiuAHiczpuat/F0pb+fdyRJ3pm6FFoqvXuTKBNQ9W4VI1WC+QoCAADlB2c2ADwiKydPlm07YgIqrfiXkZ3nuK993aomoOrXtrbUDA/miAMAgHKJ4ArAecvLs8mve4+bxX1n/xEvyenZjvsaRVU2c6i0OEWDqMocZQAAUO5ZYpLD5MmTpUGDBhISEiJdu3aVNWvWFLrvlVdeKX5+fgUu/fr1M/dnZ2fL448/Lm3atJHKlStL7dq1ZciQIXLo0KEyfEdA+bY1IUVenLtFLn9psdz69kr5dPU+E1hpVuruyxrKrFHdZeHDPWRMz6YEVgAAoMLweuZq+vTp8tBDD8nUqVNNYDVp0iTp06ePbN26VaKjowvs/+2330pWVpbj9tGjR6Vdu3Zyyy23mNvp6emybt06efLJJ83248ePy5gxY+SGG26QX3/9tUzfG1CeHEo+JbM2HJIZvx2ULQkpju06b6pvay1MESfdGtcw86oAAAAqIq8HVxMnTpThw4fLsGHDzG0NsmbPni3Tpk2TJ554osD+1atXd7n9xRdfSFhYmCO4ioyMlAULFrjs89Zbb0mXLl1k3759Uq9evVJ9P0B5kpyeJXP+OF2YYs3uY47tgQF+cmXzaBNQXXNRtIQEBni1nQAAAFLRgyvNQK1du1bGjh3r2Obv7y89e/aUlStXFus53n//fbntttvMEMDCnDhxwgwdrFq1qtv7MzMzzcXu5MmTjiGGevEW+2t7sw3wLZ7oMxnZubJ46xGZtSFelm5PkuzcM6X+RKRLg2pyQ7tY6dOyllQNCzyzNU+ynYpXwLfwPQP6DfiugRVlW+g8uCRt8LPZbGfPnMqYzoOKi4uTFStWSLdu3RzbH3vsMVm6dKmsXr26yMfr3CwdSqj7aWbKnYyMDOnevbu0aNFCPv30U7f7PP300zJ+/PgC2z/77DOTFQPKOy2Vvu2En6xN8pMNx/wkM/fs0L7aYTbpHJUnHaNsUo1CfwAAoIJJT0+XQYMGmYRNRESEtYcFXgjNWmnhisICK40yb731VtH4ccqUKYU+j2bOdN6Xc+aqbt260rt373MewNKk7dchjr169ZLAQHuWAPBMn9H/FxsPnTQZqtkbE+RI6tm5jLUjQ6R/21i5oV2MNKsVziEvx/ieAf0GfNfAirItdB5sH9VWHF4NrqKioiQgIEAOHz7ssl1vx8TEFPnYtLQ0M9/qmWeeKTKw2rt3ryxatKjIICk4ONhc8tMP0tsfppXaAd9RVJ/Zk5QmM9cfMuXTdyWlObbrML9+bWJlQIc46VSvmvhTmKJC4XsG9BvwXQMrCrTAeXBJXt+rwVVQUJB06tRJFi5cKAMGDDDb8vLyzO1Ro0YV+divvvrKzJO6/fbbCw2stm/fLosXL5YaNWqU2nsAfMGRlEz54fdDMmP9IdmwP9mxPSTQX3peVMsUpriiWU0JqmSJ1RkAAAB8kteHBepwvKFDh0rnzp3N8D4txa5ZKXv1QF2jSudlTZgwocCQQA3I8gdOGlj99a9/NeXYf/jhB8nNzZWEhARHpUEN6ICKIDUzR+Zv0kp/h+TnHUmSqxOrtGiMn8hlTWvKgPa1pXerGFNKHQAAABfO62dVAwcOlCNHjshTTz1lgqD27dvL3LlzpVatWuZ+LZ+uFQSd6RpYy5cvl/nz5xd4voMHD8qsWbPMdX0uZ5rF0kWIgfIqKydPNh7zk/lf/i4LtyRKhlMVv3Z1q5qA6vq2tc1ivwAAAChnwZXSIYCFDQNcsmRJgW3Nmzc3k/HdadCgQaH3AeVRXp5N1u47bhb3nfNHvBxP1zWnTmdrG0VVlhvbx8kN7WtLw6jClysAAABAOQmuAJTc1oQUU5RCi1McTD7l2B4RaJObO9eXmzvVlTZxkWaNNwAAAJQ+givAhxxKPiWzNhwyWaotCSmO7Tpvqk+rGLm+TS1J3rparr+uhdcr6wAAAFQ0JQ6utPT5I488UmBx3VOnTsnLL79s5k4B8JwT6dkyZ2O8CajW7Dkm9lGvgQF+cmXzaLmxfW1T8S8kMMAUdJmzjaMPAADgE8HV+PHj5d577y0QXOnKxXofwRVw4TKyc2Xhn4kyY/1BWbI1UbJzz84j7NKwuimdfl2bGKkaRvVLAAAAnw2utFiEuzkcGzZsMKXOAZwfLZW+cudRE1DN25ggKZk5jvtaxIQ7ClPEVQ3lEAMAAPhycFWtWjUTVOmlWbNmLgGWriWVmppqMloASvbHij8OnjBFKb7fcEgSUzId92kQpcGUZqmax4RzWAEAAMpLcKWL++qJ4F133WWG/0VGRjru04V5tQR6t27dSqudQLmy92iazPjtkMzccFB2HUlzbK8aFijXtYk1AVXn+tXEX1f8BQAAQPkKroYOHWp+NmzYUC699FIqkQEllJSaKT9opb/1h2T9/mTH9uBK/tKrZS0TUF3RrKYEVXJdNBsAAADldM5Vjx49JC8vT7Zt2yaJiYnmurMrrrjCk+0DfFpqZo7M35Rghv0t35Fk5lUpTUh1bxJlAqo+rWNMKXUAAAD4thKf0a1atUoGDRoke/fuNcMEnek8LJ1/BVRk2bl5smzbEZOhWrA5QTKyz/4Bol2dSFOY4vp2sRIdHuLVdgIAAMDLwZUWrejcubPMnj1bYmNj3VYOBCqavDybrN13XGauPyizf4+X4+nZjvsaRlU2a1FpUKXXAQAAUD6VOLjavn27fP3119KkSZPSaRHgQ7YdTjGL++qwv4PJpxzbo6oES/92pwtTtK0TyR8hAAAAKoASB1ddu3aVHTt2EFyhwjqUfMqUTddhf3/Gn3Rs13lTfVrFmCzVpY1rSKUAClMAAABUJMUKrn7//XfH9dGjR8vDDz8sCQkJ0qZNmwJVA9u2bev5VgJediI9W+ZsjDdZqjV7jol9umFggJ/0aBYtAzrUlp4X1ZKQwABvNxUAAABWDq7at29vhjU5F7DQ9a7s7PdR0ALlSUZ2rizakmgCqiVbj0hW7tnCFF0aVjcZqn5tYqVqWJBX2wkAAAAfCq52795d+i0BLEBLpa/ceVRmrD8o8zYmSEpmjuO+FjHhpijFDe1rS1zVUK+2EwAAAD4aXNWvX7/0WwJ4iWZdNx48aQIqnUuVmJLpuK92ZIjc0D7ODPtrERPBZwQAAADPFbSYNWuW2+06JDAkJMQUumjYsGFJnxYoc3uPppkqfxpU7TqS5tgeGRoo/drGyo3tasvFDaqLv674CwAAAHg6uBowYECB+Vf5511ddtllMmPGDKlWrVpJnx4oVUmpmfLDmUp/6/cnO7YHV/KXni1rmdLpPZrVlKBKVPoDAABAyZT4DHLBggVy8cUXm58nTpwwF72uJdp/+OEHWbZsmRw9elQeeeSRkj41UCrSMnPku98OyNBpa6Tr8wvl6e83m8BKE1KXN42SV25pJ7/+q6dMHtRRerWsRWAFAACAsslcjRkzRt555x259NJLHduuueYaMyTwnnvukU2bNsmkSZNcqgkCni46sWb3MUlMyZDo8BBTuS8g39C97Nw8WbbtiBn2t2DzYTmVneu4r12dSFOY4vp2sebxAAAAgFeCq507d0pERMGJ/bpt165d5nrTpk0lKSnJIw0EnM3dGC/jv98s8ScyHNtiI0NkXP+WZgHftXuPmzlUs3+Pl+Pp2Y59GtQIMwGVlk9vVLMKBxUAAADeD646deokjz76qHz88cdSs2ZNs+3IkSPy2GOPmeGCavv27VK3bl3PtxZS0QOrEZ+sE9fZfmICrXs/WSc1KgfJ0bQsx/aoKsHSv12sCao0W6XzAQEAAADLBFfvv/++3HjjjVKnTh1HALV//35p1KiRzJw509xOTU2Vf/3rX55vLSr0UEDNWOUPrJxpYBUW6C9928SawhSXNq4hlQIoTAEAAACLBlfNmzeXzZs3y/z582Xbtm2Obb169RJ/f39HRUHAk3SOlfNQwMJMub2T9GgezcEHAACA9YMrpUFU3759zQUoC1q8ojiST52dZwUAAABYLrh64403TCVArQio14ty//33e6ptgENxq/pR/Q8AAACWDq5ee+01GTx4sAmu9HphtGAAwRVKw8UNqklIoL9kZOe573siEhN5uiw7AAAAYNngavfu3W6vA2XlszX7igyslJZjz7/eFQAAAFBWzruUWlZWlmzdulVycnI82yIgnw37k+XfP2w21//aMc6sa+VMM1ZTbu8ofVvHcuwAAADgOwUt0tPTZfTo0fLRRx+Z21oxUMuw67a4uDh54oknSqOdqKCS07Pkvk/XSXauTfq0qiUv39JO8mynqwdqkQudY6VDAclYAQAAwOcyV2PHjpUNGzbIkiVLzBwsu549e8r06dM93T5UYHl5Nnn4yw1yMPmU1KseJi/9tZ2Z16eBVLfGNcziwPqTwAoAAAA+mbmaMWOGCaIuueQSc6Jr16pVK9m5c6en24cK7J2fdsnCLYkSVMlf/jO4o0SGBnq7SQAAAIDnMldHjhyR6OiCi7SmpaW5BFvAhVi966i8PG+ro1BF67hIDigAAADKV3DVuXNnmT17tuO2PaB67733pFu3bp5tHSqkpNRMGf35b5KbZ5MB7WvLoC71vN0kAAAAwPPDAp9//nm59tprZfPmzaZS4Ouvv26ur1ixQpYuXVrSpwNcaEA15ovfJDElU5pEV5HnbmpDRhQAAADlM3N12WWXyfr1601g1aZNG5k/f74ZJrhy5Urp1KlT6bQSFcYbC7fLzzuOSmhggEwZ3FEqB5c4/gcAAAC84rzOXBs3bizvvvuu51uDCm3ZtiPyxqLt5vrzN7eWprXCvd0kAAAAwPPB1cmTJ4u1X0RERPFfHTgj/sQpeWD6erHZRP7Wpa7c1KEOxwYAAADlM7iqWrVqkXNfbDabuT83N9dTbUMFkZ2bJ6M/+02OpWVJy9gIGde/lbebBAAAAJRecLV48WKXQOq6664zFQLj4uJK/qqAEy25/uve4xIeXMmsZxUSGMDxAQAAQPkNrnr06OFyOyAgwCwk3KhRo9JoFyqI+ZsS5J1lu8z1l29pKw2iKnu7SQAAAEDZVAsEPGX/sXR55KsN5vpd3RtK39axHFwAAAD4LIIreEVGdq7c9+k6OZmRIx3qVZUnrm3BJwEAAICKG1wVVeACKMpzs/+UPw6ekKphgfLWoI4SVIk4HwAAABVkztXNN9/scjsjI0PuvfdeqVzZdY7Mt99+67nWoVyateGQ/HfVXnP9tYHtJa5qqLebBAAAAJRdcBUZGely+/bbb7/wV0eFsyMxVZ745ndzfeRVjeWq5tHebhIAAABQtsHVBx984JlXRIV1KkvnWa2V9KxcuaRRdXmwZzNvNwkAAADwGEtMdJk8ebI0aNBAQkJCpGvXrrJmzZpC973yyivNXK/8l379+rmsw/XUU09JbGyshIaGSs+ePWX79u1l9G7gjn4m/5qxUbYdTpWoKsHyxt86SKUAS3Q/AAAAwCO8fnY7ffp0eeihh2TcuHGybt06adeunfTp00cSExPd7q9zuuLj4x2XjRs3mjW3brnlFsc+L730krzxxhsydepUWb16tZkXps+p88TgHV/9ekC+WXdA/P1E3vxbB4kOD+GjAAAAQLni9eBq4sSJMnz4cBk2bJi0bNnSBERhYWEybdo0t/tXr15dYmJiHJcFCxaY/e3BlWZIJk2aJP/617/kxhtvlLZt28rHH38shw4dkhkzZpTxu4PafOikPDlzo7n+cO/m0q1xDQ4MAAAAKu6cq9KQlZUla9eulbFjxzq2+fv7m2F8K1euLNZzvP/++3Lbbbc5qhbu3r1bEhISzHM4F+PQ4Yb6nLpvfpmZmeZid/LkSfMzOzvbXLzF/trebMOFSsnIMfOsMnPypEfTKPm/S+v59PuxuvLQZ1C26DOg34DvGlhRtoXOaUrSBq8GV0lJSZKbmyu1atVy2a63t2zZcs7H69wsHRaoAZadBlb258j/nPb78pswYYKMHz++wPb58+ebrJi3aXbOF9lsIh9u95c9R/2lapBN+kQmyNy5P3q7WRWCr/YZeA99BvQb8F0DK1pggXOa9PT00guudIhdUYYMGSJlRYOqNm3aSJcuXS7oeTRzpvO+nDNXdevWld69e0tERIR4M0rWDtWrVy8JDAwUX/Pxqn2yftUWqeTvJ+/c2UU61K3q7SaVe77eZ1D26DOg34DvGlhRtoXOaeyj2koluBozZkyBN67RXFBQkMnylCS4ioqKMsUoDh8+7LJdb+t8qqKkpaXJF198Ic8884zLdvvj9Dm0WqDzc7Zv397tcwUHB5tLfvpBevvDtFI7SmL9/mR5Ye5Wc33sdRdJl0Y1vd2kCsUX+wy8iz4D+g34roEVBVrgnKYkr1/ighbHjx93uaSmpsrWrVvlsssuk88//7xEz6UBWadOnWThwoWObXl5eeZ2t27dinzsV199ZeZJ5V/MuGHDhibAcn5OjTa1auC5nhOekZyeJSM/XSfZuTa5tnWM3NW9AYcWAAAA5Z5H5lw1bdpUXnjhBRPoFGeulDMdjjd06FDp3LmzGd6nlf40K6XVA5VmwuLi4sy8qPxDAgcMGCA1arhWntM1rx544AF59tlnTbs02HryySeldu3aZn+Urrw8mzz05QY5mHxK6tcIkxf/2tZ8JgAAAEB557GCFpUqVTLlzktq4MCBcuTIEbPorxac0KF7c+fOdRSk2Ldvn6kg6EwzZcuXLzcFJ9x57LHHTIB2zz33SHJyssmq6XPqIsXwrNw8m6zZfUwSUzLM2lXr9h2XRVsSJaiSv/xncEeJCGFoGgAAACqGEgdXs2bNcrmt60rpYr5vvfWWdO/e/bwaMWrUKHNxZ8mSJQW2NW/e3LxuYTRTonOx8s/HgmfN3Rgv47/fLPEnCi7OPP6GVtKqdiSHHAAAABVGiYOr/EPrNJCpWbOmXH311fLqq696sm2weGA14pN1UliIWzWUjBUAAAAqlhIHV1pwAhWbDgXUjFVhgZXOsHrmh83Su1WMBPgz3woAAAAVQ4mrBQI6x8rdUEA7Dbr0ft0PAAAAqCjOq6DFgQMHzNwrLTaRlZXlct/EiRM91TZYlBav8OR+AAAAQIUMrnT9qBtuuEEaNWpkyq63bt1a9uzZYwpMdOzYsXRaCUvRqoCe3A8AAACokMMCx44dK4888oj88ccfprT5N998I/v375cePXrILbfcUjqthKV0aVhdYiNDzNwqd3S73q/7AQAAABVFiYOrP//80yzsa1/b6tSpU1KlShVT9vzFF18sjTbCYrRIxbj+Ld0WtLAHXHo/xSwAAABQkZQ4uKpcubJjnlVsbKzs3LnTcV9SUpJnWwfLurJ5tESEFBxVGhMZIlNu7yh9W8d6pV0AAACAz8y5uuSSS2T58uVy0UUXyXXXXScPP/ywGSL47bffmvtQMXz16345mZEjsRHB8tJf28mx9Cwzx0qHApKxAgAAQEVU4uBKqwGmpqaa6+PHjzfXp0+fLk2bNqVSYAWRnZsnU5fuMtfvvbKJXN6sprebBAAAAPhecKVVAp2HCE6dOtXTbYLFzfjtoBxMPiVRVYJl4MV1vd0cAAAAwHcXEU5OTpb33nvPVA48duz0QrHr1q2TgwcPerp9sJjcPJtMWXJ6nt3/Xd5QQgIDvN0kAAAAwDczV7///rv07NlTIiMjzfpWw4cPl+rVq5s5V7qo8Mcff1w6LYUl/LgxXnYlpUlkaKDcfkl9bzcHAAAA8N3M1UMPPSR33nmnbN++3axzZafFLZYtW+bp9sFCdKHoyYtPZ63uvLSBVAkucWwOAAAAlFslDq5++eUX+fvf/15ge1xcnCQkJHiqXbCgxVsT5c/4k1I5KECGdW/g7eYAAAAAvh1cBQcHy8mTJwts37Ztm9SsSdW48py1emvRDnNdhwNWDQvydpMAAAAA3w6ubrjhBnnmmWckOzvb3Pbz8zNzrR5//HH5y1/+UhpthAWs3HVU1u1LlqBK/nL35Q293RwAAADA94OrV1991axtFR0dLadOnZIePXpIkyZNJDw8XJ577rnSaSW8bvLi01mrgZ3rmsWCAQAAALgqcUUCrRK4YMEC+fnnn2XDhg0m0OrYsaOpIIjy6bd9x+XnHUelkr+f/L3H2XXOAAAAAFxAcKWl1gcOHCjdu3c3F7usrCz54osvZMiQISV9SvhI1mpAhzipUy3M280BAAAAysewwGHDhsmJEycKbE9JSTH3oXzR6oD/+zNR/PxERlzZ2NvNAQAAAMpPcKVV47SIRX4HDhwwQwZRPrNW17WJlcY1q3i7OQAAAIDvDwvs0KGDCar0cs0110ilSmcfmpubK7t375a+ffuWVjvhBbuOpMrsP+LN9ZFXNuEzAAAAADwRXA0YMMD8XL9+vfTp00eqVDmbxQgKCpIGDRpQir2cmbJkp9hsIte0iJaWtSO83RwAAACgfARX48aNMz81iNKCFiEhlOMuzw4cT5fvfjtoro+8mqwVAAAA4PFqgUOHDi3pQ+CD3lm2S3LybHJp4xrSsV41bzcHAAAAKH/Blc6veu211+TLL7+Uffv2mRLszo4dO+bJ9sELElMy5Itf9pvro64iawUAAACUSrXA8ePHy8SJE83QQC3J/tBDD8nNN98s/v7+8vTTT5f06WBB7/+0W7Jy8qRDvarSrXENbzcHAAAAKJ/B1aeffirvvvuuPPzww6Zi4N/+9jd577335KmnnpJVq1aVTitRZpLTs+STVXsdWSt3ZfcBAAAAeCC4SkhIkDZt2pjrWjHQvqDw9ddfL7Nnzy7p08FiPvh5j6Rl5cpFsRFydYtobzcHAAAAKL/BVZ06dSQ+/vTaR40bN5b58+eb67/88osEBwd7voUoM6mZOfLhij3m+sirGpO1AgAAAEozuLrppptk4cKF5vro0aPlySeflKZNm8qQIUPkrrvuKunTwUJ0OOCJU9nSqGZlubZ1rLebAwAAAJTvaoEvvPCC47oWtahXr56sXLnSBFj9+/f3dPtQRjKyc+W9n3ab6yN6NJYAf+ZaAQAAAKUaXOXXrVs3c4Fvm/7LfklKzZS4qqEyoEOct5sDAAAAlM/gatasWcV+whtuuOFC2gMv0LLrby/daa7f26ORBAaUeLQoAAAAUOEVK7gaMGBAsQ6Ulu3WRYbhW2b8dlAOnciQmuHBckvnut5uDgAAAFB+g6u8vLzSbwm8IjfPJlPOZK2GX95QQgID+CQAAACA81Ci8V82m022b98umzZtkpycnPN5PVjM7D/iZXdSmlQNC5TBXet7uzkAAABA+Q+udu/eLW3btpUWLVqYn40aNTJrW8F35eXZ5D+Ld5jrwy5tKJWDL7i+CQAAAFBhFTu4evTRR0226pNPPpGvv/5a6tatK/fee2/ptg6lauGWRNmSkCJVgivJnZc24GgDAAAAF6DYqYrly5eboOqyyy4zty+55BKpU6eOpKWlSeXKlS+kDfACHeL51pms1e2X1JfIsEA+BwAAAKAsMleJiYlmoWC72NhYCQ0NNdvhe37ecVQ27E+W4Er+cvdlDb3dHAAAAKDiZK60zHpqaqoJqOz8/f0lJSVFTp486dgWERHh+VbC4yafyVr9rUs9U4IdAAAAQBkFVzqMrFmzZgW2dejQwXGdda58w9q9x2XlrqMSGOAn91zRyNvNAQAAACpWcLV48eLSbQnKPGt1c4c6Urvq2UwkAAAAgDIIrnr06HEBLwOr2HTohCzakij+fiIjrmzs7eYAAAAAFXMRYfi+/yzeaX5e37a2NIiiyiMAAABQboKryZMnS4MGDSQkJES6du0qa9asKXL/5ORkGTlypKlWGBwcbOaBzZkzx3F/bm6uPPnkk9KwYUNTfKNx48by73//28wJq+h2JKbKnI3x5vrIq5p4uzkAAABAxRwWWBqmT58uDz30kEydOtUEVpMmTZI+ffrI1q1bJTo6usD+WVlZ0qtXL3OfrrkVFxcne/fulapVqzr2efHFF2XKlCny0UcfSatWreTXX3+VYcOGSWRkpNx///1SkU1ZslM0xuzVspY0jwn3dnMAAACAcsWrwdXEiRNl+PDhJvhRGmTNnj1bpk2bJk888USB/XX7sWPHZMWKFRIYeHrRW816OdP7brzxRunXr5/j/s8///ycGbHybv+xdJmx/qC5TtYKAAAAKEfBlWah1q5dK2PHjnVZN6tnz56ycuVKt4+ZNWuWdOvWzQwLnDlzptSsWVMGDRokjz/+uAQEBJh9Lr30UnnnnXdk27ZtZsjghg0bZPny5SaQK0xmZqa52NnX7crOzjYXb7G/tifaMGXJdsnNs8mljatLq5jKXn1f8I0+g4qBPgP6DfiugRVlW+icpiRtKFZwdfPNNxf7Cb/99tti7ZeUlGTmR9WqVctlu97esmWL28fs2rVLFi1aJIMHDzbzrHbs2CH33XefecPjxo0z+2jGS4OjFi1amIBLX+O5554zjynMhAkTZPz48QW2z58/X8LCwsTbFixYcEGPP5El8uU6DT79pGPwEZc5aiifLrTPoOKhz4B+A75rYEULLHBOk56e7tngSucr2WlhiO+++85s69y5s9mmGSgtNFGSIOx85OXlmflWmpnSwKlTp05y8OBBefnllx3B1ZdffimffvqpfPbZZ2bO1fr16+WBBx6Q2rVry9ChQ90+r2bPdO6XnQZndevWld69e0tERIR4iwaN2qF0npl9GOT5eGHuVsmx7ZWO9arK/bddbBZ7RvnkqT6DioM+A/oN+K6BFWVb6JzGPqrNY8HVBx984LiuQ/BuvfVWMz/KPhRPs0OaQSpJIBIVFWUef/jwYZftejsmJsbtY7RCoB5c++uqiy66SBISEswww6CgIHn00UdN9uq2224z97dp08YUvdDsVGHBlVYd1Et++lre/jAvtB3H07Lk818OmOujr25qjhHKP6v0XfgO+gzoN+C7BlYUaIFzmpK8folLsWtRiUceecQlwNHrmvnR+4pLT/I187Rw4UKXzJTe1nlV7nTv3t0MBdT97HRulQZd9qBB03Y6d8uZts/5MRXJBz/vlvSsXGlVO0KubF7T280BAAAAyq0SB1c5OTlu50TptpIGMBqQvfvuu6Zs+p9//ikjRoyQtLQ0R/XAIUOGuBS80Pu1WuCYMWNMUKWVBZ9//nlT4MKuf//+Zo6V3rdnzx4zhFGLWdx0001S0aRkZMuHK/Y4KgQyHBAAAACwULVADXzuvvtu2blzp3Tp0sVsW716tbzwwguOoKi4Bg4cKEeOHJGnnnrKDO1r3769zJ0711HkYt++fS5ZKJ0HNW/ePHnwwQelbdu2Zp0rDbR0qKLdm2++aRYR1mGKiYmJZq7V3//+d/MaFc1/V+2Vkxk50rhmZenbyv1QSwAAAABeCq5eeeUVMyfq1Vdflfj4eLNNh+XpXKeHH364xA0YNWqUubizZMmSAtt0yOCqVasKfb7w8HCzGLFeKiItt75m9zE5eDxdpi7Zabbdd2UT8feniAUAAABgqeBKM0mPPfaYudgrZ3izoh7OmrsxXsZ/v1niT2Q4tgX4iQQHlnj0JwAAAIASOq+zbp139b///U8+//xzxzyeQ4cOSWpq6vk8HTwUWI34ZJ1LYKVybSKjP/vN3A8AAADAQpkrLWvet29fMx8qMzPT1J7XoXgvvviiua0l2lH2QwE1Y2UrYh+9v1fLGAlgeCAAAABgjcyVFpDQxYOPHz8uoaGhju1ajc+5rDrKjs6xyp+xcqZBl96v+wEAAACwSObqp59+khUrVhRYjLZBgwZy8OBBT7YNxZSYkuHR/QAAAACUQeZK17LKzc0tsP3AgQNmeCDKXnR4iEf3AwAAAFAGwVXv3r1dypxrQQstZDFu3Di57rrrzqMJuFBdGlaX2MgQKazYum7X+3U/AAAAABYJrnR9q59//llatmwpGRkZMmjQIMeQQC1qgbKnRSrG9W/p9j57wKX3U8wCAAAAsNCcqzp16siGDRtk+vTp5qdmre6++24ZPHiwS4ELlK2+rWNlyu0d5eGvNkha5tlhmzGRISaw0vsBAAAAWCi4Mg+qVMkEU3qBdWgANfv3ePn+93i5sX1tue3iemYoIBkrAAAAwILDAgMCAuSqq66SY8dcy3ofPnzY3AfvOnSmJHvvljHSrXENAisAAADAqsGVzWYziwXrWlebNm0qcB+868DxdPOzTjWGaAIAAACWDq60OuA333wj/fv3l27dusnMmTNd7oP3ZOXkSWJKprkeR3AFAAAAWD9zpcP/Xn/9dXnllVdk4MCB8uyzz5K1soD4E6dEk4chgf5So7LrIs8AAAAALFjQwu6ee+6Rpk2byi233CLLli3zXKtwXg4cP2V+xlUNJYsIAAAAWD1zVb9+fZfCFVrcYtWqVbJ//35Ptw0ldNAeXFUL49gBAAAAVs9c7d69u8C2Jk2ayG+//WYqBsJ7DiSfzVwBAAAAsHjmqjAhISEmqwXvoVIgAAAAYPHMVfXq1WXbtm0SFRUl1apVK3I+T/71r1D2wwIpww4AAABYNLh67bXXJDw83FyfNGlSabcJ5+ngmWGBBFcAAACARYOroUOHur0O68jJzZP4ExnmelxVCloAAAAAlgyuTp48WewnjIiIuJD24DwdTsmU3DybBAb4SXR4MMcRAAAAsGJwVbVq1XOum6SLC+s+ubm5nmobSuDAsXTzs3bVUPH3L/qzAgAAAOCl4Grx4sWl8NIojflWlGEHAAAALBxc9ejRo/RbAs8sIMwaVwAAAIBvLCJsl56eLvv27ZOsrCyX7W3btvVEu1BCBxxl2ClmAQAAAPhEcHXkyBEZNmyY/Pjjj27vZ86Vl4cFVgv1UgsAAACAis2/pA944IEHJDk5WVavXi2hoaEyd+5c+eijj6Rp06Yya9as0mklzok5VwAAAICPZa4WLVokM2fOlM6dO4u/v7/Ur19fevXqZUqwT5gwQfr161c6LUWh8vJsjjlXLCAMAAAA+EjmKi0tTaKjo831atWqmWGCqk2bNrJu3TrPtxDnlJSaKVm5eaIV2GMiQzhiAAAAgC8EV82bN5etW7ea6+3atZO3335bDh48KFOnTpXY2NjSaCPO4cCZ+VaxkaESGFDijxQAAACAN4YFjhkzRuLj4831cePGSd++feXTTz+VoKAg+fDDDz3RJpxnpUDKsAMAAAA+FFzdfvvtjuudOnWSvXv3ypYtW6RevXoSFRXl6fahJGtcUSkQAAAA8JrzHkOm61vp8EDNWHXs2JHAyosOHE83PylmAQAAAPhQcKWLB999990SFhYmrVq1MgsJq9GjR8sLL7xQGm3EOVCGHQAAAPDB4Grs2LGyYcMGWbJkiYSEnK1M17NnT5k+fbqn24diYFggAAAA4INzrmbMmGGCqEsuuUT8/Pwc2zWLtXPnTk+3D+dgs9kcBS3qVAvjeAEAAAC+krnSda3s61zlX//KOdhC2Tieni2nsnPN9VjWuAIAAAB8J7jq3LmzzJ4923HbHlC999570q1bN8+2DsUeElgzPFhCAgM4YgAAAICvDAt8/vnn5dprr5XNmzdLTk6OvP766+b6ihUrZOnSpaXTShSKSoEAAACAj2auLrvsMlm/fr0JrNq0aSPz5883wwRXrlxp1r1C2aJSIAAAAOCjmSvVuHFjeffddwts//rrr+Wvf/2rJ9qFYqKYBQAAAOCDmSvNVm3cuFG2bdvmsn3mzJnSrl07GTx4sKfbh2IGV3HVQjlWAAAAgC8EVxpUNWnSxARRF110kdx8881y+PBh6dGjh9x1111mHhal2L03LLBOVYIrAAAAwCeGBT7++OMmuHrrrbfk888/N5c///xT7r77bpk7d66EhnJy7w0UtAAAAAB8LLj65ZdfTPGK9u3by+WXX26Cq3/84x9yxx13lG4LUaiTGdmSkpFjrjMsEAAAAPCRYYFJSUlSu3Ztcz0yMlIqV64sl1xySWm2DcVc46paWKCEBZ1XbRIAAAAAHlLsM3JdLDglJUVCQkLEZrOZ26dOnZKTJ0+67BcREeGptuEcqBQIAAAA+GDmSgOqZs2aSbVq1aR69eqSmpoqHTp0MLf1UrVqVfOzpCZPniwNGjQwQVvXrl1lzZo1Re6fnJwsI0eOlNjYWAkODjZtmjNnjss+Bw8elNtvv11q1Khh5oLpely//vqrlDcHj6ebn3EUswAAAAB8J3O1ePFij7/49OnT5aGHHpKpU6eawGrSpEnSp08f2bp1q1mYOL+srCzp1auXuU/X1IqLi5O9e/eawM7u+PHj0r17d7nqqqvkxx9/lJo1a8r27dvPK/DzmQWEKcMOAAAA+E5wpSXXPW3ixIkyfPhwGTZsmLmtQdbs2bNl2rRp8sQTTxTYX7cfO3ZMVqxYIYGBgWabZr2cvfjii1K3bl354IMPHNsaNmwo5XtYIJUaAQAAAG/zWhUEzUKtXbtWxo4d69jm7+8vPXv2lJUrV7p9zKxZs6Rbt25mWKAuXKxZqUGDBpky8QEBAY59NPt1yy23yNKlS01267777jNBXGEyMzPNxc4+jyw7O9tcvMX+2oW1wV6GPSY8yKvthHWcq88A9BnwXQNv4PcTfLnPlKQNXguutPpgbm6u1KpVy2W73t6yZYvbx+zatUsWLVokgwcPNvOsduzYYQInfcPjxo1z7DNlyhQz3FBLxWsJ+fvvv1+CgoJk6NChbp93woQJMn78+ALbtfR8WFiYeNuCBQvcbt99WANKP9m98VeZs7vMmwULK6zPAPQZ8F0Db+L3E3yxz6Snn05oFIefTStVeMGhQ4dMVkmH+Gk2yu6xxx4zGafVq1cXeIwWr8jIyJDdu3c7MlU6tPDll1+W+Ph4c1uDqM6dO5vntdPgSoOswjJi7jJXOrRQA0BvVj/UoFE7lM4zsw+DtEvPypF2/15krq/9x1USEep6PyqmovoMQJ8B3zXwFn4/wZf7jMYGUVFRcuLEiXPGBl7LXGkDNUA6fPiwy3a9HRMT4/YxWiFQD649sFIXXXSRJCQkmGGGGljpPi1btnR5nO7zzTffFNoWrTqol/z0tbz9YRbWjiPHM8zP8OBKUiPC+9k1WItV+i58B30G9BvwXQMrCrTAOU1JXr/Ypdg9TQOhTp06ycKFCx3b8vLyzG3nTJYzrQKoQwF1P7tt27aZgEqfz76PVht0pvvUr19fypP9Z4pZUCkQAAAAsIZiZa5uvvnmYj/ht99+W+x9dV6UzoPSYXxdunQxpdjT0tIc1QOHDBlihg7qnCg1YsQIeeutt2TMmDEyevRoU2L9+eefN8P+7B588EG59NJLzfZbb73VrJv1zjvvmEt5cpBKgQAAAIDvBVeRkZGO6zpF67vvvjPbNChSWvVPF/ctSRCmBg4cKEeOHJGnnnrKDO1r3769zJ0711HkYt++faaCoJ3Og5o3b54JoNq2bWsCLw20tFqg3cUXX2zap1UIn3nmGVOGXYM2LYJRXuTm2WTN7mPmeoCfn7kd4O/n7WYBAAAAFVqxgivnNaM0kNGMkK5JZZ/7pFX/tGrf+RR/GDVqlLm4s2TJkgLbdMjgqlWrinzO66+/3lzKo7kb42X895sl/sTpOVfzNh+Wy15cJOP6t5S+rWO93TwAAACgwirxnCtdyPeRRx5xKSqh13WIn96H0g2sRnyyzhFY2SWcyDDb9X4AAAAAPhJc5eTkuF2HSrc5F5qAZ+nQP81Yuaubb9+m9+t+AAAAAMpeiUuxa7GJu+++W3bu3GmKUChdk+qFF15wFKKA5+kcq/wZK2caUun9ul+3xjX4CAAAAACrB1evvPKKWYfq1VdfdSzcq6XQH330UXn44YdLo40QkcSUwgOr89kPAAAAgJeDK63e99hjj5mLrlaszqeQBUomOjzEo/sBAAAA8KzzWkRY513973//k88//1z8/E6XAD906JCkpqZ6uHmw69KwusRGhkhhBdd1u96v+wEAAADwgeBq79690qZNG7nxxhtl5MiRZp0q9eKLL5oqgigduo6Vllt3xx5w6f2sdwUAAAD4SHCli/bq4sHHjx+X0NBQx/abbrpJFi5c6On2wYmuYzXl9o4SGRroclxiIkPMdta5AgAAAHxoztVPP/0kK1askKCgIJftDRo0kIMHD3qybXBDA6hdSWny0tytcnH9avJQ7+ZmKCAZKwAAAMDHgitdyyo3N7fA9gMHDkh4eLin2oUinDiVbX62rVuVsusAAACArw4L7N27t0yaNMlxWwtaaCGLcePGyXXXXefp9sGN5LTTwVW1MNfhgQAAAAB8KHOl61v16dNHWrZsKRkZGTJo0CDZvn27REVFmeqBKH3H07PMz6phrkMzAQAAAPhQcFWnTh3ZsGGDTJ8+3fzUrNXdd98tgwcPdilwgdKTnG7PXBFcAQAAAD4bXJkHVapkgim9wHuZK4YFAgAAAD485yogIECuuuoqOXbsmMv2w4cPm/tQ+o6fyVwxLBAAAADw4eDKZrNJZmamWetq06ZNBe5D6dJjnGzPXFWmoAUAAADgs8GVVgf85ptvpH///tKtWzeZOXOmy30oXamZOZKTdzqIZc4VAAAA4OOZKx3+9/rrr8srr7wiAwcOlGeffZasVRk5fqYMe0igv4QEMgwTAAAA8OmCFnb33HOPNG3aVG655RZZtmyZ51qFYhSzoFIgAAAA4NOZq/r167sUrtDiFqtWrZL9+/d7um1wgzWuAAAAgHKSudq9e3eBbU2aNJHffvvNVAxEWa1xRTELAAAAwKczV4UJCQkxWS2ULoYFAgAAAD6cuapevbps27ZNoqKipFq1akVWBcy//hVKa40rMlcAAACAzwVXr732moSHh5vrkyZNKu02oQiONa4oaAEAAAD4XnA1dOhQt9dR9shcAQAAAD4cXJ08ebLYTxgREXEh7cE5kLkCAAAAfDi4qlq1apHzrOyLC+s+ubm5nmobiipoUZk5VwAAAIDPBVeLFy8u/ZagWI6n2QtasIgwAAAA4HPBVY8ePUq/JSgWhgUCAAAA5WQRYbv09HTZt2+fZGWdHqZm17ZtW0+0C25k5eRJWtbpYZcsIgwAAAD4eHB15MgRGTZsmPz4449u72fOVelnrfz9RCJCmHMFAAAAWIl/SR/wwAMPSHJysqxevVpCQ0Nl7ty58tFHH0nTpk1l1qxZpdNKuJRhjwwNFH+NsAAAAAD4buZq0aJFMnPmTOncubP4+/tL/fr1pVevXqYE+4QJE6Rfv36l01KcrRRIMQsAAADA9zNXaWlpEh0dba5Xq1bNDBNUbdq0kXXr1nm+hSgwLLBqGEMCAQAAAJ8Prpo3by5bt24119u1aydvv/22HDx4UKZOnSqxsbGl0UbkGxZI5goAAAAoB8MCx4wZI/Hx8eb6uHHjpG/fvvLpp59KUFCQfPjhh6XRRuQbFsgaVwAAAEA5CK5uv/12x/VOnTrJ3r17ZcuWLVKvXj2JiorydPvgJNmRuWJYIAAAAFBu1rmyCwsLk44dO3qmNSjS8bQzBS0qB3GkAAAAAF8Prmw2m3z99deyePFiSUxMlLy8PJf7v/32W0+2D27mXFHQAgAAACgHwZWuc6VFLK666iqpVauW+Pmx3lJZVwukoAUAAABQDoKr//73vyY7dd1115VOi1CMghbMuQIAAAB8vhR7ZGSkNGrUqHRag2IWtGDOFQAAAODzwdXTTz8t48ePl1OnTpVOi1DoXLfkUwRXAAAAQLkZFnjrrbfK559/LtHR0dKgQQMJDHQdorZu3TpPtg9nnMzIkdw8m7nOsEAAAACgHARXQ4cOlbVr15r1rihoUfbFLEIDAyQkMKAMXxkAAABAqQRXs2fPlnnz5slll11W0ofCA2XYWUAYAAAAKCdzrurWrSsRERGl0xoUo1IgxSwAAACAchFcvfrqq/LYY4/Jnj17PNaIyZMnm/lbISEh0rVrV1mzZk2R+ycnJ8vIkSMlNjZWgoODpVmzZjJnzhy3+77wwgtmLS5dn6tcrHFVmTLsAAAAQLkYFqhzrdLT06Vx48YSFhZWoKDFsWPHSvR806dPl4ceekimTp1qAqtJkyZJnz59ZOvWraZoRn5ZWVnSq1cvc9/XX38tcXFxsnfvXqlatWqBfX/55Rez4HHbtm3F1x1Po1IgAAAAUK6CKw1+PGnixIkyfPhwGTZsmLmtQZbO65o2bZo88cQTBfbX7RrArVixwhHYadYrv9TUVBk8eLC8++678uyzz4qvc2SuGBYIAAAA+H5wlZ2dLUuXLpUnn3xSGjZseMEvrlkorTw4duxYxzZ/f3/p2bOnrFy50u1jZs2aJd26dTPDAmfOnCk1a9aUQYMGyeOPPy4BAWer6On9/fr1M891ruAqMzPTXOxOnjzpeL968Rb7a+vPo6mn2xcREuDVNsHanPsMQJ8B3zWwCn4/wZf7TEnaUKLgSjNF33zzjQmuPCEpKUlyc3NNSXdnenvLli1uH7Nr1y5ZtGiRyUrpPKsdO3bIfffdZ970uHHjzD5ffPGFWW9LhwUWx4QJE8zCyPnNnz/fDH30tgULFsjmnTo9zl8O7dkuc+Zs83aTYHHaZwD6DPiugdXw+wm+2Gd0SlSpDQscMGCAzJgxQx588EHxhry8PDPf6p133jGZqk6dOsnBgwfl5ZdfNsHV/v37ZcyYMeaD0AIZxaGZM5335Zy50qqIvXv39mplRA0Y9X3oHLPpiRtEjh6T7p3ayXXta3utTbA25z6Tfz4kQJ8B3zXwFn4/wZf7jH1UW6kEV02bNpVnnnlGfv75ZxPYVK5c2eX++++/v9jPFRUVZQKkw4cPu2zX2zExMW4foxUC9QA7DwG86KKLJCEhwTHMMDExUTp27Oi4X7Njy5Ytk7feessM/3N+rNKKg3rJT1/H2x+mvR0nTuWY6zUiQi3RJlibVfoufAd9BvQb8F0DKwq0wDlNSV6/xMHV+++/byrzaRCjF2da8rwkwVVQUJAJ0BYuXGgyYvbMlN4eNWqU28d0795dPvvsM7Ofzs9S27ZtM0GXPt8111wjf/zxh8tjtFhGixYtCszL8iXJjkWEWecKAAAAsKISB1e7d+/2aAN0ON7QoUOlc+fO0qVLF1ONMC0tzVE9cMiQIabcus6LUiNGjDAZKB36N3r0aNm+fbs8//zzjqAuPDxcWrdu7fIaml2rUaNGge2+IjfPJklnClrsSUqVNnGREuDv5+1mAQAAALiQ4MqZzWZzZKzO18CBA+XIkSPy1FNPmaF97du3l7lz5zqKXOzbt8+RoVI6F2revHlmzpeuX6WBlwZampUqjzYc9ZPnX10mmTl55vYD0zfIi3O3yrj+LaVv61hvNw8AAADAhQRXH3/8sSkgoVkj1axZM3n00UfljjvuOJ+nM0MACxsGuGTJkgLbtBT7qlWriv387p7DF8zbdFimbdPA8myZeJVwIkNGfLJOptzekQALAAAA8NXgShf91VLsGgzp/Ce1fPlyuffee01pdW9VESxvdCjgs3Pcl6PXfKHmCsd/v1l6tYxhiCAAAADgi8HVm2++KVOmTDFzoexuuOEGadWqlTz99NMEVx6yZvcxSTipGSu/QgOs+BMZZr9ujWt46mUBAAAAnKezk5mKKT4+Xi699NIC23Wb3gfPSEzJ8Oh+AAAAACwWXDVp0kS+/PLLAtunT59u1sCCZ0SHh3h0PwAAAAAWGxY4fvx4U+FPF+W1z7nSBYV1bSp3QRfOT5eG1SUmIlgSTma4HRqoW2IiQ8x+AAAAAHwwc/WXv/xFVq9eLVFRUTJjxgxz0etr1qyRm266qXRaWQHpOlb/uq6F2/vsoZaWY2e9KwAAAMCHS7F36tRJPvnkE8+3Bi76tKoldzXLk2/3h0jyqWzHds1Ysc4VAAAAUI4WEUbpa1fDJm3atpAHv/5DmkRXln/f2MYMBSRjBQAAAPhocOXv7y9+fu7Lgtvp/Tk5OZ5oF5zknflZKyKEsusAAACArwdX3333XaH3rVy5Ut544w3Jy7OHAfD0gsIqwL/EU+QAAAAAWC24uvHGGwts27p1qzzxxBPy/fffy+DBg+WZZ57xdPugwZXtdHBVyb/ozCEAAAAA7zmvVMihQ4dk+PDh0qZNGzMMcP369fLRRx9J/fr1Pd9CODJX/ucYlgkAAADAR4KrEydOyOOPP24WEt60aZNZ20qzVq1bty69FkJyzgRXZK4AAACAcjAs8KWXXpIXX3xRYmJi5PPPP3c7TBClI88+5yqAzBUAAADg88GVzq0KDQ01WSsdAqgXd7799ltPtg9yNnMVwLBAAAAAwPeDqyFDhpyzFDtKd84VwwIBAACAchBcffjhh6XbEhSjFDvBLQAAAGBVLJzkAwiuAAAAAOsjuPKhda7IXAEAAADWRXDlA8hcAQAAANZHcOUDCK4AAAAA6yO48gFUCwQAAACsj+DKh4Irf6oFAgAAAJZFcOVDiwizzhUAAABgXQRXPiDPUS2QjwsAAACwKs7WfShzFeDHIsIAAACAVRFc+VJBiwCCKwAAAMCqCK58AKXYAQAAAOsjuPKl4IphgQAAAIBlEVz50pwrSrEDAAAAlkVw5QPymHMFAAAAWB7BlQ9lrvwZFggAAABYFsGVD61zxSLCAAAAgHURXPkA5lwBAAAA1kdw5QMoxQ4AAABYH8GVDxW0oFogAAAAYF0EVz6AYYEAAACA9RFc+dCwQApaAAAAANZFcOUDcs9UCwzw5+MCAAAArIqzdZ8qaOHtlgAAAAAoDKfrPiAnl8wVAAAAYHUEVz6ARYQBAAAA6yO48qFqgf5+ft5uCgAAAIBCEFz5UrXAAIIrAAAAwKoIrnyqoAXBFQAAAGBVBFe+FFwxLBAAAACwLIIrn1rniswVAAAAYFUEVz6AOVcAAACA9VkiuJo8ebI0aNBAQkJCpGvXrrJmzZoi909OTpaRI0dKbGysBAcHS7NmzWTOnDmO+ydMmCAXX3yxhIeHS3R0tAwYMEC2bt0qvophgQAAAID1eT24mj59ujz00EMybtw4WbdunbRr10769OkjiYmJbvfPysqSXr16yZ49e+Trr782QdO7774rcXFxjn2WLl1qgq9Vq1bJggULJDs7W3r37i1paWniiyhoAQAAAFhfJW83YOLEiTJ8+HAZNmyYuT116lSZPXu2TJs2TZ544okC++v2Y8eOyYoVKyQwMNBs06yXs7lz57rc/vDDD00Ga+3atXLFFVeIryG4AgAAAKzPq8GVZqE04Bk7dqxjm7+/v/Ts2VNWrlzp9jGzZs2Sbt26mczUzJkzpWbNmjJo0CB5/PHHJSAgwO1jTpw4YX5Wr17d7f2ZmZnmYnfy5EnzUzNeevEW+2vbFxG25eV6tT2wPnv/oJ+APgO+a2Al/H6CL/eZkrTBq8FVUlKS5ObmSq1atVy26+0tW7a4fcyuXbtk0aJFMnjwYDPPaseOHXLfffeZN61DC/PLy8uTBx54QLp37y6tW7d2+5w6R2v8+PEFts+fP1/CwsLE23Jyc0XET5YuXixVg73dGvgCHQ4L0GfAdw2sht9P8MU+k56e7jvDAktKgyUd4vfOO++YTFWnTp3k4MGD8vLLL7sNrjTDtXHjRlm+fHmhz6mZM5335Zy5qlu3rpmnFRERId6iAaN2qDzb6RLsvXpeIzXDia5w7j6j8xLtw2aB4nzP0GdwPr+f6Degz6AifM+cPDOqzfLBVVRUlAmQDh8+7LJdb8fExLh9jFYI1APsPATwoosukoSEBDPMMCgoyLF91KhR8sMPP8iyZcukTp06hbZDKw7qJT99HW9/mDoi8PSgQJGQ4CCvtwe+wQp9F76FPgP6DfiugRUFWuCcpiSv79VqgRoIaeZp4cKFLpkpva3zqtzR4X06FFD3s9u2bZsJuuyBlc1mM4HVd999Z4YQNmzYUHzVmfWDDRYRBgAAAKzL66XYdTiellL/6KOP5M8//5QRI0aYkun26oFDhgxxKXih92u1wDFjxpigSisLPv/882b4n51e/+STT+Szzz4za11pVksvp06dEl+TS3AFAAAA+ASvz7kaOHCgHDlyRJ566ikTALVv396UUrcXudi3b5+pIGinc6HmzZsnDz74oLRt29asb6WBllYLtJsyZYr5eeWVV7q81gcffCB33nmn+JKz+TmRSv6n514BAAAAsB6vB1dKh/DpxZ0lS5YU2KZDBnWB4MLosMDy4kwVdoNhgQAAAIB1eX1YIEoQXPmRuQIAAACsiuDKR4Irjav8GRYIAAAAWBbBlY8EV8y3AgAAAKyN4MpHClr4MyQQAAAAsDSCK4sjcwUAAAD4BoIrHwmumG8FAAAAWBvBlcWRuQIAAAB8A8GVjwRXAU4LKQMAAACwHs7YfaSgRQCfFAAAAGBpnLJbXK6jFDsfFQAAAGBlnLFbnM0xLNDP200BAAAAUASCKx/JXBFcAQAAANZGcGVxebbTGSuCKwAAAMDaCK58pKBFJYYFAgAAAJZGcOUriwj7MecKAAAAsDKCK19ZRDiA4AoAAACwMoIrn1lEmOAKAAAAsDKCK18JrhgWCAAAAFgawZWPFLQgcwUAAABYG8GVxTEsEAAAAPANBFcWR3AFAAAA+AaCK1+pFkhBCwAAAMDSCK4sjswVAAAA4BsIriwul1LsAAAAgE8guLK4M7GVVPLnowIAAACsjDN2H8lc+TPnCgAAALA0giuLs1HQAgAAAPAJBFcWR0ELAAAAwDcQXPlKQQs/P283BQAAAEARCK58pKBFQADBFQAAAGBlBFcWl2s7HVSxiDAAAABgbQRXPjLnyp9hgQAAAIClEVz5SHBF5goAAACwNoIrX6kWyJwrAAAAwNIIrnwluGJYIAAAAGBpBFcWl3fmJ8MCAQAAAGsjuPKVghb+lGIHAAAArIzgysJy82xyPPP09UPJp8xtAAAAANZEcGVRczfGy5WvLpPNyac/oi9/PSCXvbjIbAcAAABgPQRXFqQB1IhP1knCyTNpqzMSTmSY7QRYAAAAgPUQXFmMDv0b//1mcTcA0L5N72eIIAAAAGAtBFcWs2b3MYk/kVHo/Rpg6f26HwAAAADrILiymMSUDI/uBwAAAKBsEFxZTHR4iEf3AwAAAFA2CK4spkvD6hIbGSKFrWql2/V+3Q8AAACAdRBcWUyAv5+M69/SXM8fYNlv6/26HwAAAADrILiyoL6tY2XK7R2lVkSwy/aYyBCzXe8HAAAAYC2WCK4mT54sDRo0kJCQEOnatausWbOmyP2Tk5Nl5MiREhsbK8HBwdKsWTOZM2fOBT2n1WgAteThK2RUy1yZeEsb+Xz4JbL88asJrAAAAACL8npwNX36dHnooYdk3Lhxsm7dOmnXrp306dNHEhMT3e6flZUlvXr1kj179sjXX38tW7dulXfffVfi4uLO+zmtSof+NY20Sf+2sdKtcQ2GAgIAAAAW5vXgauLEiTJ8+HAZNmyYtGzZUqZOnSphYWEybdo0t/vr9mPHjsmMGTOke/fuJjvVo0cPE0Cd73MCAAAAwIWqJF6kWai1a9fK2LFjHdv8/f2lZ8+esnLlSrePmTVrlnTr1s0MC5w5c6bUrFlTBg0aJI8//rgEBASc13NmZmaai93JkyfNz+zsbHPxFvtre7MN8C30GdBnwHcNrIjfT/DlPlOSNng1uEpKSpLc3FypVauWy3a9vWXLFreP2bVrlyxatEgGDx5s5lnt2LFD7rvvPvOmdRjg+TznhAkTZPz48QW2z58/32S8vG3BggXebgJ8DH0G9BnwXQMr4vcTfLHPpKen+0ZwdT7y8vIkOjpa3nnnHZOp6tSpkxw8eFBefvllE1ydD81y6Rwt58xV3bp1pXfv3hIRESHeogGjdiidYxYYGOi1dsB30GdAnwHfNbAifj/Bl/uMfVSb5YOrqKgoEyAdPnzYZbvejomJcfsYrRCoB1gfZ3fRRRdJQkKCGRJ4Ps+pFQf1kp++jrc/TCu1A76DPgP6DPiugRXx+wm+2GdK8vpeLWgRFBRkMk8LFy50yUzpbZ1X5Y4WsdChgLqf3bZt20zQpc93Ps8JAAAAAD5fLVCH42kp9Y8++kj+/PNPGTFihKSlpZlKf2rIkCEuxSn0fq0WOGbMGBNUzZ49W55//nlT4KK4zwkAAAAAnub1OVcDBw6UI0eOyFNPPWWG9rVv317mzp3rKEixb98+U+3PTudCzZs3Tx588EFp27atWd9KAy2tFljc5wQAAACAchdcqVGjRpmLO0uWLCmwTYf3rVq16ryfEwAAAADK3bBAAAAAACgPCK4AAAAAwAMIrgAAAADAAwiuAAAAAKC8FLSwGpvNVuLVmEtrZer09HTTDm8vngbfQJ8BfQZ818CK+P0EX+4z9pjAHiMUheDKjZSUFEfZdwAAAABISUmRyMjIIg+En604IVgFk5eXJ4cOHZLw8HDx8/PzapSsAd7+/fslIiLCa+2A76DPgD4DvmtgRfx+gi/3GQ2XNLCqXbu2y/q77pC5ckMPWp06dcQqtEN5u1PBt9BnQJ8B3zWwIn4/wVf7zLkyVnYUtAAAAAAADyC4AgAAAAAPILiysODgYBk3bpz5CdBnwPcMrILfT6DPgO8Z9yhoAQAAAAAeQOYKAAAAADyA4AoAAAAAPIDgCgAAAAA8gOAKAAAAADyA4MqiJk+eLA0aNJCQkBDp2rWrrFmzxttNQhlZtmyZ9O/f36wC7ufnJzNmzCiwSvhTTz0lsbGxEhoaKj179pTt27e77HPs2DEZPHiwWXSvatWqcvfdd0tqaqrLPr///rtcfvnlpo/pCugvvfRSmbw/eN6ECRPk4osvlvDwcImOjpYBAwbI1q1bXfbJyMiQkSNHSo0aNaRKlSryl7/8RQ4fPuyyz759+6Rfv34SFhZmnufRRx+VnJwcl32WLFkiHTt2NNXimjRpIh9++CEfqQ+aMmWKtG3b1rE4Z7du3eTHH3903E9/wbm88MIL5nfUAw88QL+BW08//bTpI86XFi1alP/+YoPlfPHFF7agoCDbtGnTbJs2bbINHz7cVrVqVdvhw4e93TSUgTlz5tj++c9/2r799lub/hf97rvvXO5/4YUXbJGRkbYZM2bYNmzYYLvhhhtsDRs2tJ06dcqxT9++fW3t2rWzrVq1yvbTTz/ZmjRpYvvb3/7muP/EiRO2WrVq2QYPHmzbuHGj7fPPP7eFhoba3n77bT5jH9SnTx/bBx98YD7L9evX26677jpbvXr1bKmpqY597r33XlvdunVtCxcutP3666+2Sy65xHbppZc67s/JybG1bt3a1rNnT9tvv/1m+mFUVJRt7Nixjn127dplCwsLsz300EO2zZs32958801bQECAbe7cuWX+nnFhZs2aZZs9e7Zt27Zttq1bt9r+8Y9/2AIDA00fUvQXFGXNmjW2Bg0a2Nq2bWsbM2aMYzv9Bs7GjRtna9WqlS0+Pt5xOXLkSLnvLwRXFtSlSxfbyJEjHbdzc3NttWvXtk2YMMGr7ULZyx9c5eXl2WJiYmwvv/yyY1tycrItODjYBEhKv1z0cb/88otjnx9//NHm5+dnO3jwoLn9n//8x1atWjVbZmamY5/HH3/c1rx58zJ6ZyhNiYmJpg8sXbrU0Uf0xPmrr75y7PPnn3+afVauXGlu6y8tf39/W0JCgmOfKVOm2CIiIhz95LHHHjO/KJ0NHDjQBHfwffqd8N5779FfUKSUlBRb06ZNbQsWLLD16NHDEVzxPQN3wVW7du3cHpjy3F8YFmgxWVlZsnbtWjPUy87f39/cXrlypVfbBu/bvXu3JCQkuPSPyMhIM3TU3j/0pw4F7Ny5s2Mf3V/70erVqx37XHHFFRIUFOTYp0+fPmYo2fHjx8v0PcHzTpw4YX5Wr17d/NTvlOzsbJd+o0Mz6tWr59Jv2rRpI7Vq1XLpEydPnpRNmzY59nF+Dvs+fDf5ttzcXPniiy8kLS3NDA+kv6AoOoxLh2nl/y6g38Cd7du3m2kOjRo1MtMVdJhfee8vBFcWk5SUZH7ROXckpbf1pBoVm70PFNU/9KeOS3ZWqVIlc6LtvI+753B+DfimvLw8Mweie/fu0rp1a8dnqoG0Bt1F9Ztz9YnC9tFfdKdOnSrV9wXP++OPP8w8B52ncO+998p3330nLVu2pL+gUBqEr1u3zszzzI/vGeTXtWtXM/9p7ty5Zp6n/oFY53qnpKSU6/5SySuvCgAotb8qb9y4UZYvX84RRpGaN28u69evN5nOr7/+WoYOHSpLly7lqMGt/fv3y5gxY2TBggWmEBJwLtdee63juhbQ0WCrfv368uWXX5qCXOUVmSuLiYqKkoCAgALVUvR2TEyM19oFa7D3gaL6h/5MTEx0uV8r62gFQed93D2H82vA94waNUp++OEHWbx4sdSpU8exXT9THXKcnJxcZL85V58obB+tNleef1GWV/pXY62s1alTJ5OJaNeunbz++uv0F7ilw7j0d4tWZdPREHrRYPyNN94w1zVbwPcMiqJZqmbNmsmOHTvK9fcMwZUFf9npL7qFCxe6DPPR2zoWHhVbw4YNzReJc//Q1LfOpbL3D/2pX1b6i9Bu0aJFph/pX43s+2jJdx3vbKd/jdS/ZFerVq1M3xMunNY+0cBKh3XpZ639xJl+pwQGBrr0G51fp2PfnfuNDhNzDsy1T+gvKB0qZt/H+Tns+/DdVD7od0RmZib9BW5dc8015jtCs532i87t1Xk09ut8z6AouiTMzp07zVIy5fr3ktdKaaDIUuxa/e3DDz80ld/uueceU4rduVoKynclJi05qhf9Lzpx4kRzfe/evY5S7NofZs6cafv9999tN954o9tS7B06dLCtXr3atnz5clPZybkUu1bp0VLsd9xxhym9rH1OS5lSit03jRgxwpTnX7JkiUvJ2/T0dJeSt1qefdGiRabkbbdu3cwlf8nb3r17m3LuWsa2Zs2abkvePvroo6aq0+TJk71e8hbn54knnjDVJHfv3m2+R/S2VhSdP3++uZ/+guJwrhZIv0F+Dz/8sPm9pN8zP//8symprqXUtaJtee4vBFcWpXX6tcPpeldaml3XK0LFsHjxYhNU5b8MHTrUUY79ySefNMGRBuHXXHONWafG2dGjR00wVaVKFVOydNiwYSZoc6ZrZF122WXmOeLi4kzQBt/krr/oRde+stPg+7777jPltvUX0U033WQCMGd79uyxXXvttWbNM/0FqL8Ys7OzC/TP9u3bm++mRo0aubwGfMddd91lq1+/vvkc9WRFv0fsgZWiv+B8giv6DfKXRI+NjTXfM3qeobd37NhR7vuLn/7jvbwZAAAAAJQPzLkCAAAAAA8guAIAAAAADyC4AgAAAAAPILgCAAAAAA8guAIAAAAADyC4AgAAAAAPILgCAAAAAA8guAIAAAAADyC4AgCUS1deeaU88MADlnmeC/X0009L+/btvd0MAEARCK4AAB515513ip+fn7kEBQVJkyZN5JlnnpGcnBxLH+klS5aYNicnJ7ts//bbb+Xf//53qb/+d999J5dccolERkZKeHi4tGrVyiWoe+SRR2ThwoWl3g4AwPmrdAGPBQDArb59+8oHH3wgmZmZMmfOHBk5cqQEBgbK2LFjfe6IVa9evdRfQ4OmgQMHynPPPSc33HCDCfI2b94sCxYscOxTpUoVcwEAWBeZKwCAxwUHB0tMTIzUr19fRowYIT179pRZs2aZ+44fPy5DhgyRatWqSVhYmFx77bWyfft2x2M//PBDqVq1qsyYMUOaNm0qISEh0qdPH9m/f79LdmzAgAEur6lZHh3CV5j//ve/0rlzZ5MV0rYNGjRIEhMTzX179uyRq666ylzXdmlwo6/hblhgcds/b948ueiii0xApMFmfHx8oW37/vvvpXv37vLoo49K8+bNpVmzZub9TZ48udBhgfbsoPOlQYMGjvs3btxo2qavX6tWLbnjjjskKSmpiE8NAHChCK4AAKUuNDRUsrKyzHUNWn799VcTbK1cuVJsNptcd911kp2d7dg/PT3dZHE+/vhj+fnnn81Qvdtuu+2C2qDPr8P7NmzYYAI3DajsAVTdunXlm2++Mde3bt1qAqHXX3/d7fMUt/2vvPKKCeiWLVsm+/btM8P6CqPB3qZNm0xAVFzaRvtlx44dZvjlFVdcYe7T43X11VdLhw4dTFvnzp0rhw8flltvvbXYzw8AKDmGBQIASo0GHjrkTbM4o0ePNhkeDUo0YLr00kvNPp9++qkJbjTgueWWW8w2DVTeeust6dq1q7n90UcfmSzQmjVrpEuXLufVlrvuustxvVGjRvLGG2/IxRdfLKmpqSa7Yx/+Fx0dbTJP7pSk/VOnTpXGjRub26NGjTLzzgqjx+ann36SNm3amGyfzr3q3bu3DB482GQBCwvI7Mf4L3/5i5mr9fbbb5tteuw0sHr++ecd+0+bNs20c9u2bSYzBgDwPDJXAACP++GHH0zAokP6dGiazifSYW1//vmnVKpUyRE0qRo1apihcHqfne6jgY9dixYtTMDjvE9JrV27Vvr37y/16tUzQwN79OhhtmtWqbiK234dLmgPrFRsbKxjCKI7lStXltmzZ5sM1L/+9S9z7B5++GETSGoWrCj/+Mc/TAZt5syZJkOoNDu3ePFixzwtvegxVDt37iz2+wUAlAyZKwCAx+n8pSlTpphqgbVr1zYBiSf5+/ubjI0z52F5+aWlpZl5W3rRTFPNmjVNUKW37cMVPUmLdzjT+VD52+uOBmR6+b//+z/55z//aTJM06dPl2HDhrnd/5NPPpHXXnvNVDqMi4tzbNdsnAaSL774YoHHaKAHACgdZK4AAB6nmRidA6RZIufASof2aUn21atXO7YdPXrUzHNq2bKlY5vuo3OF7PR+nUekj1caHOUvELF+/fpC27NlyxbzOi+88IJcfvnlJouTP5OkgaDKzc0t9HmK235P0OIUmgHTwNAdzVZpEKZDAXUYobOOHTuaOVz6HPo5OF/0swEAlA6CKwBAmdHqfzfeeKMMHz5cli9fboav3X777SbrotudMz86D0mDGB3Op0UkNICwz7fSYg0afGnBC50HNW7cuCKLQWiQp8HTm2++Kbt27TLzpvKvXaVznTTDpEMajxw5YrI/59v+ktIhk4899pjJQO3evVt+++03M0dMs3G9evUqsH9CQoLcdNNNpsiHZt/0tl603UpL3x87dkz+9re/yS+//GKGAuq8N82AFRU8AgAuDMEVAKBM6fpXnTp1kuuvv166detmhsvpWljOQ+k0Y/P444+bculaolznDOnwODsNKJ588kkTkOjcrJSUFFMevTCa6dIS6V999ZXJMGkGS6v5OdMAafz48fLEE0+Y0uVahOJ8219SOv9Lgz59D5pV03lqGizNnz/fzOdyl4nT6n9a6EOH+dkv9nlqOhRTi25oIKWFMbRQhpaT13lrOqQSAFA6/GzFGQQOAEAZ0SBIAwEdBggAgC/hz1cAAAAA4AEEVwAAAADgAQwLBAAAAAAPIHMFAAAAAB5AcAUAAAAAHkBwBQAAAAAeQHAFAAAAAB5AcAUAAAAAHkBwBQAAAAAeQHAFAAAAAB5AcAUAAAAAcuH+H8RxdnrpjSxeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 2:13:40.587925\n"
     ]
    }
   ],
   "source": [
    "# Program comparingPopulationSizes.py\n",
    "# la Evolución battles against complexity,\n",
    "# given by the lengths of target sequences equal to 10000.\n",
    "# We vary the population size, and we compare the heights of the arisen plateaus.\n",
    "# The purpose is to compare the heights of the arisen plateaus.  \n",
    "\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt # Import matplotlib for plotting\n",
    "import datetime\n",
    "import time\n",
    "\n",
    "\n",
    "start_time = time.time() # Record the start time\n",
    "\n",
    "length_of_target_sequence = 10000\n",
    "population_sizesk5 =           [10,    20,  50,  100, 300,  500,  2000, 3000, 4000, 5000]\n",
    "numbersOfGenerationsk5 =      [ 400,  600, 600,  600, 600,  600,   600,  500,  500,  400]\n",
    "alphabet = '01' # Binary alphabet, binary strings\n",
    "include_clone_populationk5 = True\n",
    "include_mutate_allk5 = True\n",
    "include_crossover_allk5 = True\n",
    "mutation_rate = 0.01\n",
    "recombination_rate = 0.01\n",
    "print_best_fitness = False\n",
    "show_graph = True\n",
    "\n",
    "\n",
    "def print_parametersk5():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: compare diverse plateaus for different degrees of complexity\")\n",
    "    print(f\"Date of Experiment  = {datetime.datetime.now()}\")\n",
    "    print(f\"alphabet =  {alphabet}\")\n",
    "    print(f\"length_of_target_sequence = {length_of_target_sequence}\")\n",
    "    print(f\"include_clone_populationk5 = {include_clone_populationk5}\")\n",
    "    print(f\"include_mutate_allk5 = {include_mutate_allk5}\")\n",
    "    print(f\"include_crossover_allk5 = {include_crossover_allk5}\")\n",
    "    if include_mutate_allk5:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk5:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    if include_clone_populationk5:\n",
    "        print(f\"print_best_fitness = {print_best_fitness}\")\n",
    "\n",
    "def maxk5(data):\n",
    "    max_value = data[0]\n",
    "    for x in data:\n",
    "        if x > max_value:\n",
    "            max_value = x\n",
    "    return max_value\n",
    "\n",
    "\n",
    "\n",
    "def mean_stddev_and_maxk5(data):\n",
    "    mean = sum(data) / len(data)\n",
    "    variance = sum((x - mean) ** 2 for x in data) / len(data)\n",
    "    stddev = variance ** 0.5\n",
    "    max_value = maxk5(data)\n",
    "    return mean, stddev, max_value\n",
    "\n",
    "def create_populationk5(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with random chromosomes.\"\"\"\n",
    "    # print(\"create_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        random_string = ''.join(random.choice(alphabet) for _ in range(len(target_sequence)))\n",
    "        population.append(random_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "def create_perfect_populationk5(population_size, target_sequence, alphabet):\n",
    "    \"\"\"Creates a new population with perfect chromosomes.\"\"\"\n",
    "    # print(\"create_perfect_population8(population_size, target_sequence, alphabet)\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        perfect_string = '1'* len(target_sequence)\n",
    "        population.append(perfect_string)\n",
    "    # print(\"initial population\")\n",
    "    # print(population)\n",
    "    return population\n",
    "\n",
    "\n",
    "def calculate_fitnessk5(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome based on its similarity to the target sequence.\"\"\"\n",
    "    #  print(\"\\n *calculate_fitness8(chromosome, target_sequence)\")\n",
    "    fitness = 0\n",
    "    for i in range(len(chromosome)):\n",
    "        if chromosome[i] == target_sequence[i]:\n",
    "            fitness += 1\n",
    "    # print(chromosome, \"  fitness: \", fitness)\n",
    "    return fitness\n",
    "\n",
    "def calculate_all_fitnessk5(population, target_sequence):\n",
    "    # print(\"\\n*calculate_all_fitness8(population, target_sequence)\")\n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = [calculate_fitnessk5(chromosome, target_sequence) for chromosome in population]\n",
    "    # print(\"fitness_scores: \", fitness_scores)\n",
    "    return fitness_scores\n",
    "\n",
    "def select_the_bestk5(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome for reproduction.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk5(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome_index = fitness_scores.index(best_fitness)\n",
    "    best_chromosome = population[best_chromosome_index]\n",
    "    # print(\"\\n*select_the_bestk5(population)\")\n",
    "    # print(\"best_fitness = \", best_fitness)\n",
    "    # print(\"The chro = \", best_chromosome )\n",
    "    return best_fitness, best_chromosome\n",
    "\n",
    "def clone_populationk5(best_chromosome, population_size):\n",
    "    \"\"\"Creates a new population by cloning the best chromosome.\"\"\"\n",
    "    return [best_chromosome] * population_size\n",
    "\n",
    "\n",
    "def mutatek5(chromosome, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "                mutated_chromosome_list[i] = random.choice(alphabet)\n",
    "                counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "def mutate_allk5(population, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    touched_numbers = [] # List to store the number of touched sites for each chromosome\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek5(chromosome, mutation_rate, alphabet)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "        touched_numbers.append(counter)\n",
    "    return mutated_population\n",
    "\n",
    "def crossoverk5(parent1, parent2, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, len(target_sequence))\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "def crossover_allk5(population, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 =  population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk5(parent1, parent2, target_sequence, recombination_rate)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "def run_one_generationk5(population_size, population, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    # Calculate fitness scores for the current population\n",
    "    best_fitness, best_chromosome  = select_the_bestk5(population, target_sequence)\n",
    "    # print(f\" best_chromosome {best_chromosome}, best_fitness {best_fitness}\")\n",
    "    # Clone the best to build the new population\n",
    "    new_population = []\n",
    "    if include_clone_populationk5:\n",
    "        new_population = clone_populationk5(best_chromosome, population_size)\n",
    "    if include_mutate_allk5:\n",
    "        new_population = mutate_allk5(new_population, mutation_rate, alphabet)\n",
    "    if include_crossover_allk5:\n",
    "        new_population = crossover_allk5(new_population, target_sequence, recombination_rate)\n",
    "    return best_fitness, best_chromosome, new_population\n",
    "\n",
    "\n",
    "def plot_Best_Fitnessk5(best_fitness_history, population_size, length_of_target_sequence):\n",
    "    # Plot the fitness history\n",
    "    plt.plot(best_fitness_history, label='Best Fitness', color='blue')\n",
    "    plt.axhline(y=length_of_target_sequence, color='red', linestyle='--', linewidth=2)\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(f'Genetic Algorithm - Fitness over Generations, population_size = {population_size}, length_of_target_sequence = {length_of_target_sequence}')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def onela EvoluciónaryProcessk5( population_size, length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    all_touched_numbers = [] # List to store the arrays of number of touched sites for each generation\n",
    "    population = create_populationk5(population_size, target_sequence, alphabet)\n",
    "    #population = create_perfect_populationk5(population_size, target_sequence, alphabet)\n",
    "    # print(population)\n",
    "    best_fitness, best_chromosome = select_the_bestk5(population, target_sequence)\n",
    "    generation = 0\n",
    "    best_fitness_history = [] # List to store best fitness of each generation\n",
    "    best_fitness_history.append(best_fitness)\n",
    "    if print_best_fitness:\n",
    "        print(\"Generation  and best_fitness\")\n",
    "    #while best_fitness < len(target_sequence):\n",
    "    while generation < numberOfGenerations :\n",
    "        generation += 1\n",
    "        best_fitness, best_chromosome, population = run_one_generationk5(population_size, population, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        if print_best_fitness:\n",
    "            #print(f\" {generation}  {best_chromosome} {best_fitness}\")\n",
    "            print(f\" {generation} {best_fitness}\")\n",
    "        \n",
    "        best_fitness_history.append(best_fitness)\n",
    "    print(f\"\\nGeneration {generation}\")\n",
    "    print(f\"Population size: {population_size}\")\n",
    "    print(f\"Best chromosome: {best_chromosome}\")\n",
    "    print(f\"Best fitness: {best_fitness}\")\n",
    "    totalCost = population_size * generation\n",
    "    print(f\"Total cost = population size * number of generations: {totalCost}\")\n",
    "    if show_graph:\n",
    "        plot_Best_Fitnessk5(best_fitness_history, population_size, length_of_target_sequence)\n",
    "    # best_fitness_history is normalized to the length of the target sequence,\n",
    "    # so it is between 0 and 1. Thus, it can be comapred with other normalizations. \n",
    "    normalized_best_fitness_history = [fitness / length_of_target_sequence for fitness in best_fitness_history]\n",
    "    return normalized_best_fitness_history\n",
    "\n",
    "def draw_all_historiesk5(normalized_best_fitness_histories, population_size):\n",
    "    # Plot all the normalized best fitness histories together\n",
    "    # By the AI of VSCode. \n",
    "    for i in range(len(normalized_best_fitness_histories)):\n",
    "        # Range of x-axis is the number of generations for the current length of target sequence\n",
    "        x_range = range(len(normalized_best_fitness_histories[i]))\n",
    "        plt.plot(x_range, normalized_best_fitness_histories[i], label=f'Pop {population_sizesk5[i]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Normalized Best Fitness')\n",
    "    plt.title('Genetic Algorithm - Normalized Fitness over Generations for Population sizes')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def maink5(population_sizesk5, length_of_target_sequence, numbersOfGenerationsk5,    mutation_rate, recombination_rate, alphabet):\n",
    "    ''' An la Evoluciónary process is triggered for each population size.\n",
    "    The normalized best fitness history is stored in a list, \n",
    "    and then all the histories are plotted together.\n",
    "    A graphic is also plotted to show the tendency of the heights of the plateaus,\n",
    "    by plotting the last entry of the normalized best fitness history \n",
    "    for each length of target sequence.\n",
    "    Two fitting models are tested: a logarithmic model and a power model.\n",
    "    With the assitance of the AI of Visual Studio Code.\n",
    "    '''\n",
    "    print_parametersk5()\n",
    "    target_sequence = '1'* length_of_target_sequence \n",
    "    # List to store the normalized best fitness history for each length of target sequence   \n",
    "    normalized_best_fitness_histories = []\n",
    "    for i in range(len(population_sizesk5)):\n",
    "        population_size = population_sizesk5[i]\n",
    "        numberOfGenerations = numbersOfGenerationsk5[i]\n",
    "        normalized_best_fitness_history = onela EvoluciónaryProcessk5(population_size, length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        normalized_best_fitness_histories.append(normalized_best_fitness_history)\n",
    "    draw_all_historiesk5(normalized_best_fitness_histories, population_size)\n",
    "\n",
    "    # To see the tendency of the heights of the plateaus,   \n",
    "    # we can print and draw the last entry of \n",
    "    # the normalized best fitness history for each length of target sequence.\n",
    "    normalized_realized_plateau_heights = [history[-1] for history in normalized_best_fitness_histories]\n",
    "    print(\"\\nPopulation sizes and normalized realized plateau height\")\n",
    "    for i, (population_size, history) in enumerate(zip(population_sizesk5, normalized_best_fitness_histories)):\n",
    "        print(f\"population size {population_size}: {history[-1]}\")\n",
    "    # Draw the realized plateau heights\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(population_sizesk5, normalized_realized_plateau_heights, 'o-')\n",
    "    plt.xlabel('Population Size')\n",
    "    plt.ylabel('Normalized Realized Plateau Height')\n",
    "    plt.title('Tendency of Normalized Plateau Heights')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    end_time = time.time()\n",
    "    # Calculate the difference in seconds\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_time = timedelta(seconds=elapsed_time)\n",
    "    print(f\"Elapsed time: {elapsed_time}\")\n",
    "\n",
    "maink5(population_sizesk5, length_of_target_sequence, numbersOfGenerationsk5,    mutation_rate, recombination_rate, alphabet)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a601bc0",
   "metadata": {},
   "source": [
    "*Observación por la IA de Visual Studio Code*\n",
    ">\n",
    "> Las alturas de las mesetas son muy similares para todos los tamaños de población, y todas están alrededor de 0,5, lo que significa que la mejor aptitud es aproximadamente la mitad de la longitud de la secuencia objetivo. Esto sugiere que el tamaño de la población no tiene un impacto significativo en la altura de la meseta.\n",
    "\n",
    "*Nuestras observaciones para una tarea de 10000 bits.*\n",
    "\n",
    "1. Aleatoriedad + MAX crean poblaciones cuya proporción de tamaños resueltos es superior a la mitad. \n",
    "2. Con un tamaño de población de 10, la proporción de sitios resueltos en la población inicial es 0,6 y con 20, es 0,64. Esto significa que más de 6000 sitios se resuelven con poblaciones tan pequeñas.\n",
    "3. La ganancia marginal de los sitios resueltos disminuye rápidamente y con 5000 cromosomas, alcanza 0,75 y tiende a un crecimiento mínimo.  Las curvas parecen agruparse alrededor de 0,75. \n",
    "4. El gráfico de la proporción alcanzada de sitios resueltos o la altura de la meseta en relación con el tamaño de la población recuerda a una raíz cuadrada y a una función logarítmica. \n",
    " \n",
    "\n",
    "*Las salidas son muy estables.*\n",
    "\n",
    "En un experimento, el resultado fue: \n",
    "\n",
    "Tamaños de la población y altura de meseta realizada normalizada.    \n",
    "tamaño de la población 10: 0,6032.  \n",
    "tamaño de la población 20: 0,6371.   \n",
    "tamaño de la población 50: 0,6566.   \n",
    "tamaño de la población 100: 0,6763.   \n",
    "tamaño de la población 300: 0,6964.   \n",
    "tamaño de la población 500: 0,7091.   \n",
    "tamaño de la población 2000: 0,7348.   \n",
    "tamaño de la población 3000: 0,7394.   \n",
    "tamaño de la población 4000: 0.7468.   \n",
    "tamaño de la población 5000: 0,7467.   \n",
    "\n",
    "En otro experimento, el resultado fue el siguiente:\n",
    "\n",
    "Tamaños de las poblaciones y altura de meseta realizada normalizada.    \n",
    "tamaño de la población 10: 0,6145.     \n",
    "tamaño de la población 30: 0,6388.     \n",
    "tamaño de la población 50: 0,6562.     \n",
    "tamaño de la población 300: 0,6975.     \n",
    "tamaño de la población 500: 0,7117.     \n",
    "tamaño de la población 2000: 0,7364.     \n",
    "tamaño de la población 3000: 0,7382.     \n",
    "tamaño de la población 4000: 0,747.     \n",
    "tamaño de la población 5000: 0,7494.     \n",
    "\n",
    "Para los ejercicios a continuación, debes elegir uno de estos dos resultados.\n",
    "\n",
    "**Ejercicio.** Prueba un modelo de raíz cuadrada que se ajuste a los datos del gráfico de Altura de la meseta vs tamaño de la población.\n",
    ">\n",
    "> *Respuesta.* El siguiente código prueba un modelo de potencia $y = a x^b$ que se ajuste a los datos del gráfico de Altura de la meseta vs tamaño de la población. La potencia esperada $b$ debe ser alrededor de 0,5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf1a8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#program powerModelForNormalizedPlateauHeights.py\n",
    "# la Evolución battles against complexity,\n",
    "# given by the lengths of target sequences equal to 10000.\n",
    "# We vary the population size, and we compare the heights of the arisen plateaus. \n",
    "# We test a power model for the tendency of the heights of the plateaus. \n",
    "# \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#    \n",
    "# WE choose the following data:\n",
    "# Population sizes and normalized realized plateau height \n",
    "# population size 10: 0.6032.  \n",
    "# population size 20: 0.6371.   \n",
    "# population size 50: 0.6566.   \n",
    "# population size 100: 0.6763.   \n",
    "# population size 300: 0.6964.   \n",
    "# population size 500: 0.7091.   \n",
    "# population size 2000: 0.7348.   \n",
    "# population size 3000: 0.7394.   \n",
    "# population size 4000: 0.7468.   \n",
    "# population size 5000: 0.7467. \n",
    "#  \n",
    "population_sizesk5 =                  [10,      20,     50,       100,      300,     500,    2000,    3000,    4000,   5000 ]\n",
    "normalized_realized_plateau_heights = [0.6032, 0.6371, 0.6566,  0.6763,  0.6964,  0.7091,  0.7348,  0.7394,  0.7468, 0.7467 ]\n",
    "print(\"\\nPopulation sizes and normalized realized plateau height\")\n",
    "for i, (population_size, normalized_realized_plateau_height) in enumerate(zip(population_sizesk5, normalized_realized_plateau_heights)):\n",
    "    print(f\"population size {population_size}: {normalized_realized_plateau_height}\")\n",
    "# Draw the realized plateau heights\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(population_sizesk5, normalized_realized_plateau_heights, 'o-')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Tendency of Normalized Plateau Heights')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# A power model is tested\n",
    "# Power model: y = a * x^b\n",
    "# Taking logarithms\n",
    "# ln y = ln(a) + b ln(x). This is a linear model\n",
    "x = np.array(population_sizesk5)\n",
    "y = np.array(normalized_realized_plateau_heights)\n",
    "# Fit the power model\n",
    "log_x = np.log(x)\n",
    "log_y = np.log(y)\n",
    "coefficients = np.polyfit(log_x, log_y, 1)\n",
    "a = np.exp(coefficients[1])\n",
    "b = coefficients[0]\n",
    "print(f\"Power model: y = {a:.4f} * x^{b:.4f}\")  \n",
    "# Models and data are plotted together\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y , 'o', label='Data')\n",
    "plt.plot(x, a * x**b , label=f'Power model: y = {a:.4f} * x^{b:.4f} ')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Power Model for Normalized Plateau Heights')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9059176",
   "metadata": {},
   "source": [
    "*Nuestra observación.* El modelo produce un ajuste muy bueno. La potencia no es una raíz cuadrada. Es 0,0319.\n",
    "\n",
    "**Ejercicio.** Prueba un modelo logarítmico $y = a + b \\log x$ que se ajuste a los datos del gráfico Altura de la meseta vs tamaño de la población.\n",
    ">\n",
    ">*Respuesta.* El código siguiente pone a prueba un modelo de logaritmo  $y = a + b \\log x$ que se ajuste a los datos del gráfico de Altura de la meseta vs tamaño poblacional.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb281fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#program logModelForHeightsOfPlateaus.py\n",
    "# la Evolución battles against complexity,\n",
    "# given by the lengths of target sequences equal to 10000.\n",
    "# We vary the population size, and we compare the heights of the arisen plateaus. \n",
    "# We test a logarithmic model for the tendency of the heights of the plateaus.   \n",
    "#     \n",
    "population_sizesk5 =                  [10,      20,     50,       100,      300,     500,    2000,    3000,    4000,   5000 ]\n",
    "normalized_realized_plateau_heights = [0.6032, 0.6371, 0.6566,  0.6763,  0.6964,  0.7091,  0.7348,  0.7394,  0.7468, 0.7467 ]\n",
    "print(\"\\nPopulation sizes and normalized realized plateau height\")\n",
    "for i, (population_size, normalized_realized_plateau_height) in enumerate(zip(population_sizesk5, normalized_realized_plateau_heights)):\n",
    "    print(f\"population size {population_size}: {normalized_realized_plateau_height}\")\n",
    "# Draw the realized plateau heights\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(population_sizesk5, normalized_realized_plateau_heights, 'o-')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Tendency of Normalized Plateau Heights')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    " \n",
    "\n",
    "# A logarithmic model is tested \n",
    "# y = a + b ln(x). This is a linear model in ln(x)\n",
    "log_x = np.log(x)\n",
    "coefficients_log = np.polyfit(log_x, y , 1)\n",
    "a_log = coefficients_log[1]\n",
    "b_log = coefficients_log[0]\n",
    "print(f\"Logarithmic model: y = {a_log:.4f} + {b_log:.4f} * log( x)\")\n",
    "# Models and data are plotted together\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y, 'o', label='Data')\n",
    "plt.plot(x, a_log + b_log * log_x  , label=f'Logarithmic model: y = {a_log:.4f} + {b_log:.4f} * log( x)')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title(' Log Model for Normalized Plateau Heights')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548f30ea",
   "metadata": {},
   "source": [
    "*Nuestra observación.* El modelo logarítmico produce un ajuste muy bueno.\n",
    "\n",
    "Tenemos dos modelos que encajan bastante bien con los datos, el modelo de potencia y el modelo logarítmico. Se basan en la observación, más exactamente, en la forma del gráfico de alturas de meseta normalizadas observadas frente al tamaño de la población. La observación es el primer paso del método científico. A continuación, necesitamos modelos matemáticos para explicar las observaciones. Sin embargo, ¿estaremos olvidando que ya tengamos una teoría para entender la Evolución?\n",
    "\n",
    "**Ejercicio.** El fundamento de la Evolución es la selección del más apto, un procedimiento que es modelado por MAX. Utilice MAX para proponer un modelo de la dependencia de las alturas de meseta normalizadas observadas del tamaño de la población. Pruebe su modelo.\n",
    "\n",
    "*Respuesta.*  La selección del más apto cuando se proporciona variabilidad recursiva para nuestro problema de emparejar una cadena binaria de longitud $H$  se modela por MAX. Este procedimiento toma el máximo de  $H$ distribuciones binomiales $Bi(H, 1/2)$. Esto significa que la Evolución toma el mejor cromosoma, que es aquel en el que el número de sitios resueltos es máximo, dado que la aleatoriedad posiciona en cada sitio un 1 con probabilidad $1/2$ y un 0 con la misma probabilidad.  Así que exploremos una simple directiva: la Evolución no puede ir más allá del poder de MAX. El valor medio de MAX es proporcional a la raíz cuadrada del log del tamaño de la población $x$. Tenemos, $ \\overline{max} = k \\sqrt{\\log(x)} $. Esto permite proponer que la altura normalizada de la meseta debe seguir un modelo de log de raíz cuadrada: $y = a + b \\sqrt{\\log (x)} $. Necesitamos probar este modelo.  \n",
    ">\n",
    "> El siguiente código prueba el modelo que propone que la altura normalizada de la meseta $y$ debe seguir un modelo de raíz cuadrada: $y = a + b \\sqrt{\\log (x)} $ donde $x$ es el tamaño poblacional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e4e95bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#program squareRootOfLogModelForHeightsOfPlateaus.py\n",
    "# la Evolución battles against complexity,\n",
    "# given by the lengths of target sequences equal to 10000.\n",
    "# We vary the population size, and we compare the heights of the arisen plateaus. \n",
    "# We test a square-root-of-logarithmic model.  \n",
    "#     \n",
    "population_sizesk5 =                  [10,      20,     50,       100,      300,     500,    2000,    3000,    4000,   5000 ]\n",
    "normalized_realized_plateau_heights = [0.6032, 0.6371, 0.6566,  0.6763,  0.6964,  0.7091,  0.7348,  0.7394,  0.7468, 0.7467 ]\n",
    "print(\"\\nPopulation sizes and normalized realized plateau height\")\n",
    "for i, (population_size, normalized_realized_plateau_height) in enumerate(zip(population_sizesk5, normalized_realized_plateau_heights)):\n",
    "    print(f\"population size {population_size}: {normalized_realized_plateau_height}\")\n",
    "# Draw the realized plateau heights\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(population_sizesk5, normalized_realized_plateau_heights, 'o-')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Tendency of Normalized Plateau Heights')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Square-root-of-log model.\n",
    "sqrtlog_x = (np.log(x))**0.5\n",
    "coefficients_log = np.polyfit(sqrtlog_x, y , 1)\n",
    "a_log = coefficients_log[1]\n",
    "b_log = coefficients_log[0]\n",
    "print(f\"Square-root-of-log model: y = {a_log:.4f} + {b_log:.4f} * [(log(x)** 0.5]\")\n",
    "# Models and data are plotted together\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y, 'o', label='Data')\n",
    "plt.plot(x, a_log + b_log * sqrtlog_x  , label=f'Square-root-of-log model: y = {a_log:.4f} + {b_log:.4f} *[(log(x)** 0.5] ')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title(' Square-root-of-log Model for Normalized Plateau Heights')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d218e1",
   "metadata": {},
   "source": [
    "*Observación.* El modelo de raíz cuadrada es muy bueno.\n",
    "\n",
    "**Ejercicio.** Hemos probado tres modelos y todos son buenos. Formula un criterio cuantitativo para decidir cuál es el mejor e impleméntalo. \n",
    ">\n",
    "> *Respuesta.* Nuestros modelos son funciones definidas sobre el mismo conjunto, que además es discreto. Así que lucen como coordenadas de un vector o array. Para medir la diferencia, usamos la distancia cartesiana entre vectores. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3942f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#program comparingThreeModelsForHeightOfPlateau.py\n",
    "# la Evolución battles against complexity,\n",
    "# given by the lengths of target sequences equal to 10000.\n",
    "# We vary the population size, and we compare the heights of the arisen plateaus. \n",
    "# We also test three fitting models for the tendency of the heights of the plateaus: \n",
    "# a power model, a logarithmic model, and a square-root-of-logarithmic model.  \n",
    "# To decid which is the best, we measure the distance between \n",
    "# the data points and the fitted curve for each model.\n",
    "# The best model is the one with the smallest distance or error.\n",
    "\n",
    "population_sizesk5 =                  [10,      20,     50,       100,      300,     500,    2000,    3000,    4000,   5000 ]\n",
    "normalized_realized_plateau_heights = [0.6032, 0.6371, 0.6566,  0.6763,  0.6964,  0.7091,  0.7348,  0.7394,  0.7468, 0.7467 ]\n",
    "print(\"\\nPopulation sizes and normalized realized plateau height\")\n",
    "for i, (population_size, normalized_realized_plateau_height) in enumerate(zip(population_sizesk5, normalized_realized_plateau_heights)):\n",
    "    print(f\"population size {population_size}: {normalized_realized_plateau_height}\")\n",
    "# Draw the realized plateau heights\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(population_sizesk5, normalized_realized_plateau_heights, 'o-')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Observed Tendency of Normalized Plateau Heights')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# A distance between vectors measures the discrepancy between x and y.\n",
    "# The smaller the distance, the better the model fits the data.\n",
    "def distance(x,y):\n",
    "    dist2 = 0\n",
    "    for i in range(len(x)):\n",
    "        dist2 += (x[i] - y[i])**2\n",
    "    dist = np.sqrt(dist2)\n",
    "    return dist\n",
    "\n",
    "# A power model is tested\n",
    "# Power model: y = a * x^b\n",
    "# Taking logarithms\n",
    "# ln y = ln(a) + b ln(x). This is a linear model\n",
    "x = np.array(population_sizesk5)\n",
    "y = np.array(normalized_realized_plateau_heights)\n",
    "# Fit the power model\n",
    "log_x = np.log(x)\n",
    "log_y = np.log(y)\n",
    "coefficients = np.polyfit(log_x, log_y, 1)\n",
    "a = np.exp(coefficients[1])\n",
    "b = coefficients[0]\n",
    "print(f\"Power model: y = {a:.4f} * x^{b:.4f}\")  \n",
    "print(f\"Error of Power Model = {distance(y, a * x**b ):.4f}\")\n",
    "\n",
    "'''\n",
    "# Models and data are plotted together\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y , 'o', label='Data')\n",
    "plt.plot(x, a * x**b , label=f'Power model: y = {a:.4f} * x^{b:.4f} ')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Power Model for Normalized Plateau Heights')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "'''\n",
    "\n",
    "log_x = np.log(x)\n",
    "coefficients_log = np.polyfit(log_x, y , 1)\n",
    "a_log = coefficients_log[1]\n",
    "b_log = coefficients_log[0]\n",
    "print(f\"Logarithmic model: y = {a_log:.4f} + {b_log:.4f} * log( x)\")\n",
    "print(f\"Error of Logarithmic Model = {distance(y,  a_log + b_log * log_x ):.4f}\")\n",
    "\n",
    "'''\n",
    "# Models and data are plotted together\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y, 'o', label='Data')\n",
    "plt.plot(x, a_log + b_log * log_x  , label=f'Logarithmic model: y = {a_log:.4f} + {b_log:.4f} * log( x)')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title(' Log Model for Normalized Plateau Heights')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "'''\n",
    "\n",
    "\n",
    "# Square-root-of-log model.\n",
    "sqrtlog_x = (np.log(x))**0.5\n",
    "coefficients_log = np.polyfit(sqrtlog_x, y , 1)\n",
    "a_sqrtlog = coefficients_log[1]\n",
    "b_sqrtlog = coefficients_log[0]\n",
    "print(f\"Square-root-of-log model: y = {a_sqrtlog:.4f} + {b_sqrtlog:.4f} * [(log(x)** 0.5]\")\n",
    "print(f\"Error of Square-root-of-log Model = {distance(y,  a_sqrtlog + b_sqrtlog * sqrtlog_x ):.4f}\")\n",
    "'''\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y, 'o', label='Data')\n",
    "plt.plot(x, a_sqrtlog + b_sqrtlog * sqrtlog_x  , label=f'Square-root-of-log model: y = {a_sqrtlog:.4f} + {b_sqrtlog:.4f} *[(log(x)** 0.5] ')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title(' Square-root-of-log Model for Normalized Plateau Heights')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "'''\n",
    "\n",
    "# All three models and data are plotted together for comparison\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(x, y, 'o', label='Data')\n",
    "plt.plot(x, a_sqrtlog + b_sqrtlog * sqrtlog_x  , label=f'Square-root-of-log model: y = {a_sqrtlog:.4f} + {b_sqrtlog:.4f} *[(log(x)** 0.5] ')\n",
    "plt.plot(x, a * x**b , label=f'Power model: y = {a:.4f} * x^{b:.4f} ')\n",
    "plt.plot(x, a_log + b_log * log_x  , label=f'Logarithmic model: y = {a_log:.4f} + {b_log:.4f} * log( x)')\n",
    "plt.xlabel('Population Size')\n",
    "plt.ylabel('Normalized Realized Plateau Height')\n",
    "plt.title('Three models for Normalized Plateau Heights as function of population size')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c91ce0",
   "metadata": {},
   "source": [
    "*Nuestra observación. * Comparamos tres modelos de las alturas de meseta normalizadas en función del tamaño de la población, un modelo de potencia, un modelo logarítmico y un modelo Square-root-of_log o cuadrado-raíz-logarítmico. Según el gráfico, el mejor modelo es el Square-root-of_log Model porque su ajuste es el mejor tanto para tamaños de población bajos como altos. Los otras dos parecen producir predicciones muy superiores a las observadas para un gran valor de tamaño poblacional. Esta impresión subjetiva concuerda con los resultados de la medida objetiva que es la raíz cuadrada de la suma de las desviaciones cuadradas para cada punto. El error producido por el modelo Square-root-of_log es 0.0105, mientras que para el modelo Log es 0.0202 y para el modelo de potencia es 0.0241. Así, la precisión del modelo Square-root-of_log es muy alta y duplica la de los otros dos.\n",
    "\n",
    "**Ejercicio.** Un uso muy simple de MAX nos ha permitido proponer un modelo exitoso de un observable de la Evolución. Pero el problema planteado es complicado, así como la Evolución. Esas complicaciones son captadas por el modelo Underdog, que es mucho más complejo que MAX. Sin embargo, una aplicación directa de MAX ha sido exitosa. Explicar este misterio, es decir, encontrar la predicción del modelo Underdog, y probar que con alguna aproximación, se recupera el modelo recién propuesto. \n",
    ">\n",
    ">*Answer.* La predicción del Modelo del Perdedor y su test se encuentran más abajo.   \n",
    "\n",
    "**Ejercicio.** Calcula cuál es el costo de lograr la perfección, es decir, alcanzar una altura de meseta normalizada de 1.\n",
    ">\n",
    "> *Respuesta.* Usamos el modelo Square-root-of_log para calcular la predicción del costo de perfección para una tarea de emparejar 10000 sitios binarios. Dice que la altura de la meseta normalizada $y$ como una función del tamaño de la población $x$ es\n",
    ">>\n",
    ">> $ y = 0.4591 + 0.0997 * [(log(x))** 0.5]$\n",
    ">>\n",
    "> Así, si $y = 1$, \n",
    ">>\n",
    ">> $ 1 = 0.4591 + 0.0997 * [(log(x))** 0.5]$\n",
    ">>\n",
    ">> $ 1 - 0.4591 = 0.0997 * [(log(x))** 0.5]$\n",
    ">>\n",
    ">> $ 0.5409 = 0.0997 * [(log(x))** 0.5]$\n",
    ">>\n",
    ">> $ \\frac{0.5409}{0.0997} =  [(log(x))** 0.5]$\n",
    ">>\n",
    ">> $ 5.4253 =  \\sqrt{log(x)}$\n",
    ">>\n",
    ">> $ 5.4253^2 =   log(x) $\n",
    ">>\n",
    ">> $ 29.4336 =   log(x) $\n",
    ">>\n",
    ">> $x = e^{29.4336} = 6 \\times 10^{12}$\n",
    ">>\n",
    "Así que, para emparejar 10000 bits, necesitamos $6 \\times 10^{12} $ cromosomas. Esto no es mayor problema para una supercomputadora. \n",
    "\n",
    "**Desafío.** Utilice MAX para demostrar que el costo de la perfección para la Evolución, el tamaño de población necesario, es una función exponencial de la longitud de la entrada, es decir, del nivel de complejidad.  Decimos simplemente: el costo de la perfección para la Evolución es exponencial en la longitud de la entrada. \n",
    "\n",
    "**Ejercicio.** Según lo que ves en esta contribución, ¿Qué predicción de la Teoría Evolutiva es un fracaso manifiesto y la convierte en una teoría obviamente falsa? \n",
    ">\n",
    "> *Respuesta.* Vemos aquí que \n",
    ">\n",
    ">> 1. La Evolución es muy eficiente para resolver problemas muy simples, y para producir aproximaciones a los complejos.  \n",
    ">> 2. La Evolución puede resolver perfectamente problemas complejos. Pero, \n",
    ">> 3. El costo de lograr la perfección implica una función exponencial en la complejidad de la tarea. \n",
    ">> 4. Si los recursos no son suficientes, la altura de la meseta realizada está en algún lugar dentro de la mediocridad y la perfección. \n",
    ">> 5. Si el tiempo es ilimitado, puedes esperar hasta que una fluctuación feliz resuelva tu problema sin necesidad de un gran tamaño poblacional. Cuando el tiempo es ilimitado, esto es reproducible. Esto también puede suceder en tiempo finito pero no es reproducible, no es científico. Si alguien cree esto, su fe es ciega. \n",
    ">> 6. Es mucho más barato adivinar cadenas cortas con función que largas. Por lo tanto, esperamos en un largo Proceso Evolutivo el surgimiento de un supra-alfabeto muy rico, con una gran cantidad de símbolos que son tramos binarios bastante cortos. Si el tiempo es ilimitado, esos fragmentos pueden dar origen a genes. Si el tiempo es limitado, como en nuestro universo presente, la Evolución explorará con sus fluctuaciones el mundo infinito de baja calidad.  Por lo tanto, tenemos aquí una predicción obligatoria: si la Evolución nos creó, entonces el mundo, el registro fósil y tu propio cuerpo, todo debe estar lleno en productos cuya calidad esté dentro de la mediocridad y la perfección. Para tomar una decisión sobre la Teoría de la Evolución, basta con mirar tu cuerpo. Si está lleno de una miríada de defectos de anatomía y fisiología, la Teoría Evolutiva es verdadera. Si solo encuentras algunos cientos de imperfecciones en tu cuerpo, la Teoría Evolutiva es falsa porque las imperfecciones son esperadas por miríadas. Además, ve a un museo paleontológico. Si está lleno de todo tipo de esqueletos con deformaciones, la teoría evolutiva es verdadera. Pero si encuentras fósiles que te maravillan a ti y a los especialistas, la Teoría de la Evolución es falsa.\n",
    ">\n",
    "> En resumen: la predicción obligatoria de la Teoría Evolutiva es que el registro fósil y nuestro propio cuerpo deben estar llenos de miríadas de defectos, tanto anatómicos como fisiológicos. Para tomar una decisión, mira tu propio cuerpo. \n",
    "\n",
    "**Ejercicio.** Explica de manera clara y concisa por qué todos los científicos consideran que la Teoría de la Evolución es un hecho de la Naturaleza. ¿Ves algún error en alguna parte?\n",
    ">\n",
    "> *Respuesta.* Varios factores colaboran para llevar a los científicos a creer que la Teoría de la Evolución es un hecho de la Naturaleza, es decir, que todas las especies aparecieron por la Evolución:\n",
    ">> 1. [La creación de una nueva especie de mariposa por hibridación de dos especies madres se ha reproducido en el laboratorio](https://doi.org/10.1038/nature04738). Esto significa que los híbridos son fértiles y pueden reproducirse entre sí y que los cruces de la nueva especie con cualquiera de las especies madre son infértiles y/o que estos son impedidos por señales específicas de la especie. [Parece que la coloración de las alas no es suficiente para prevenir el cortejo](https://doi.org/10.1038/s41586-021-03330-8), por lo que la señalización etológica y bioquímica podría ser decisiva. El error es imaginar que esto es una prueba de la Teoría Evolutiva. Esto es falso porque el número de las especies de vertebrados y de plantas son del orden de millones, y de microorganismos es del orden de cientos de millones. Entonces, el sentido común predice miles de eventos de especiación reproducibles y que cada mujer debe saber cómo hacer nuevas especies de plantas en casa. Falso. Si se reconoce que hay una batalla contra la complejidad, y que la Evolución necesita tiempo, los experimentos correspondientes deben llevarse a cabo; por ejemplo, los chimpancés deben ser seleccionados en el zoológico para caminar erguidos. Esto debe hacerse.  \n",
    ">> 2. Según los resultados que vemos aquí, la Evolución hace coincidir casi instantáneamente más de 6000 bits de 10000 con un tamaño poblacional de apenas 10.  ¡Esto es simplemente asombroso! Esto implica que si la vida surgió por la Evolución, no debe existir registro fósil de la batalla de la Evolución contra la complejidad desde nada a algo más allá de la mediocridad. Los resultados victoriosos como estos, referidos a diversos campos de la biología, son reportados con frecuencia.  Ejemplo: [Los biomorfos de Dawkins](https://cs.lmu.edu/~ray/notes/biomorphs/), que muestra que los ideogramas de variación morfológica pueden ser generados por algoritmos muy simples. En general, me parece que el poder de la Evolución Aplicada, tanto in silico como en la naturaleza, está limitado sólo por nuestra imaginación. Además de la carga de la complejidad. \n",
    ">> 3. Las instancias de alta complejidad de problemas difíciles se realizan de forma rutinaria y han demostrado ser solucionables por Evolución, por ejemplo, [la creación de software y hardware por parte de la Evolución](https://link.springer.com/journal/10710). Lo que uno debe imaginar es que la Evolución puede resolver cualquier instancia si se le concede el tiempo suficiente. Esto podría ser cierto, pero el error es ignorar el cumplimiento de una ley: se deben producir enormes volúmenes de basura cuando la Evolución lucha contra la complejidad. Y, ¿qué es la Basura Evolutiva? Es la generalidad de individuos con miríadas de todo tipo de malformaciones y disfunciones. No hay excepción a esta regla. Ni siquiera una. Por eso la Teoría de la Evolución es obviamente falsa.\n",
    "\n",
    "\n",
    "**Ejercicio.** Responde a la siguiente objeción contra la afirmación de que la Evolución tiene un costo exponencial en la longitud de la cadena binaria objetivo.  \n",
    ">\n",
    ">> La afirmación propuesta de la Teoría Evolutiva se basa en evidencia experimental, que fue producida por simulaciones de la Evolución, por Algoritmos Genéticos. Por lo tanto, es una extrapolación estadística. Ahora, cada generalización estadística tiene un rango de validez muy limitado porque muy pronto, la incertidumbre es tan grande que cualquier cosa se vuelve posible, como si no se hubiera hecho ningún experimento.\n",
    ">\n",
    "> *Respuesta.* La objeción es correcta. Las extrapolaciones que se basan en generalizaciones estadísticas tienen un rango de validez muy limitado porque la incertidumbre aumenta rápidamente.  Por eso se necesitan modelos matemáticos para hacer extrapolaciones.\n",
    "\n",
    "Utilicemos, pues, nuestro modelo matemático para hacer predicciones. \n",
    "\n",
    "**Ejercicio.** Utilice el modelo de Underdog para demostrar que el costo de la Evolución es exponencial en la complejidad de la tarea, es decir, en la longitud de la cadena binaria objetivo.\n",
    ">\n",
    "*Respuesta.* En la siguiente sección, revisamos nuestro modelo matemático basado en MAX, y luego probamos la afirmación del costo exponencial. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5544a45a",
   "metadata": {},
   "source": [
    "# EL MODELO DEL PERDEDOR O UNDERDOG\n",
    "\n",
    "Nuestro propósito es entender la Evolución. Esto significa construir modelos matemáticos que capturen su esencia y predigan su comportamiento en su batalla contra la complejidad. Con ese objetivo, hemos dado a la Evolución la tarea de resolver el *Desafío Shakespeare Binario*, que consiste en emparejar o adivinar una cadena binaria objetivo de longitud $H$.  Esta longitud representa fielmente la complejidad porque cuanto más alta es, más recursos necesita la Evolución para completar la tarea. La Evolución utiliza la población y el tiempo. La población permite hacer experimentos en paralelo, el tiempo permite hacer experimentos en serie. Hemos modelado a la Evolución para resolver este desafío por el el Dúo Mutación-MAX, que es una versión débil de la Evolución y consta de Selección del más apto, modelado por MAX, seguido de su clonación para generar la próxima generación, y mutación para crear variabilidad. Nuestro modelo no contiene recombinación. Diríamos que la recombinación mezcla las cartas, añadiendo más variabilidad a la producida por la mutación y curando la homogeneización causada por la clonación del más apto. Sin embargo, no hemos sido capaces de detectar un posible papel importante de la recombinación.\n",
    "\n",
    "Nuestro modelo matemático de la Evolución explica los macroobservables utilizando microobservaciones. Nuestra macro-variable ha sido el número de sitios resueltos que representa la porción de la tarea resuelta y que crece con las generaciones. La Evolución recoge pequeños avances de esta variable generación tras generación.  Las microobservaciones registran las fluctuaciones que se crean por la mutación debido a su naturaleza estocástica, donde una fluctuación es una desviación hacia afuera del valor medio o esperado de la variable observable dada. El cambio de lo macro-observable es un reflejo de los micro-observables. O, *El combustible de la Evolución consiste en fluctuaciones*. \n",
    "\n",
    "Las fluctuaciones pueden ser positivas o negativas y el modelo de Underdog dice que la Evolución funciona en ambos tipos, no solo positivo. La relación cambia con el tiempo, con las generaciones, pero nos parece que el comportamiento dominante es suficiente para producir buenas predicciones.  Aceptando esto, las micro-observaciones se incluyen con los siguientes sabores: \n",
    "\n",
    " 1. La fluctuación en el número de sitios tocados por mutación es muy negativa. \n",
    " 2. Las fluctuaciones en el número de sitios tocados y resueltos se equilibran, las fluctuaciones positivas y negativas juegan un papel similar en la meseta. En la rampa son positivas. \n",
    " 3. La fluctuación en el número de sitios no tocados por mutaciones es positiva, excepto al principio.\n",
    " 4. Las fluctuaciones en el número de sitios sin tocar y resueltos son muy positivas. Tenemos fluctuaciones positivas súper potentes. \n",
    "\n",
    "\n",
    "Además, si la tendencia es a bajar, cuanto mayor sea el tamaño de la población, más profunda será la caída. Si, por el contrario, la tendencia es al alza, cuanto mayor sea el tamaño de la población, mayor será el aumento. Esto significa que todas las fluctuaciones, positivas o negativas, están conectadas a MAX y al número de cromosomas o de monos. \n",
    "\n",
    "\n",
    "Las especificaciones de nuestro modelo son las siguientes:\n",
    "\n",
    "**********\n",
    "EL MODELO DEL PERDEDOR O UNDERDOG\n",
    "**********\n",
    "\n",
    "\n",
    "Dividimos el proceso de mutación en dos etapas. En la primera, los sitios se tocan o no por la mutación. En la segunda, la mutación posiciona un uno o un cero al azar en cada sitio tocado. \n",
    "\n",
    "El modelo del Underdog está definido por las siguientes limitaciones:\n",
    "\n",
    "> $ \\mu = $ tasa de mutación, constante\n",
    ">\n",
    "> $H = $ longitud de la cadena objetivo, número total de sitios. La cadena objetivo consiste de $H$ unos.\n",
    ">\n",
    "> $m = $ número de distribuciones binomiales, o de monos, o de cromosomas que proponen cada uno una posible solución.\n",
    ">\n",
    "> $M_t = $ número de sitios resueltos en el más apto en la generación $t$\n",
    ">\n",
    "> $I = M_o = $ número inicial de sitios correspondientes\n",
    ">>>\n",
    ">> $ = \\frac{H}{2} +  \\sqrt{\\frac{H}{k}\\ln m  }  $.\n",
    ">>\n",
    ">> $ = \\frac{H}{2} +  \\lambda_k\\sqrt{H \\ln m  }  $.\n",
    "\n",
    "> $I$ es una observable, cuyo valor predicho puede ser puesto a prueba. Debido a que cada cromosoma es una cadena binaria y creada al azar con probabilidades iguales de 1 y 0, se espera que la mitad de los sitios sean resueltos. Esto está representado por $ \\frac{H}{2}$. El otro término corresponde al valor MAX-imal debido a la variación de los sitios resueltos entre la población de cromosomas. Este es el primer efecto de las fluctuaciones, un efecto poblacional que depende de  $m$, el número de cromosomas. Estas fluctuaciones son positivas.\n",
    ">\n",
    "\n",
    "La segunda predicción observable es el número de sitios tocados, $T$. Si la tasa de mutación es $ \\mu$, y la longitud de la cadena objetivo es $H$, el valor esperado de $T$ es $T =  \\mu H$. Pero hay variabilidad entre la población. Max elige lo mejor, el cual puede diferir del valor esperado. El valor esperado de $T$ según Max es constante e igual a\n",
    ">\n",
    "> $T= $ número de sitios $ = \\mu H +  \\lambda_T \\sqrt{H \\mu (1 - \\mu) } \\sqrt{ \\ln (m)} $\n",
    ">\n",
    "> donde el segundo término representa el efecto poblacional captado por MAX.  \n",
    "> \n",
    "La tercera observable es $ s = $ número de sitios resueltos entre los tocados. Está dado por\n",
    ">\n",
    "$ s =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  $. Es una constante y un efecto poblacional.\n",
    ">\n",
    "> Tenemos también:\n",
    ">\n",
    "> $H-T = $  número de sitios sin tocar\n",
    ">\n",
    "> $p_t = \\frac{M_t}{H}$ proporción de sitios resueltos en el cromosoma ganador de la generación $t$.  \n",
    ">\n",
    "También:\n",
    ">\n",
    ">  $ r = $ número de sitios intocados que estaban resueltos en el cromosoma ganador de la generación  $t$.\n",
    ">\n",
    "> $ r =  (H-T)p_t +  \\lambda_r \\sqrt{(H-T)p_t(1-p_t)} \\sqrt{ \\ln (m)} $.\n",
    ">\n",
    "> Utilizamos una aproximación de $r$ en el trabajo anterior porque el modelo resultante es fácilmente solucionable. Ignorémoslo por el momento. \n",
    ">\n",
    "> Por último, la predicción del número de sitios resueltos en el cromosoma ganador para la próxima generación viene dada por\n",
    ">\n",
    "> $M_{t+1} =  s + r = $ predicción para el ganador de la próxima generación\n",
    "\n",
    "Los parámetros a especificar son 4: $\\lambda_k$, $\\lambda_T$, $\\lambda_s$, $\\lambda_r$. Ya se ha considerado una asignación.\n",
    "**********\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99958924",
   "metadata": {},
   "source": [
    "\n",
    "## Prediciendo el costo de la perfección\n",
    "\n",
    "Según los resultados de nuestras simulaciones, cuando se aumenta el número de cromosomas, las curvas que describen la proporción de sitios resueltos o correspondidos se acumulan alrededor de 0,75. Por lo tanto, la perfección parece inalcanzable. Al contrario de esta apreciación, estamos seguros de que la Evolución puede alcanzar la perfección pero con un costo exponencial. Esto se demuestra de la siguiente manera:\n",
    "\n",
    "**Teorema.** El costo que la Evolución debe pagar por la perfección es exponencial en la longitud de la cadena binaria objetivo.\n",
    ">\n",
    "> *Demostración.* La perfección debe ser el estado estable del proceso evolutivo y significa que todos los sitios $H$ están resueltos, es decir, $M_t = M_{t+1} = H$, y que la fracción de sitios resueltos $p_t =  \\frac{M_t}{H} = 1$. Por lo tanto, la ecuación\n",
    "\n",
    "$M_{t+1} = s + r =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)p_t +  \\lambda_r \\sqrt{(H-T)p_t(1-p_t)} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "se convierte en\n",
    "\n",
    "$M_{t+1} = H =   \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)$.\n",
    "\n",
    "$0 =   \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }   -T$.\n",
    "\n",
    "$0 =   -\\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  } $.\n",
    "\n",
    "$ \\frac{T}{2} = \\lambda_s \\sqrt{T} \\sqrt{\\ln m  } $.\n",
    "\n",
    "$\\sqrt{\\ln m  } = \\frac{T}{2\\lambda_s \\sqrt{T}} $\n",
    "\n",
    "$\\sqrt{\\ln m  } = \\frac{\\sqrt{T}}{2\\lambda_s } $\n",
    "\n",
    "$ \\ln m   = [\\frac{\\sqrt{T}}{2\\lambda_s }]^2 $\n",
    "\n",
    "$ \\ln m   = \\frac{ T}{4 (\\lambda_s)^2 } $\n",
    "\n",
    "$  m   = e^{\\frac{ T}{4 (\\lambda_s)^2 }} $\n",
    "\n",
    "$T$ es el número de sitios que se ven afectados por la mutación. El valor esperado es $T =  \\mu H$, donde $ \\mu$ es la tasa de mutación por sitio, y $H$ es la complejidad de la tarea, que viene dada por la longitud de la cadena binaria objetivo. Por lo tanto, \n",
    "\n",
    "> El tamaño de población esperado $m$ para alcanzar la perfección es\n",
    ">>>\n",
    ">> $  m   = e^{\\frac{ \\mu H}{4 (\\lambda_s)^2 }} $\n",
    ">>>\n",
    "Esta ecuación dice que el costo de la perfección en una función exponencial de la complejidad. \n",
    "\n",
    "**Ejercicio.** Prueba que el costo de la imperfección también es exponencial.\n",
    ">\n",
    "> *Respuesta.* Una solución exacta a esta pregunta requeriría resolver el sistema de ecuaciones recursivas del modelo Underdog. Esto indicaría cuál es el valor de $M_t$ para cada generación $t$ en términos de la condición inicial y los parámetros del modelo. No nos parece realizable. Pero podemos plantear un límite superior a las posibilidades de la Evolución. Entonces, intentemos una aproximación: sustituyamos la fracción de sitios resueltos $p_t$ por $1/2$, que da a la expresión $p(1-p)$ su valor máximo. Así,\n",
    "\n",
    "$M_{t+1} = s + r =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)p_t +  \\lambda_r \\sqrt{(H-T)p_t(1-p_t)} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "se convierte en\n",
    "\n",
    "$M = s + r =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)\\frac{1}{2} +  \\lambda_r \\sqrt{(H-T)(\\frac{1}{2} )(1-\\frac{1}{2} )} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "cuando uno decide quedar satisfecho con la imperfección, uno toma  $M =\\omega H $ en donde $\\omega $ es la fracción objetivo de perfección, digamos,  0.9.  Tenemos:\n",
    "\n",
    "$\\omega H  =   \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)\\frac{1}{2} +  \\lambda_r \\sqrt{(H-T)(\\frac{1}{2} )(1-\\frac{1}{2} )} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "$\\omega H  =   \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)\\frac{1}{2} +  \\frac{\\lambda_r}{2} \\sqrt{(H-T)} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "$\\omega H  =   \\frac{T}{2} + \\lambda_s \\sqrt{T} \\sqrt{\\ln m}  + \\frac{1}{2}(H-T) +  \\frac{\\lambda_r}{2} \\sqrt{(H-T)} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "$\\omega H  -  \\frac{T}{2} - \\frac{1}{2}(H-T) =  \\lambda_s \\sqrt{T} \\sqrt{\\ln m}  +  \\frac{\\lambda_r}{2} \\sqrt{(H-T)} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "$\\omega H  -  \\frac{1}{2} ( T  + (H-T)) =  [\\lambda_s \\sqrt{T}  +  \\frac{\\lambda_r}{2} \\sqrt{H-T}] \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "$\\omega H  -  \\frac{1}{2}  H =  [\\lambda_s \\sqrt{T}  +  \\frac{\\lambda_r}{2} \\sqrt{H-T}] \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "$\\sqrt{ \\ln (m)}  = \\frac{\\omega H  -  \\frac{1}{2}  H} {\\lambda_s \\sqrt{T}  +  \\frac{\\lambda_r}{2} \\sqrt{H-T}} $.\n",
    "\n",
    "$ \\ln (m)  = [\\frac{\\omega H  -  \\frac{1}{2}  H} {\\lambda_s \\sqrt{T}  +  \\frac{\\lambda_r}{2} \\sqrt{H-T}} ]^2$.\n",
    "\n",
    "$ \\ln (m)  = [\\frac{(\\omega  -  \\frac{1}{2})  H} {\\lambda_s \\sqrt{T}  +  \\frac{\\lambda_r}{2} \\sqrt{H-T}} ]^2$.\n",
    "\n",
    "$ m   = e^{[\\frac{(\\omega  -  \\frac{1}{2})  H} {\\lambda_s \\sqrt{T}  +  \\frac{\\lambda_r}{2} \\sqrt{H-T}} ]^2}$.\n",
    "\n",
    "Because the mutation rate is usually low, $T = \\mu H << H$, and so \n",
    "\n",
    "$ m \\sim e^{[\\frac{(\\omega  -  \\frac{1}{2})  H} {  \\frac{\\lambda_r}{2} \\sqrt{H}} ]^2} $\n",
    "\n",
    "$ m \\sim e^{[\\frac{(\\omega  -  \\frac{1}{2})  \\sqrt{H}} {  \\frac{\\lambda_r}{2} } ]^2} $\n",
    "\n",
    "$m \\sim e^{[\\frac{(\\omega  -  \\frac{1}{2})^2 H} {  ( \\frac{\\lambda_r}{2})^2 } ]} $.\n",
    "\n",
    "\n",
    "Esta ecuación dice que si quieres superar la aleatoriedad, debes pagar un costo exponencial en la complejidad de la tarea. \n",
    "\n",
    "También, tomando derivadas a ambos lados con respecto a $ \\omega$: si quieres mejorar la calidad de tu solución, para aumentar la altura de la meseta, debes pagar un costo exponencial en la longitud de la cadena binaria objetivo. \n",
    "\n",
    "**Ejercicio.** Explicar el significado científico del costo exponencial que la Evolución debe pagar para alcanzar la perfección y aumentar la calidad.\n",
    ">\n",
    "> *Respuesta.* La Teoría Evolutiva dice que la Evolución es el creador de todas las especies en la Tierra. El costo exponencial de cualquier calidad por encima de la aleatoriedad significa que la Teoría Evolutiva es científica. Es decir: produce predicciones obligatorias cuya violación implica que dicha teoría es irremediablemente falsa. La más directa es la siguiente: \n",
    ">\n",
    ">> Si una especie apareció por la Evolución, aparte de algunos casos, existirán toneladas de Basura Evolutiva que daría testimonio de una tremenda batalla de la Evolución contra la complejidad. ¿O tal vez, alguien piense que es fácil crear una nueva especie con divergencia morfológica? Ahora, ¿qué es la Basura Evolutiva? Es la totalidad de los individuos que la Evolución utilizaba para intentar hacer algo mejor. Así, la Teoría Evolutiva tiene una predicción obligatoria: el registro fósil y nuestros propios cuerpos deben estar rellenos en todo tipo de malformaciones y fallos. Para saber qué especies fueron creadas por la Evolución, ve al museo. Si ves un esqueleto lleno de miradas de malformaciones, representa una especie que surgió por Evolución. De la misma manera, si tu esqueleto tiene miles de malformaciones y tu fisiología millones de fracasos, déjame aconsejarte que creas que tu especie apareció por la Evolución y que la Teoría Evolutiva es la verdadera explicación de tu existencia. De lo contrario, la Teoría Evolutiva es falsa y la Evolución no es tu creador.\n",
    "\n",
    "**Ejercicio.** El Método Científico dice que la Teoría Evolutiva es falsa. Expresa sucintamente si aceptas este veredicto y en qué es lo que crees.\n",
    ">\n",
    "> *Respuesta.* Creo que el Método Científico es una herramienta para rechazar teorías pero no para construirlas. Sin embargo, nos encanta inventar teorías y explicaciones y rechazamos una creencia solo cuando es conveniente para nosotros. En mi caso particular, su rechazo me es conveniente porque satisface mi intuición y, además, porque soy cristiano y creo firmemente que el Dios bíblico me creó con un esfuerzo extremadamente grande, y que, en términos prácticos\n",
    ">>>\n",
    ">>> **El Reino de Dios es menos juicio y más salvación**, porque Jesús dijo: *\"Si alguien escucha mis palabras pero no las cumple, yo no juzgo a esa persona. Porque no he venido a juzgar al mundo, sino a salvar al mundo [Juan 12:47]\"*.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612105f6",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Especificación del modelo del Underdog\n",
    "\n",
    "Nuestro modelo tiene parámetros que deben especificarse.  Ya hemos propuesto algunas asignaciones, pero podemos usar nuestras simulaciones actuales para intentar dar mejores valores. \n",
    "\n",
    "**Concurso.** Use simulaciones para encontrar una asignación de parámetros del modelo Underdog con el mayor rango de validez posible.\n",
    "\n",
    "*Inauguramos* este concurso con la estimación de un parámetro.  Nos limitamos aquí a asignaciones constantes, pero es más posible que los parámetros sean funciones del tamaño y la complejidad de la población. \n",
    "\n",
    "**Estimación de $ \\lambda_s$**. Para proponer una estimación del parámetro $ \\lambda_s$, podemos comparar dos resultados que se refieren al costo de la perfección. El primero es teórico, el segundo es estadístico. \n",
    "\n",
    "*Resultado teórico.*\n",
    ">\n",
    "> El tamaño de población esperado $m$ para alcanzar la perfección, es decir, todos los sitios binarios $H$ resueltos, es\n",
    ">>\n",
    ">> $  m   = e^{\\frac{ \\mu H}{4 (\\lambda_s)^2 }} $\n",
    ">>\n",
    "If $H = 10000$ and $\\mu = 0.1$,\n",
    ">>\n",
    ">>  $  m   = e^{\\frac{ 0.01 \\times 10000}{4 (\\lambda_s)^2 }}  = e^{\\frac{ 100}{4 (\\lambda_s)^2 }} $\n",
    ">>\n",
    "Tomando logaritmos a ambos lados:\n",
    ">>\n",
    ">> $ \\log (m)  = \\log (e^{ \\frac{100}{4 (\\lambda_s)^2}}) = \\frac{100}{4 (\\lambda_s)^2}$\n",
    ">>\n",
    "\n",
    "*Resultado estadístico.*\n",
    ">\n",
    "> Utilizamos el modelo Square-root-of-log para calcular la predicción del costo de perfección para una tarea de emparejar 10000 sitios binarios. Dice que la altura de la meseta normalizada $y$ como una función del tamaño de la población $x$ es\n",
    ">>>\n",
    ">> $ y = 0,4591 + 0,0997 * [(\\ log(x))** 0,5]$\n",
    ">>>\n",
    "Al reescribir este resultado, utilizando la nomenclatura del modelo teórico, obtenemos:\n",
    ">>>\n",
    ">> $ \\frac{M}{H} = 0,4591 + 0,0997 * [( \\log(m))** 0,5]$\n",
    ">>>\n",
    "La condición para la perfección es $M = H$, o $  \\frac{M}{H} = 1$. Entonces,\n",
    ">>\n",
    ">> $ 1  = 0.4591 + 0.0997 * [(\\log(m))** 0.5]$\n",
    ">>\n",
    ">> $0.5409 = 0.0997 * [(\\log(m))** 0.5]$\n",
    ">>\n",
    ">> $\\frac{0.5409}{0.0997} = [(\\log(m))** 0.5]$\n",
    ">>\n",
    ">> $ \\log (m) = [\\frac{0.5409}{0.0997}]^2 = 29.4337$\n",
    ">>\n",
    ">>\n",
    "*Igualando ambos resultados, el teórico con el estadístico, obtenemos*: \n",
    ">>\n",
    ">> $ \\log (m) = \\frac{100}{4 (\\lambda_s)^2} = 29.4337$\n",
    ">>\n",
    "Resolviendo $\\lambda_s$, obtenemos la estimación de nuestro parámetro:\n",
    ">>\n",
    ">> $(\\lambda_s)^2 =  \\frac{100}{4 \\times 29.4337} = 0.84937$\n",
    ">>\n",
    ">> $\\lambda_s = \\sqrt{ 0.84937} = 0.9216$\n",
    "\n",
    "\n",
    "## Predicción de la altura de la meseta.\n",
    "\n",
    "El modelo Underdog puede predecir la dependencia de la altura de la meseta como función de la longitud de la cadena objetivo y el tamaño de la población. Esto se puede hacer de la siguiente manera. Tenemos la ecuación\n",
    "\n",
    "$M_{t+1} = s + r =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)p_t +  \\lambda_r \\sqrt{(H-T)p_t(1-p_t)} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "Puesto que, $p_t = \\frac{M_t}{H} $, esta ecuación se convierte en la relación recurrente \n",
    "\n",
    "$M_{t+1} =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  + (H-T)\\frac{M_t}{H} +  \\lambda_r \\sqrt{(H-T)\\frac{M_t}{H}(1-\\frac{M_t}{H})} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "Si la meseta no es un artefacto creado por nuestra imaginación que confunde una función suavemente creciente con una meseta, ésta se define por:\n",
    "\n",
    "\n",
    "$M_{t+1} = M_{t} $,\n",
    "\n",
    "esto es\n",
    "\n",
    "$M_{t} =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m } + (H-T)\\frac{M_t}{H} + \\lambda_r \\sqrt{(H-T)\\frac{M_t}{H}(1-\\frac{M_t}{H})} \\sqrt{ \\ln (m)} $.\n",
    "\n",
    "\n",
    "**Ejercicio.** Haga que su asistente de IA resuelva $M_t$ de la ecuación anterior, digiera y reporte la solución.  \n",
    ">\n",
    "> *Respuesta.* La respuesta por Gemini de Google fue en verdad muy clara y muy bien organizada. Nuestro entendimiento de su trabajo es el siguiente: \n",
    "\n",
    "1. Reorganización:\n",
    "\n",
    "$M_{t} =  [\\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m }] + \\frac{H-T}{H} M_t + \\lambda_r  \\sqrt{ \\ln (m)} \\sqrt{\\frac{H-T}{H} M_t (1-\\frac{M_t}{H})} $.\n",
    "\n",
    "\n",
    "2. Para hacer el álgebra manejable, definamos las siguientes constantes:  \n",
    "\n",
    "$ A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $  \n",
    "\n",
    "$K = \\frac{H - T}{H}$\n",
    "\n",
    "$L = \\lambda_r \\sqrt{\\ln m}$  \n",
    "\n",
    "3. Con el uso de dichas constantes, la ecuación original deviene:  \n",
    "\n",
    " $M_{t}=A+KM_{t}+L\\sqrt{KM_{t}(1-\\frac{M_{t}}{H})}$. \n",
    "\n",
    "4. Para separar el término con la raíz cuadrada, substrayamos   $KM_{t}$ de ambos lados:\n",
    "\n",
    "$M_{t}- KM_{t} =A+L\\sqrt{KM_{t}(1-\\frac{M_{t}}{H})}$. \n",
    "\n",
    "$M_{t}(1-K)=A+L\\sqrt{KM_{t}(1-\\frac{M_{t}}{H})}$\n",
    "\n",
    "5. Notemos que\n",
    "\n",
    "$1 - K = 1 - \\frac{H - T}{H} = \\frac{T}{H}$. \n",
    "\n",
    "Así, \n",
    "\n",
    "$M_{t}(1-K)= M_{t} \\frac{T}{H} = A+L\\sqrt{KM_{t}(1-\\frac{M_{t}}{H})}$\n",
    "\n",
    "$T  \\frac{M_{t}}{H} = A+L\\sqrt{KM_{t}(1-\\frac{M_{t}}{H})}$\n",
    "\n",
    "Sea $x = \\frac{M_t}{H}$, then  $M_t = Hx$. Si sustituímos esto en la ecuación, obtenemos:\n",
    "\n",
    "$T  \\frac{M_{t}}{H} = Tx=A+L\\sqrt{KHx(1-x)} $\n",
    "\n",
    "$Tx-A=L\\sqrt{KHx(1-x)}$\n",
    "\n",
    "6. Elevemos al cuadrado en ambos lados para formar una cuadrática:  \n",
    "\n",
    "$(Tx-A)^{2}=L^{2}KHx(1-x)$\n",
    "\n",
    "$T^{2}x^{2}-2ATx+A^{2}=L^{2}KHx-L^{2}KHx^{2}$\n",
    "\n",
    "Reorganizando al modo tradicional: \n",
    "\n",
    "$ax^2 + bx + c = 0:$ \n",
    "\n",
    "$T^{2}x^{2} + L^{2}KHx^{2} -2ATx- L^{2}KHx +A^{2}=$\n",
    "\n",
    "\n",
    "$(T^{2}+L^{2}KH)x^{2}-(2AT+L^{2}KH)x+A^{2}=0$\n",
    "\n",
    "7.  Sea $B = L^2 K H $ \n",
    "\n",
    "Recordando que\n",
    "\n",
    "$L = \\lambda_r \\sqrt{\\ln m}$ \n",
    "\n",
    "y que\n",
    "\n",
    "$K = \\frac{H - T}{H}$\n",
    "\n",
    "obtenemos\n",
    "\n",
    " $B = L^2 K H = \\lambda_r^2  \\ln m \\frac{H - T}{H} H = \\lambda_r^2 (H - T) \\ln m$. \n",
    "\n",
    "8. La fórmula cuadrática es:\n",
    "\n",
    "$(T^{2}+B)x^{2}-(2AT+B)x+A^{2}=0$\n",
    "\n",
    " \n",
    "\n",
    "Despejando $x$, y usando:\n",
    "\n",
    "$x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a}$\n",
    "\n",
    "$x = \\frac{-b \\pm \\sqrt{D}}{2a}$ (abajo está $D$)\n",
    "\n",
    "$x = \\frac{(2AT+B) \\pm \\sqrt{D}}{2(T^{2}+B)}$\n",
    "\n",
    "con\n",
    "\n",
    "$D= b^2 - 4ac = (2AT+B)^{2}-4(T^{2}+B)A^{2}$\n",
    "\n",
    "$D=4A^{2}T^{2}+4ATB+B^{2}-4A^{2}T^{2}-4A^{2}B= +4ATB+B^{2} -4A^{2}B =    B(4AT+B-4A^{2})$\n",
    "\n",
    "En una palabra:\n",
    "\n",
    "$ D =  B(4AT-4A^{2} + B)$\n",
    "\n",
    "Recordando que\n",
    "\n",
    "$ A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $, \n",
    "\n",
    "el término $4 A T - 4 A^2 = 4A(T-A)$ se simplifica bastante:\n",
    "\n",
    "$4A(T-A)=4\\left(\\frac{T}{2}+\\lambda _{s}\\sqrt{T\\ln m}\\right)\\left(T-(\\frac{T}{2}+\\lambda _{s}\\sqrt{T\\ln m})\\right) $\n",
    "\n",
    ">>>> $=4(\\frac{T}{2}+\\lambda _{s}\\sqrt{T\\ln m})(T-\\frac{T}{2}-\\lambda _{s}\\sqrt{T\\ln m}) $\n",
    "\n",
    ">>>> $=4(\\frac{T}{2}+\\lambda _{s}\\sqrt{T\\ln m})(\\frac{T}{2}-\\lambda _{s}\\sqrt{T\\ln m}) $\n",
    "\n",
    ">>>> $ =4(\\frac{T^{2}}{4}-\\lambda _{s}^{2}T\\ln m)$\n",
    "\n",
    ">>>> $=T^{2}-4\\lambda _{s}^{2}T\\ln m$\n",
    "\n",
    "Así, \n",
    "\n",
    "$D = B (T^2 - 4 \\lambda_s^2 T \\ln m + B)$.\n",
    "\n",
    "9. Solución final\n",
    "\n",
    "La solución de $M_t = H x$ es, por consiguiente:\n",
    "\n",
    "$x = \\frac{M_t}{H}  = \\frac{(2TA+B) \\pm \\sqrt{D}}{2(T^{2}+B)}$\n",
    "\n",
    "$M_t = H \\frac{(2TA+B) \\pm \\sqrt{D}}{2(T^{2}+B)}$\n",
    "\n",
    "$M_{t}=H\\cdot \\frac{2TA+B\\pm \\sqrt{B(T^{2}-4\\lambda _{s}^{2}T\\ln m+B)}}{2(T^{2}+B)}$\n",
    "\n",
    "Recordemos que\n",
    "\n",
    "$ A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $\n",
    "\n",
    " y \n",
    " \n",
    " $B = L^2 K H = \\lambda_r^2 (H - T) \\ln m$\n",
    "\n",
    "obtenemos finalmente que\n",
    "\n",
    " $M_{t}=H\\cdot \\frac{2T(\\frac{T}{2}+\\lambda _{s}\\sqrt{T\\ln m})+\\lambda _{r}^{2}(H-T)\\ln m\\pm \\sqrt{\\lambda _{r}^2(H-T)\\ln m\\left[T^{2}-4\\lambda _{s}^{2}T\\ln m+\\lambda _{r}^{2}(H-T)\\ln m\\right]}}{2(T^{2}+\\lambda _{r}^{2}(H-T)\\ln m)}$\n",
    "\n",
    "$M_{t}=H\\cdot \\frac{2T(\\frac{T}{2}+\\lambda _{s}\\sqrt{T\\ln m})+\\lambda _{r}^{2}(H-T)\\ln m\\pm \\lambda _{r}\\sqrt{(H-T)\\ln m\\left[T^{2}-4\\lambda _{s}^{2}T\\ln m+\\lambda _{r}^{2}(H-T)\\ln m\\right]}}{2(T^{2}+\\lambda _{r}^{2}(H-T)\\ln m)}$\n",
    "\n",
    "Guiada por una analogía con la teoría de pruebas secuanciales, Gemini  aconseja tomar la raíz positiva por dar el mayor valor. \n",
    " \n",
    "\n",
    "**Ejercicio.** Reescribir la fórmula encontrada para la altura de la meseta en un formato apropiado para la programación. \n",
    ">\n",
    "> *Respuesta.* Una serie de fórmulas cortas es más manejable que una sola fórmula larga. La altura de la meseta está determinada según el modelo Underdog por:\n",
    ">\n",
    "> $ \\mu = $ tasa de mutación, constante\n",
    ">\n",
    "> $H = $ longitud de la cadena objetivo, número total de sitios\n",
    ">\n",
    "> $m = $ tamaño de la población, número de distribuciones binomiales, o de monos, o de cromosomas que proponen cada uno una posible solución.\n",
    ">\n",
    "> $M_t = $ número de sitios resueltos en la generación $t$. El $M_t$ que se encuentra aquí es la altura de la meseta.\n",
    ">\n",
    "> $T= $ número de sitios tocados por mutación $ = \\mu H +  \\lambda_T \\sqrt{H \\mu (1 - \\mu) } \\sqrt{ \\ln (m)} $\n",
    ">\n",
    "> donde el primer término es el valor esperado, y el segundo representa el efecto de población recogido por MAX.\n",
    ">\n",
    ">$x = \\frac{M_t}{H} = p_t $ =  Altura de la meseta normalizada\n",
    ">\n",
    "> $A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $\n",
    ">\n",
    "> $B = \\lambda_r^2 (H - T) \\ln m$\n",
    ">\n",
    "> $D = B (T^2 - 4 \\lambda_s^2 T \\ln m + B)$.\n",
    ">\n",
    "> $x = \\frac{M_t}{H}  = \\frac{(2TA+B) \\pm \\sqrt{D}}{2(T^{2}+B)} = p_t$\n",
    ">\n",
    "> $M_t = H \\frac{(2TA+B) \\pm \\sqrt{D}}{2(T^{2}+B)}$\n",
    "> \n",
    "> Ya sabemos por la condición de perfección que \n",
    "> \n",
    "> $ lambda_s = 0,9216$\n",
    ">\n",
    "> La recombinación se ignora porque no hemos detectado un papel significativo en la batalla de la Evolución contra la complejidad. De todos modos, la recombinación podría ser muy importante una vez que se incluyan los genes porque las funciones completas ahora pueden distribuirse fácilmente entre la descendencia, y también horizontalmente. \n",
    "\n",
    "**Ejercicio.** Pon a prueba la predicción del modelo Underdog sobre la altura de la meseta en función del tamaño y complejidad de la población.\n",
    ">\n",
    "> *Respuesta.* El resultado de un programa anterior para la altura de la meseta normalizada fue\n",
    "\n",
    "Population sizes and normalized realized plateau height.   \n",
    "population size 10: 0.6032.   \n",
    "population size 20: 0.6371.   \n",
    "population size 50: 0.6566.   \n",
    "population size 100: 0.6763.   \n",
    "population size 300: 0.6964.   \n",
    "population size 500: 0.7091.   \n",
    "population size 2000: 0.7348.   \n",
    "population size 3000: 0.7394.   \n",
    "population size 4000: 0.7468.   \n",
    "population size 5000: 0.7467.   \n",
    "\n",
    "Los parámetros fueron los usuales en esta contribución:\n",
    "\n",
    "length_of_target_sequence = 10000    \n",
    "include_clone_populationk5 = True    \n",
    "include_mutate_allk5 = True    \n",
    "include_crossover_allk5 = True    \n",
    "mutation_rate = 0.01    \n",
    "recombination_rate = 0.01    \n",
    "\n",
    "El programa que compara estos resultados observados con los predichos por nuestra fórmula es el siguiente, en el cual los valores de los parámetros se ajustaron a mano, es decir con Evolución Rudimentaria.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fd05f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program testingPredictionOfHeightOfPlateau.py\n",
    "# The Underdog Model predicts the height of the Plateau \n",
    "# for a given length of target sequence and population size.,\n",
    "# We test the prediction by comparing the predicted height\n",
    "# with the observed height for each length of target sequence.\n",
    "# The observed heights of the plateaus are obtained from the previous experiments.\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "population_sizesk5 =                  [10,      20,     50,       100,      300,     500,    2000,    3000,    4000,   5000 ]\n",
    "observed_normalized_plateau_heights = [0.6032, 0.6371, 0.6566,  0.6763,  0.6964,  0.7091,  0.7348,  0.7394,  0.7468, 0.7467 ]\n",
    "length_of_target_sequence = 10000\n",
    "include_clone_populationk5 = True    \n",
    "include_mutate_allk5 = True    \n",
    "include_crossover_allk5 = True    \n",
    "mutation_rate = 0.01    \n",
    "recombination_rate = 0.01 \n",
    "lambda_s = 0.805 #Original value = 0.9216, but we can also test other values, such as 0.5 or 1.5, to see how it affects the prediction.\n",
    "lambda_r = 0.009  \n",
    "\n",
    "def print_parametersk5():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: compare diverse plateaus for different degrees of complexity\")\n",
    "    print(f\"length_of_target_sequence = {length_of_target_sequence}\")\n",
    "    print(f\"include_clone_populationk5 = {include_clone_populationk5}\")\n",
    "    print(f\"include_mutate_allk5 = {include_mutate_allk5}\")\n",
    "    print(f\"include_crossover_allk5 = {include_crossover_allk5}\")\n",
    "    if include_mutate_allk5:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk5:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    print(f\"lambda_s = {lambda_s}\")\n",
    "    print(f\"lambda_r = {lambda_r}\")\n",
    "\n",
    "# A distance between vectors measures the discrepancy between x and y.\n",
    "# The smaller the distance, the better the model fits the data.\n",
    "def distance(x,y):\n",
    "    dist2 = 0\n",
    "    for i in range(len(x)):\n",
    "        dist2 += (x[i] - y[i])**2\n",
    "    dist = np.sqrt(dist2)\n",
    "    return dist\n",
    "\n",
    "def draw_realized_plateau_heightsk5(population_sizesk5, normalized_realized_plateau_heights):\n",
    "    # Plot the realized plateau heights\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(population_sizesk5, normalized_realized_plateau_heights, 'o-')\n",
    "    plt.xlabel('Population Size')\n",
    "    plt.ylabel('Normalized Realized Plateau Height')\n",
    "    plt.title('Observed Tendency of Normalized Plateau Heights')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# Prediction of the Underdog Model for the height of the plateau for each population size.\n",
    "def predicted_height_underdog_model(population_size, length_of_target_sequence, mutation_rate, lambda_s, lambda_r ):\n",
    "   \n",
    "        # $x = \\frac{M_t}{H} = p_t $ =  Height of the normalized plateau\n",
    "\n",
    "        # $A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $\n",
    "        \n",
    "        # $B = \\lambda_r^2 (H - T) \\ln m$\n",
    "\n",
    "        #3$D = B (T^2 - 4 \\lambda_s^2 T \\ln m + B)$.\n",
    "\n",
    "        # Predicted height = x = (2TA + B - sqrt(D)) / (2(T^2 + B))\n",
    "        # $x = \\frac{M_t}{H}  = \\frac{(2TA+B) \\pm \\sqrt{D}}{2(T^{2}+B)} = p_t$\n",
    "    \n",
    "    \n",
    "    H = length_of_target_sequence\n",
    "    T = mutation_rate * length_of_target_sequence\n",
    "    m = population_size\n",
    "    A = T/2 + lambda_s * ( (T * np.log(m)) ** 0.5 )\n",
    "    B = (lambda_r ** 2) * (H - T) * np.log(m)\n",
    "    D = B * (T**2 - 4 * (lambda_s ** 2) * T * np.log(m) + B)\n",
    "    predicted_normalized_height = (2 * T * A + B + D**0.5) / (2 * (T**2 + B))\n",
    "    return predicted_normalized_height\n",
    "\n",
    "def main( population_sizesk5, observed_normalized_plateau_heights):\n",
    "    print_parametersk5()\n",
    "    '''\n",
    "    print(\"\\nPopulation sizes and observed normalized plateau height\")\n",
    "    for i, (population_size, observed_normalized_plateau_height) in enumerate(zip(population_sizesk5, observed_normalized_plateau_heights)):\n",
    "        print(f\"population size {population_size}: {observed_normalized_plateau_height}\")           \n",
    "    '''\n",
    "    predicted_heights = []\n",
    "    for population_size in population_sizesk5:\n",
    "        predicted_height = predicted_height_underdog_model(population_size, length_of_target_sequence, mutation_rate, lambda_s, lambda_r )\n",
    "        predicted_heights.append(predicted_height)\n",
    "        # print(f\"Population size {population_size}: Predicted height of plateau = {predicted_height:.4f}\")   \n",
    "    # Calculate relative error between predicted heights and observed heights\n",
    "    relative_errors = []\n",
    "    for observed, predicted in zip(observed_normalized_plateau_heights, predicted_heights):\n",
    "        relative_error = abs(observed - predicted) / observed\n",
    "        relative_errors.append(relative_error)\n",
    "    '''\n",
    "    #print(\"\\nPopulation size and relative error between predicted and observed heights\")\n",
    "    for population_size, relative_error in zip(population_sizesk5, relative_errors):\n",
    "        print(f\"Population size {population_size}: Relative error = {relative_error:.4f}\")\n",
    "    '''\n",
    "    # Calculate the distance between predicted heights and observed heights\n",
    "    error_distance = distance(observed_normalized_plateau_heights, predicted_heights)\n",
    "    print(f\"\\nDistance between predicted heights and observed heights: {error_distance:.4f}\")\n",
    "    # Population size, observed, predicted, and error are printed together for comparison\n",
    "    print(\"\\nPopulation size, observed, predicted, and error:\")\n",
    "    for population_size, observed, predicted, relative_error in zip(population_sizesk5, observed_normalized_plateau_heights, predicted_heights, relative_errors):\n",
    "        print(f\"Population size {population_size}: Observed = {observed:.4f}, Predicted = {predicted:.4f}, Relative error = {relative_error:.4f}\")\n",
    "\n",
    "    # Plotting the predicted heights and observed heights together for comparison\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(population_sizesk5, observed_normalized_plateau_heights, 'o-', label='Observed Heights')\n",
    "    plt.plot(population_sizesk5, predicted_heights, 's-',  label=f'Predicted Heights (Underdog Model), lambda_s = {lambda_s}, lambda_r = {lambda_r}')\n",
    "    plt.xlabel('Population Size')\n",
    "    plt.ylabel('Height of Plateau')\n",
    "    plt.title('Comparison of Observed and Predicted Heights of Plateaus')\n",
    "    plt.legend()\n",
    "    plt.grid(True)   \n",
    " \n",
    "if __name__ == \"__main__\":\n",
    "    main(population_sizesk5, observed_normalized_plateau_heights)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21be8d47",
   "metadata": {},
   "source": [
    "*Nuestras observaciones.* \n",
    "1. El ajuste de la altura predicha de la meseta a la observada es muy bueno, con un error máximo en valor absoluto del 5 % obtenido para un tamaño poblacional de 10. Para todos los demás valores, el error es inferior al 2 %. Para valores altos de tamaño poblacional, el ajuste es muy preciso. \n",
    "2. La buena coincidencia entre lo observado y lo predicho induce  la sensación de estar en el camino correcto, es decir, de que el Modelo Underdog es la descripción correcta del trabajo de la Evolución que utiliza los recursos disponibles para luchar contra la complejidad.\n",
    "3. El valor de $ \\lambda_s = 0,805$ y $ \\lambda_r = 0,009$ son inferiores a 1. Ambos se refieren al peso de las fluctuaciones creadas por la variabilidad en los sitios resueltos, el primero en los sitios que han sido tocados y resueltos por mutación, y el segundo a aquellos que no han sido tocados pero que ya estaban resueltos.  Esto muestra que el canal más importante para la acción de MAX es a través de las fluctuaciones positivas en el número de sitios resueltos debido a la mutación. \n",
    "\n",
    "*Crítica.* Cuando un modelo tiene muchos parámetros libres, se pueden encontrar muchas asignaciones para ajustarse a un pequeño conjunto de datos. Esta acusación ataca nuestro modelo, a menos que pase pruebas rigurosas. \n",
    "\n",
    "*Respuesta.* Aceptamos la crítica. Para disminuir la libertad de los parámetros libres, podemos ampliar el conjunto de datos.\n",
    "\n",
    "**Ejercicio.** Amplía el rango de valores de las dos principales variables independientes, el tamaño de la población y la longitud de la cadena objetivo, y observa cómo se ajusta la altura de la meseta observada a la predicción del modelo Underdog. \n",
    ">\n",
    "> *Respuesta.* En el siguiente programa, se ejecuta un Proceso Evolutivo para cada par de valores, tamaño de la población y longitud de la cadena objetivo, que se toman de una cuadrícula generada por dos listas de valores. La salida es la altura de la meseta, que es el último valor de aptitud cuando se ha corrido un número razonable de generaciones. El resultado de este programa se procesa para otro más abajo para comparar lo observado con lo predicho. Allí, refinamos la asignación de valores obtenida a mano, es decir, por Evolución Rudimentaria. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ada88c32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lab: Simple Genetic Algorithm\n",
      "Purpose: compare diverse plateaus for different combinations of resources and complexity.\n",
      "lengths_of_target_sequences = [   50   500  1500  2500  5000 10000]\n",
      "population_sizesk6 = [  5  50 200 500]\n",
      "include_clone_populationk6 = True\n",
      "include_mutate_allk6 = True\n",
      "include_crossover_allk6 = True\n",
      "mutation_rate = 0.01\n",
      "recombination_rate = 0.01\n",
      "Population size 5, Target length 50: Normalized best fitness = 1.0\n",
      "Population size 5, Target length 500: Normalized best fitness = 0.848\n",
      "Population size 5, Target length 1500: Normalized best fitness = 0.7086666666666667\n",
      "Population size 5, Target length 2500: Normalized best fitness = 0.6624\n",
      "Population size 5, Target length 5000: Normalized best fitness = 0.6058\n",
      "Population size 5, Target length 10000: Normalized best fitness = 0.5816\n",
      "Population size 50, Target length 50: Normalized best fitness = 1.0\n",
      "Population size 50, Target length 500: Normalized best fitness = 1.0\n",
      "Population size 50, Target length 1500: Normalized best fitness = 0.8746666666666667\n",
      "Population size 50, Target length 2500: Normalized best fitness = 0.7896\n",
      "Population size 50, Target length 5000: Normalized best fitness = 0.7218\n",
      "Population size 50, Target length 10000: Normalized best fitness = 0.6554\n",
      "Population size 200, Target length 50: Normalized best fitness = 1.0\n",
      "Population size 200, Target length 500: Normalized best fitness = 1.0\n",
      "Population size 200, Target length 1500: Normalized best fitness = 0.9353333333333333\n",
      "Population size 200, Target length 2500: Normalized best fitness = 0.8636\n",
      "Population size 200, Target length 5000: Normalized best fitness = 0.7622\n",
      "Population size 200, Target length 10000: Normalized best fitness = 0.6881\n",
      "Population size 500, Target length 50: Normalized best fitness = 1.0\n",
      "Population size 500, Target length 500: Normalized best fitness = 1.0\n",
      "Population size 500, Target length 1500: Normalized best fitness = 0.9613333333333334\n",
      "Population size 500, Target length 2500: Normalized best fitness = 0.8848\n",
      "Population size 500, Target length 5000: Normalized best fitness = 0.7916\n",
      "Population size 500, Target length 10000: Normalized best fitness = 0.7149\n",
      "Normalized plateau heights: [[np.float64(1.0), np.float64(0.848), np.float64(0.7086666666666667), np.float64(0.6624), np.float64(0.6058), np.float64(0.5816)], [np.float64(1.0), np.float64(1.0), np.float64(0.8746666666666667), np.float64(0.7896), np.float64(0.7218), np.float64(0.6554)], [np.float64(1.0), np.float64(1.0), np.float64(0.9353333333333333), np.float64(0.8636), np.float64(0.7622), np.float64(0.6881)], [np.float64(1.0), np.float64(1.0), np.float64(0.9613333333333334), np.float64(0.8848), np.float64(0.7916), np.float64(0.7149)]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 0:34:10.893883\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program heightOfPlateausExtendedRange.py\n",
    "# An la Evoluciónary Process is run for each pair of values, \n",
    "# population size and length of target string, \n",
    "# that are taken from a grid generated by two lists of values. \n",
    "# The output is the height of the plateau, \n",
    "# which is the last value of fitness when \n",
    "# a reasonable number of generations has been run. \n",
    "# The output of this program \n",
    "# is processed for another one in the sequel \n",
    "# to compare observed with predicted. \n",
    "# By the AI of Visual Studio Code with the help of the Author.\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import time\n",
    "from datetime import timedelta\n",
    "\n",
    "\n",
    "# Resources and parameters for the experiments.\n",
    "population_sizesk6 = np.array([5, 50, 200, 500])\n",
    "# Complexity is given by the length of the target sequence, \n",
    "# which is the string of 1s that the algorithm has to match.\n",
    "lengths_of_target_sequences = np.array([50, 500, 1500, 2500, 5000, 10000])\n",
    "numbersOfGenerationsk6 = np.array([1000, 1000, 1000, 1000, 1000, 1000]) \n",
    "\n",
    "'''\n",
    "# Parameters for a smaller range of population sizes and lengths of target sequences, to test the program before running the extended range.\n",
    "population_sizesk6 = np.array([10, 20, 30])\n",
    "# Complexity is given by the length of the target sequence, \n",
    "# which is the string of 1s that the algorithm has to match.\n",
    "lengths_of_target_sequences = np.array([10,  100, 500])\n",
    "numbersOfGenerationsk6 = np.array([100, 100, 100]) ''' \n",
    "\n",
    "mutation_rate = 0.01 \n",
    "recombination_rate = 0.01\n",
    "alphabet = '01'\n",
    "include_clone_populationk6 = True \n",
    "include_mutate_allk6 = True \n",
    "include_crossover_allk6 = True\n",
    "print_best_fitness = False\n",
    "show_graph = False \n",
    "\n",
    "def print_parametersk6():\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: compare diverse plateaus for different combinations of resources and complexity.\")\n",
    "    print(f\"lengths_of_target_sequences = {lengths_of_target_sequences}\")\n",
    "    print(f\"population_sizesk6 = {population_sizesk6}\")\n",
    "    print(f\"include_clone_populationk6 = {include_clone_populationk6}\")\n",
    "    print(f\"include_mutate_allk6 = {include_mutate_allk6}\")\n",
    "    print(f\"include_crossover_allk6 = {include_crossover_allk6}\")\n",
    "    if include_mutate_allk6:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk6:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "    \n",
    "\n",
    "def create_populationk6(population_size, target_sequence, alphabet): \n",
    "    \"\"\"Creates an initial population of random chromosomes.\"\"\"\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        chromosome = ''.join(random.choice(alphabet) for _ in range(len(target_sequence)))\n",
    "        population.append(chromosome)\n",
    "    return population\n",
    "\n",
    "def create_perfect_populationk6(population_size, target_sequence, alphabet): \n",
    "    \"\"\"Creates an initial population of perfect chromosomes.\"\"\"\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        chromosome = target_sequence\n",
    "        population.append(chromosome)\n",
    "    return population\n",
    "\n",
    "def calculate_fitnessk6(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome.\"\"\"\n",
    "    fitness = sum(1 for c, t in zip(chromosome, target_sequence) if c == t)\n",
    "    return fitness \n",
    "\n",
    "def calculate_all_fitnessk6(population, target_sequence): \n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = []\n",
    "    for chromosome in population:\n",
    "        fitness = calculate_fitnessk6(chromosome, target_sequence)\n",
    "        fitness_scores.append(fitness)\n",
    "    return fitness_scores\n",
    "\n",
    "def select_the_bestk6(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome from the population.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk6(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome = population[fitness_scores.index(best_fitness)] \n",
    "    return best_chromosome, best_fitness\n",
    "\n",
    "def select_the_bestk6(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome from the population.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk6(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome = population[fitness_scores.index(best_fitness)]\n",
    "    return best_fitness, best_chromosome \n",
    "\n",
    "def clone_populationk6(chromosome, population_size):\n",
    "    \"\"\"Clones a chromosome to create a new population.\"\"\"\n",
    "    new_population = [chromosome] * population_size\n",
    "    return new_population \n",
    "\n",
    "def mutatek6(chromosome, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates a chromosome with a given mutation rate.\"\"\"\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "                mutated_chromosome_list[i] = random.choice(alphabet)\n",
    "                counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "\n",
    "def mutate_allk6(population, mutation_rate, alphabet):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek6(chromosome, mutation_rate, alphabet)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "    return mutated_population\n",
    "\n",
    "def crossoverk6(parent1, parent2, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, len(target_sequence))\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    " \n",
    "def crossover_allk6(population, target_sequence, recombination_rate):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 = population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk6(parent1, parent2, target_sequence, recombination_rate)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "def run_one_generationk6(population_size, population, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    best_fitness, best_chromosome = select_the_bestk6(population, target_sequence)\n",
    "    new_population = clone_populationk6(best_chromosome, population_size)\n",
    "    if include_mutate_allk6:\n",
    "        new_population = mutate_allk6(new_population, mutation_rate, alphabet)\n",
    "    if include_crossover_allk6:\n",
    "        new_population = crossover_allk6(new_population, target_sequence, recombination_rate)\n",
    "    best_fitness, best_chromosome = select_the_bestk6(new_population, target_sequence)\n",
    "    return best_fitness, best_chromosome, new_population \n",
    "\n",
    "def plot_Best_Fitnessk6(best_fitness_history, population_size, length_of_target_sequence):\n",
    "    \"\"\"Plots the best fitness history over generations.\"\"\"\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(best_fitness_history, label=f'Population size {population_size}, Target length {length_of_target_sequence}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title('Best Fitness History over Generations')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "def oneEvolucionaryProcessk6(population_size, length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one la Evoluciónary process and returns the normalized best fitness history.\"\"\"\n",
    "    population = create_populationk6(population_size, target_sequence, alphabet)\n",
    "    generation = 0\n",
    "    best_fitness, best_chromosome = select_the_bestk6(population, target_sequence)\n",
    "    best_fitness_history = [best_fitness]\n",
    "    while generation < numberOfGenerations and best_fitness < length_of_target_sequence:\n",
    "        best_fitness, best_chromosome, population = run_one_generationk6(population_size, population, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "        generation += 1\n",
    "        if print_best_fitness: \n",
    "            print(f\"Generation {generation}: Best Fitness = {best_fitness}\")\n",
    "        best_fitness_history.append(best_fitness) \n",
    "    normalized_best_fitness_history = [fitness / length_of_target_sequence for fitness in best_fitness_history]\n",
    "    if show_graph:\n",
    "        plot_Best_Fitnessk6(best_fitness_history, population_size, length_of_target_sequence)\n",
    "    return normalized_best_fitness_history \n",
    "\n",
    "def draw_all_historiesk6(normalized_best_fitness_histories, population_sizesk6, lengths_of_target_sequences):\n",
    "    \"\"\"Draws all the normalized best fitness histories together.\"\"\"\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for i in range(len(population_sizesk6)):\n",
    "        for j in range(len(lengths_of_target_sequences)):\n",
    "            index = i * len(lengths_of_target_sequences) + j\n",
    "            plt.plot(normalized_best_fitness_histories[index], label=f'Population size {population_sizesk6[i]}, Target length {lengths_of_target_sequences[j]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Normalized Best Fitness')\n",
    "    plt.title('Normalized Best Fitness Histories over Generations')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def main(population_sizesk6, lengths_of_target_sequences, numbersOfGenerationsk6, mutation_rate, recombination_rate, alphabet):\n",
    "    ''' An la Evoluciónary process is triggered for each population size.\n",
    "    The normalized best fitness history is stored in a list, \n",
    "    and then all the histories are plotted together.\n",
    "    A graphic is also plotted to show the tendency of the heights of the plateaus,\n",
    "    by plotting the last entry of the normalized best fitness history \n",
    "    for each length of target sequence.\n",
    "    Two fitting models are tested: a logarithmic model and a power model.\n",
    "    With the assitance of the AI of Visual Studio Code.\n",
    "    '''\n",
    "    print_parametersk6()\n",
    "    # List to store the normalized best fitness history for each length of target sequence \n",
    "    normalized_best_fitness_histories = []\n",
    "    normalized_plateau_heights = []\n",
    "    # An la Evoluciónary process is triggered for each population size \n",
    "    # and each length of target sequence.\n",
    "    for i in range(len(population_sizesk6)):\n",
    "        normalized_plateau_heights_popSize = []\n",
    "        for j in range(len(lengths_of_target_sequences)):\n",
    "            population_size = population_sizesk6[i]\n",
    "            numberOfGenerations = numbersOfGenerationsk6[i]\n",
    "            length_of_target_sequence = lengths_of_target_sequences[j]\n",
    "            target_sequence = '1' * length_of_target_sequence\n",
    "            normalized_best_fitness_history = oneEvolucionaryProcessk6(population_size, length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet)\n",
    "            normalized_best_fitness_histories.append(normalized_best_fitness_history)\n",
    "            # To see the tendency of the heights of the plateaus, \n",
    "            # we can print and draw the last entry of \n",
    "            # the normalized best fitness history for each length of target sequence.\n",
    "            print(f\"Population size {population_size}, Target length {length_of_target_sequence}: Normalized best fitness = {normalized_best_fitness_history[-1]}\")\n",
    "            \n",
    "            normalized_plateau_heights_popSize.append(normalized_best_fitness_history[-1])\n",
    "        normalized_plateau_heights.append(normalized_plateau_heights_popSize)\n",
    "    print(f\"Normalized plateau heights: { normalized_plateau_heights}\")\n",
    "    draw_all_historiesk6(normalized_best_fitness_histories, population_sizesk6, lengths_of_target_sequences)\n",
    "    \n",
    "    # Draw the normalized plateau heights as function of population size for each length of target sequence.\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for j in range(len(lengths_of_target_sequences)):\n",
    "        normalized_plateau_heights_for_length = [normalized_plateau_heights[i][j] for i in range(len(population_sizesk6))]\n",
    "        plt.plot(population_sizesk6, normalized_plateau_heights_for_length, 'o-', label=f'Target length {lengths_of_target_sequences[j]}')\n",
    "    plt.xlabel('Population Size')\n",
    "    plt.ylabel('Normalized Realized Plateau Height')\n",
    "    plt.title('Tendency of Normalized Plateau Heights for different lengths of target sequences')\n",
    "    plt.legend()\n",
    "\n",
    "    end_time = time.time()\n",
    "    # Calculate the difference in seconds\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_time = timedelta(seconds=elapsed_time)\n",
    "    print(f\"Elapsed time: {elapsed_time}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    start_time = time.time()\n",
    "    main(population_sizesk6, lengths_of_target_sequences, numbersOfGenerationsk6, mutation_rate, recombination_rate, alphabet)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63ce6929",
   "metadata": {},
   "source": [
    "*Observación.* La tendencia de las alturas de la meseta normalizadas para diferentes longitudes de secuencias objetivo es repetitiva. Esta tendencia denota la batalla de la Evolución contra la complejidad: hay un equilibrio entre los recursos y la complejidad. Para una complejidad dada, si los recursos aumentan, la altura de la meseta también aumenta, pero muy lentamente. Si la complejidad aumenta, todo el comportamiento se desplaza hacia abajo: la complejidad aplasta los recursos disponibles contra el suelo dado por la mediocridad y no tan lentamente.\n",
    "\n",
    "**Desafío.** Convierte la observación anterior en una ecuación que describa aproximadamente el efecto de la complejidad sobre los recursos disponibles para la Evolución.  ¿Deducirías esa ecuación del modelo  Underdog? \n",
    "\n",
    "\n",
    "**Comparación de lo observado con lo predicho.** El código que compara estos resultados observados con los previstos por el Modelo del Perdedor, dado por la fórmula recién hallada, es el siguiente, en el cual los valores de los parámetros fueron ajustados a mano, es decir, por Evolución Rudimentaria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a67b3195",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lab: Simple Genetic Algorithm\n",
      "Purpose: compare diverse plateaus for different combinations of resources and complexity\n",
      "length_of_target_sequences = [   50   500  1500  2500  5000 10000]\n",
      "population_sizesk7 = [  5  50 200 500]\n",
      "observed_normalized_plateau_heights = [[1.         0.848      0.70866667 0.6624     0.6058     0.5816    ]\n",
      " [1.         1.         0.87466667 0.7896     0.7218     0.6554    ]\n",
      " [1.         1.         0.93533333 0.8636     0.76622    0.6881    ]\n",
      " [1.         1.         0.96133333 0.8848     0.7916     0.7149    ]]\n",
      "mutation_rate = 0.01\n",
      "lambda_s = 0.7\n",
      "lambda_r = 0.015\n",
      "D: -0.023469783651470903 Warning: Discriminant D is negative for population size 5 and target length 50. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 1.6718627103780257 Warning: Predicted normalized height is greater than 1 for population size 5 and target length 50. Adjusting to 1.0.\n",
      "Population size 5, Target length 50, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "Population size 5, Target length 500, Observed height 0.848: Predicted height of plateau = 0.9201, Relative error = 0.0850\n",
      "Population size 5, Target length 1500, Observed height 0.7086666666666667: Predicted height of plateau = 0.7504, Relative error = 0.0590\n",
      "Population size 5, Target length 2500, Observed height 0.6624: Predicted height of plateau = 0.6950, Relative error = 0.0493\n",
      "Population size 5, Target length 5000, Observed height 0.6058: Predicted height of plateau = 0.6385, Relative error = 0.0539\n",
      "Population size 5, Target length 10000, Observed height 0.5816: Predicted height of plateau = 0.5981, Relative error = 0.0283\n",
      "D: -0.15424760685538422 Warning: Discriminant D is negative for population size 50 and target length 50. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 2.1674078531747467 Warning: Predicted normalized height is greater than 1 for population size 50 and target length 50. Adjusting to 1.0.\n",
      "Population size 50, Target length 50, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "D: -5.621475535373328 Warning: Discriminant D is negative for population size 50 and target length 500. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 1.1085692800037965 Warning: Predicted normalized height is greater than 1 for population size 50 and target length 500. Adjusting to 1.0.\n",
      "Population size 50, Target length 500, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "Population size 50, Target length 1500, Observed height 0.8746666666666667: Predicted height of plateau = 0.8821, Relative error = 0.0085\n",
      "Population size 50, Target length 2500, Observed height 0.7896: Predicted height of plateau = 0.8005, Relative error = 0.0138\n",
      "Population size 50, Target length 5000, Observed height 0.7218: Predicted height of plateau = 0.7147, Relative error = 0.0099\n",
      "Population size 50, Target length 10000, Observed height 0.6554: Predicted height of plateau = 0.6525, Relative error = 0.0044\n",
      "D: -0.2881660001949367 Warning: Discriminant D is negative for population size 200 and target length 50. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 2.343526917979293 Warning: Predicted normalized height is greater than 1 for population size 200 and target length 50. Adjusting to 1.0.\n",
      "Population size 200, Target length 50, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "D: -15.539347843759705 Warning: Discriminant D is negative for population size 200 and target length 500. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 1.2039633450033875 Warning: Predicted normalized height is greater than 1 for population size 200 and target length 500. Adjusting to 1.0.\n",
      "Population size 200, Target length 500, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "Population size 200, Target length 1500, Observed height 0.9353333333333333: Predicted height of plateau = 0.9375, Relative error = 0.0023\n",
      "Population size 200, Target length 2500, Observed height 0.8636: Predicted height of plateau = 0.8470, Relative error = 0.0192\n",
      "Population size 200, Target length 5000, Observed height 0.76622: Predicted height of plateau = 0.7489, Relative error = 0.0226\n",
      "Population size 200, Target length 10000, Observed height 0.6881: Predicted height of plateau = 0.6772, Relative error = 0.0159\n",
      "D: -0.3994478785576592 Warning: Discriminant D is negative for population size 500 and target length 50. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 2.432753018016666 Warning: Predicted normalized height is greater than 1 for population size 500 and target length 50. Adjusting to 1.0.\n",
      "Population size 500, Target length 50, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "D: -24.371368374126064 Warning: Discriminant D is negative for population size 500 and target length 500. Adjusting D to zero to avoid complex numbers.\n",
      "Predicted normalized height: 1.2593804942849196 Warning: Predicted normalized height is greater than 1 for population size 500 and target length 500. Adjusting to 1.0.\n",
      "Population size 500, Target length 500, Observed height 1.0: Predicted height of plateau = 1.0000, Relative error = 0.0000\n",
      "Population size 500, Target length 1500, Observed height 0.9613333333333334: Predicted height of plateau = 0.9676, Relative error = 0.0065\n",
      "Population size 500, Target length 2500, Observed height 0.8848: Predicted height of plateau = 0.8737, Relative error = 0.0125\n",
      "Population size 500, Target length 5000, Observed height 0.7916: Predicted height of plateau = 0.7690, Relative error = 0.0286\n",
      "Population size 500, Target length 10000, Observed height 0.7149: Predicted height of plateau = 0.6917, Relative error = 0.0325\n",
      "\n",
      "Total relative error between predicted heights and observed heights: 0.4521\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pn+8qpK2vNf158803i68+2pXdCRMmuCt55fV7sCu+VkNjV738rxhZLYU1pfFvvlJZdmXWrnDZFTtL+hCIbWPbTnbFzZ81E7R1sit0/uzKoX12gdiVPP8rtLbtbRk23cfey66m+W9r439F05qRWu2GfSZ7m7HImkFYR177PO2Kspk4caK7um61If77iz1vV/98+4tdtbUaCNtX/BNmWA2rffbBsjbvtt/azZqR2nfFrpZbLUFZbDv53tPKak337CqwbbtgtonNb7WMto529da3jtZMya50Ll68uLiJmTVB8e2Dtv/aPPadte9jVWSMsiZY9p0tfSt9VT3YzykQa65qV6Pt++Rj62jNbstS0TGhLLbv2mdlzSJLN4GuDN9+Z/uuf39Cq8G1WqBAxwb/GvdQVMWxszLbvixnnHGGa17l/16mos/Fjmv2u+Frcmdsn7baCWs2ZjWy4eR//LIaC9uG9ttlAn2HSm/DyZMnu++3feb+rCa0NDt+2Haw7eL/mVkLCPv+fv/995VaB6sNtZopOzbs7TEl2N/uUIS63hXtO1YLbq+x5n5WC+bP9ztmv1/222G1iXbf/31tne13yvf52vazZnzBNlstr4bOajZtn3rwwQdLPGfb/uWXX3a1rHaOYYlwrKWJfQa+mjbj+723Y3tpdkzxPx+w+2XN578shI5mgdhrdmC1A41/06tAHbd9Sh/MjB0I/U9OrDmX/cCXZid9pd/bWDO4QCxY8efrP1Xee1vTAKtCt+Zf5bGDnAVT1lzQghkLmqwZgZ08l8fWLdC62Ematdv3Pb+3rKmHBUTPPfecSzkcqBy2ntbPIlDWx9LlKJ2hqLzP1BeAlG4+Z9NLn4Ra8z9rk29NDP3beJfVByIY1rTNfojsv+9Ex7e/2A9lWVmQfE1GfOteej573j6jYNk+bOtm62JNPGzbBtOB3poxWXMP6xPg32SuvM/Av9mtraM1qbNbWd9Ja97j61to/f+sKa9/O/7ymi5Wlp1wBeqM7TtpCvVzCsQ+O+v3VLpJTVnZNoM5JpTFTkwsULYLEtZ3wfY161NqxwZfQB+Kso4NxoIr69voz/pUWFOfyqiKY2eo2748pY8pvpPlij6Xsn47/I9r4Uz7b4GHNYe25sylf+vsd7G00t9h32deehvZ749/gOD7zKy5WenPwaf0+wfLmqbaxUJLZGDbxvoX2++Y76JHKMeUYH+7QxHqele07/iCrPL2AwvA7OKDNY8rawgU3/veeOON7vffjm/2OR555JGuOaA1owyWHXutT6kF/3ZhM5gm2XYBwba1f+p2X7AfqL+UBf/+FwPsflnz+S8LoSO4wl6zEzQLrCzZQSClD4h2ZTSQ0okOgn1vY+3nA53M+HfoLO+9K8OuXtkJlXW6t+DK/lsZaktaZSuTdZy32qtwZKYr70Bb1nYNNN3/c7bA1PpCWFntBN9OzOzE2QKjymYqsv4e1u7d+g/Y1erS+4sFOvbjFahs4R5Q1vqmhbo/2H5kNWR2ddL6v9l3y8pqfSIs8A/2O2HJBspKrOI7kbN+H3ayZFdw7UqondBZTYPVqvp3FC8r0C2rU/Xeqs7PaW+PCbat7Oq29V2zK8m2Pe2zsiv9++23n6qSf81jXTp2BiOcvxNVyWoUrP+PfVetP6Ttm7ZtLUAJlIBpb05YbXnWQsQSAwViwVFl2PHXji3WP85q263Pp/W5tQtzdhwN5ZgSDoESTIWy3uHYd3zrbMlkyuqv6Qs+LXC3ftN2odBqb63Gy37PLDGRLyV+Ray2115v51FlXfAIxC5g2nv72G+osb5opS9u2jT/GkWb16aV5psW7j63kYTgCnvNOjTblRO7ShOuKx0dO3YM2ETB/yDie29jJ6DhCmpsmXaSZFcky6u9sgO4XZ2yDuh29dpOruwAWdFJiK2bb138a0GsqaDVHoQzOLPaKwuwrAYnUDnsc7NmHv61V1Zb4l/OqmQ/QnZF3La3f/MEC64qw642WqdiO8nxZSMr/dnaD6xdPS7vRMS37rYP+v/QWS2SfUZlZYoMB+t0b/uFNY3zD2pKN1MtK+Dx7VMWpFa0L9l7WfNRS5jhz67Y+mex8l359c9macJVy1rZz6msz86atFkTG/8aFLv6XlkV1aJaea32ym62z9j+ZzWPFiiHWnbfsaH0CZZNC+d3siqOnaFs+72pma6oDKV/J/b2uFZWWa0mxJr12Qm0nUj7lNW8rqzy+raRf62WNfcqXUtnn5klM6jo86rMtrXfOkvKYTd7Dwu47PfDgqtQjinB/nb7jiuljyn2O1j6hD/Y9Q6Wb30sIVVZ7KKw/S5aoBfM+1pXAGuOaDdbB0ugYc31LOlGRUOGWGBuv3mWmMOa6YbCauH8L2DbscfXxNg/kLIEMtZ00f+Co81rFzgtkPS/SGOJOOz7W9lgHaRiR5iu3NkByK58l2ZtyUsfPINhqUgt05LVQvifOJeuHbOraL7xGwJlHLPXhMqyf9mJXaArTqWvfFnTCfsBtKxEdvAPZuwvO1BbE8Ann3yyxPLsBNeakYSSIa8i1izQgisL/kpnnrJtbJ+bZXX0Z1cs7cfZMiVWNQtE7b38r1RavwgLVEPla1ZhP2wWtAUaXNh+8Ow97bMt/VnaY1/6ZGu6Zj9YduXWludjgXRl9udQ+IJz//LZj93PP/9cYj7fyWvp8tjJsi+gDnRV0v87Ye9VejtY/4bSab99J+L+fRtse5fVXGZvBfs5BWLHBDsWWAYvHzt5CBRsB6usbW1BROnvlW0rOymrTBpj2+/s87P9zv/1VoM3f/78sB4bquLYGcq29/VLDff3yY5r9rvh/32xISdsX7WMcGX1G63M5x/ou2pKZ68rz+GHH+5qCUsPnVH6uOz7rbX1sotRpVnZ7Pe2vPKWpfT3yWrfrCbKtw+GckwJ9rfb910p3V/KPqfSNVfBrnew7NhuwaNlKrasnv58n6V9tnYuYL8lgYIw/3Uuvf3st8f2M1tWoO+WP8uga9k3b7nlFtfioiyBvo/Wv80G+7VaUh/LuGxNiEtvR9u/7LfWN56VsfvWlcEu5PlY82z7DbDa+ED9sRAcaq4QlhN4Cy6sKYz1m7H2xnaFy65e2ZfU+nT4f6GDYdX/1lzFDhp2wPGlc7WrYtb22sdODuygYUGOpRS1k2s7cNoB0zp/W21aoB+p8tiVfFueBT+2Dr7mHXaFx56zlKw+1uzH2m3belrTACtDRax8djXLThxt2dYszq7qWTMCS78c7sGZrcbDyl2aHTxtuqWFtoDGamOsSYg1DbGmTr4T6qpkJ4uPP/642w5WC2ht2O1EzH7Y/T/nYNgJqTXFsiaQpTvjW/NNa1Zi62R9oGz72zpb0zs7EbbaKEt5b1f1rOmL7b82n+3XVoNgVyNtHru6GEqfq8qwPjv2Y2epjW372PvautmPtQXwPlZLbNOs359dYbQrz7Yv2s22obXHtzS6VptqZbYfUTtBsauXvnGs7L2sv4Vdrbb01ZYAxE6CSq+j/WBbfyLbbr4aXetjEupJTbCC/ZwCsXntiq3VIlltgJ1o2Fg8Vu7KXtUva1vb+tvJsZ382fN2kmzls21tx6JQ2X5nF0Ls87Djql3F9qVit8AgUN/JyqqKY2co294SHhlLqGNBmZ3MVmablWbpvy3RkF0csmXbZ2V9GG3fsRPlyjSjLO+7Zifp1vTaTqKtz5EdQ8sbx640OzbZb5zVdNpvgR0L7ftpAbXVHvtvM6vhsO1p31vf8CUWONr31mqh7btirymvvIHYvBY82fJsXqv1sOX5/9YFe0wJ9rfbWK2YHa8tiLHjsy3DAqjSYz8Fu96hsN93Wx/b9+14YrWGthzb931DjFhSCfstsX5Nts62nWxftkQW1urDt1/bOY81rbXvjH2ediHEvjt2/C7dp9mfHStse1nfUjt/KF3TbdvElmfs+GznG3YBxvouWxksOLSmfxaYlQ7YbF+yctl3yoJDK49tb1/fQ2PnZXZct+ON9fWybWjnIRaUBducEWWoZJZBRLCyUuhammpLy5uUlORSnFra1htuuMGzdu3aMtOBl5WO2syePdtNs7Skbdu2delYX3zxxTJTQls6cEuPavN37drVpS399ddfi+ex1K6WerQ0X4pcf5YO21Ka9urVy6VNb968ueeoo47yzJgxY4/XP/zww+71999/vycUlnrdlh8XF+fSbF9++eUutXOgsoWair002472XOltbymKr7nmGpeu3MphKXVtvf3T0ZaXsjhQOunyyhLoM7DP1N7X0n/b9rBlBvpMKkrF7ntNoFvpfctSRo8YMcKVxW72vrZ+CxcuLDHfv/71L5fC2co2ePBgl1o50L4aSFn7emmll2fb3vYle729r6W7tlTcpVOOm59++sl952wfLZ3WeOnSpZ5zzz3X06pVK/fZ2nfo2GOP9bzzzjvF81haZRsCwdIN2/d2+PDhnp9//jngOtryRo0a5cpk++stt9zi+fLLL0NKxV56P/HfBoFSGQfzOQXaLrbfnXXWWe44ZMcEOxb8+OOPrgxvvfVWpY4Jgba1pWi28li5bDn2XgcccIDnf//7X7nbo6JtYmmX7XO3bd20aVPP2Wef7Vm9enWJecoqe0XvV9XHzmC3vR1jbUgCO7basA2+5fjSaQdKbx9senHbV21ICUs1b+s0ZMgQ9x0KtLxgUrGX912zz+Wkk05y72Xre9ppp7nfvNJlLe/4bNvi9ttvd99V+x5a6vr58+e7lOL+w1n4jtk333yzp1u3bq4slorb0qE/+uijLpV/ReUNxIbjsG1k62Dvb/uzDXfgv7xgjymh/HZbWnwbzsTWwdKK235oQ4CUPtYHu96h7jtz5swp/uysrD179nSfg78NGza4faR9+/ZunW3dLS29ne/4PP/88y61u31e9p2179DYsWM96enpnvKU95tV+rh66623uqFBbB+zcnTo0MGdM1gq/0AsXbvNb+WxIVAsdXvpz9NYynpLOW9lt8/APreyjtMIXpT9KSvwAlAxu6psV5TtqlegTIgAap41NbXaQMu4F0oWL7Dta4I1d7M+SVaDa60L6jrf4NhWq1fW4NpAfcE4V8BesGsT1lfKmvAQWAG1Q+nxWayZy1NPPeWawgXTdBds++oUaDwhX78ta64HoG6hzxVQCdbe29qAW3tsa/dt/ZQA1A42AKudsA4dOtR1yrc+bJYu25I3MHYL2762sb5RVrNjySAsmYTVrlq/MeszQy0rUPcQXAGVYJl7LAGDDQhrnUmt8yiA2sGSkFiCABs3xrL5WYIUq7ny76APtn1tYeMlWTIUS4yRkZFRnOTCmgQCqHvocwUAAAAAYUCfKwAAAAAIA4IrAAAAAAgD+lwFYAPGrl271g3+VpkBJwEAAADUn+zQmZmZatOmTYWDkRNcBWCBlY16DQAAAABm1apVateuncpDcBWA1Vj5NqCNiwIAAAAgMmVkZLiKF1+MUB6CqwB8TQEtsCK4AgAAABAVRHchEloAAAAAQBgQXAEAAABAGBBcAQAAAEAY0OdqL1IyFhQUqLCwMByfAxA2MTExio2NZRgBAACAakZwVQl5eXlat26dsrOzw/+JAGGQnJys1q1bKz4+nu0JAABQTQiuKjHA8PLly13tgA0kZievDDSM2lSjasH/pk2b3H7avXv3Cge7AwAAQHgQXIXITlwtwLJc91Y7ANQ2SUlJiouL04oVK9z+mpiYWNNFAgAAiAhc0q7shqM2ALUY+ycAAED1I7gCAAAAgDAguAIAAACAMCC4qiGFRR79vHSLPpi5xv23x7VFp06dNH78eNUXFa3P+eefrxNPPHGv3uOVV15R48aNQ3pNON4XAAAAtQfBVQ34bM46jXjoa535f1N11Vsz3X97bNOr0qpVq3ThhRcWZzns2LGjrrrqKm3ZsqVK3zcSnHHGGVq0aFHYl1vfAl0AAID6jOCqmlkAdfnrv2ldek6J6evTc9z0qgqwli1bpsGDB2vx4sV68803tWTJEj333HOaPHmyhg4dqq1bt6qm2EDMloGxrmfoa9GiRU0XAwAAADWI4CpMYwtl5xVUeMvMyde4D+cqUANA37Q7P5zn5gtmefa+wfr73//uaqu++OILjRw5Uh06dNBRRx2lr776SmvWrNGtt95aYv7MzEydeeaZSklJUdu2bfXMM8+UWN8777zTLSMhIcHVhP3jH/8ofj43N1fXX3+9e529/oADDtC33367RxO6Dz/8UH369HHLeOGFF1zK8O3bt5coh9WsHXbYYcWPp0yZooMOOsgFM5YO3953x44dxc9v3LhRxx13nHu+c+fOeuONN4LeRo8++qgbeLdZs2Zue+Xn54e8Tv7uvfdeF3A1bNhQF198sW666Sbtu+++Qb/vIYcc4tKpX3PNNW4sNcZTAwAAkaCwFnefqQjjXIXBzvxC9bnj871eju026zNy1O/OL4Kaf97do5UcX/FHaLVSn3/+ue677z4XdPhr1aqVzj77bE2YMEH/+te/ik/gH3nkEd1yyy2666673GstyOnRo4eOOOIIvfvuu/rnP/+pt956S3379tX69es1a9as4mVeccUVmjdvnnveAq/33ntPY8aM0R9//OEGtTXZ2dl66KGHXFBlQUW7du10xx13uGVfdNFFxTVaVi4rt1m6dKlbjgUtL730khso197Lbi+//HJxP6a1a9fqm2++cWM9WfBlAVdFbH4LcOy/1epZMz8LhC655JKg18mfBXVWbtumw4cPd6977LHHXMAX7PtOnDhRAwYM0KWXXlpcDgAAgPrssznrdNdH80q08mqdmqhxx/XRmH1aq7YjuIoA1hTQapt69+4d8Hmbvm3bNhes+Jq2WUBgNS3Ggqoff/zRBVQWXK1cudIFZaNGjXIBjNVgDRkyxM1rz1mgY/8tCDFW4/PZZ5+56ffff7+bZrUzFnhY8ODzl7/8Rf/973+Lgytrsmg1Waeccop7/MADD7hA8Oqrr3aPLah58sknXU3cs88+697z008/1fTp07X//vu7eV588cUy19tfkyZN9PTTTysmJka9evXSMccc497fgppg18nfU0895dbjggsucI8tcLRaw6ysrKDft2nTpm661XzZ9gYAAIiE7jOeUtN93WeePWdgrQ+wCK7CICkuxtUiVWT68q06/+VfKpzvlQv215DOTYN631CE0ozQ+mGVfuxLrHDaaae5+126dHG1N0cffbRrihcbG+tqcqzGyQIyf9aszmqofKyJYv/+/UvMY4HTgQce6GqeLIix2h8LNnzN7ax2bPbs2SWa+tk6WX+t5cuXu4QSVoZBgwYVP28BSzBZ/KwGzgIZH6tNsnUxwa6Tv4ULF+pvf/tbiWkWgH799ddBvy8AAECkKCzyuO4xZXWfsbZVVqN1RJ9Wion2trSqjQiuwsCa0gXTPO+g7s1dtaZF34F2HNtNWqUmuvnCudN069bNlXH+/Pk66aST9njeplsNSvPmzYNanvV1suDB+mt9+eWXLoiwZoTfffedq5mxYGHGjBklggbToEGD4vvWPLF0HyKrberatatrQnf55Ze7pnfWl8nHlv3//t//K9G/y8dqz/YmW5/VwPmzsvmSbAS7TuF+XwAAgLquqMijbdl52pSVq02ZpW5+09am79SO3MIyl2PnztZU0CorhnYNfHG7NiC4qkYWMFl7UavWtLDCP8DyhRn2fLijcatdseZ81gzPkiP497uy/lJWE3TuueeWCHamTp1aYhn22L95nS3DaqvsZkkYrIbIalz2228/V8tj/Zws8USorPbKymN9sKKjo13Nlc/AgQNdvycLFgOxMhQUFLggyNcs0ILA0kkyQlWZderZs6d++eUXt1197HGorIbP3hsAAKC2sJZDO/IKSwVLOSUDqF33N2flhTUhxcbMkhm3axuCq2pm7UStvWjpjnqtqrijnvXrGTZsmEaPHu0SQlhihblz52rs2LEuA54vaYSP9bF6+OGH3SC3Vjv19ttva9KkSe45q02yE37LmJecnKzXX3/dBVs2bpYFchYgWVBhCRwsMLG+XNaPyJoB+gdLgdhrLROhlefUU091mQR9brzxRtds0JJLWPY9y9pnwZaVz9bPAhprpmi1W9YHy5oIWv+s0kk8QmXNAUNdpyuvvNL1m7L097bdLTGHNWm0ppShjnP1/fffu/5oti3S0tL2al0AAADKkldQpM0BAqRAjy2hWyiapsSreYMENW/od/N7vHb7To19Z3aFy2nRMLFWf4AEVzXAAihrL2rVmhZ9205ifayqsv2oJX/49ddfNW7cOJ1++ukug6AlSbDgyaZZ8gR/1113nZvfsgU2atRIjz/+uAvMjPVhevDBB3Xttde6IKtfv3766KOPivsfWZIHC+BsGZbm3QICC4qOPfbYCstptVLWN8mSUpQePNcCGWt6aGnjrQbJrppYM0LLsOdj722BlyW5aNmypSvH7bffvtfbL9R1smDMxhazxBc5OTlum1smQ1uvUNx9990uWLT1tD5eofSbAwAAsGZ523fm+wVIOe7/xoxSwVNWrrZn7x6GJhgp8TFlBkvN3eNE979Zg3jFxZQ/ApTVbj3+5aIKu88Ek5egJkV5OFvbQ0ZGhlJTU5Wenu4CC392omzJE6zmx8ZlAoJlTTMtoP3Pf/5T5RuN/RQAgPptR25B4NqlUtOsJqoghGZ5sdFR5QRLu++nNUhQSkJslWQLVBndZ2oqW2B5sUFp1FwBVcDG8XruuedcbZ8lwXjzzTeLE4AAAACU1Sxvy47ygyXf/ey88DbL8z1OTYpTdA1l4xtTQ91nwongCqgClhzkk08+cX3HrBbJ+oPZAMk2NhgAAIgcZTXLCxQ4bavBZnmR3H0mnAiugCpgSTSspgoAANRP9bFZXm0REx1Vq9Otl6d+fiIAAABAiCK9WR72HsEVAAAA6i2a5aE6EVwBAACgzqFZHmojgisAAADUCjTLQ11HcAUAAIAqQ7M8RBKCKwAAAISMZnnAngiuqtv2VVL2lrKfT24mNW6vuuz888/X9u3b9f7777vHhxxyiPbdd1+NHz++Wsvx7bff6tBDD9W2bdvUuHHjMsejeu+993TiiSdW+n0qs37heF8AAMKNZnnA3iG4qu7A6ulBUkFu2fPEJkhXzAh7gGUBz6uvvurux8XFqUOHDjr33HN1yy23KDa2aneDiRMnuvcMV0BU24SyfsGqi9sBAFA70SwPqD4EV9XJaqzKC6yMPW/zVUHt1ZgxY/Tyyy8rNzdXn3zyif7+97+7oODmm2/eY968vDzFx8eH5X2bNm2q+qy+rx8AoHaiWR5Q+xBchYPHI+VnVzxfwc7glmfz5e2oeL64ZGtfFtwyJSUkJKhVq1bu/uWXX+6apX344YcuuPI15dt///31zDPPuHmXL1+uVatW6brrrtMXX3yh6OhoHXTQQXriiSfUqVMnt5zCwkKNHTtWL730kmJiYnTRRRfJY9ujnGZzFtzdcccd+u9//6uNGzeqffv2rgyHH364q60xTZo0cf/PO+88vfLKKyoqKtJDDz2kf//731q/fr169Oih22+/Xaeeemrx+1jAePXVV7syH3jgge61wdi8ebNOOukkff7552rbtq0ee+wxHX/88cXPz5kzx63jDz/8oJSUFB155JH65z//qbS0tIDrt27dOl188cX6+uuv3fa+7777XA2hlc1uFb3vn3/+WeZ2AADUbzTLQ0TbXve7zxBchYMFVve3Udi8NCa4+W5ZK8WnVPptkpKStGXL7h148uTJatSokb788kv3OD8/X6NHj9bQoUNdYGHNB++9915XAzZ79mxXs2UBgZ30W3DVu3dv99iCtsMOO6zM97XmiD///LOefPJJDRgwwAVxFmhYkPXuu+/qlFNO0cKFC11ZrIzmgQce0Ouvv67nnntO3bt31/fff69zzjlHzZs318iRI11AdfLJJ7vauEsvvVS//vqrCwqDcdddd+nhhx/WI488oqeeekpnn322VqxY4WqkLOC0dbFgyQKqnTt36sYbb9Tpp5/ugqey1s/Wx5r2Wc3gtdde64LIYN+3vO0AAKh7aJYH1O7uM+FEcBWBrGbJAimrMbnyyiuLp1utzAsvvFDcHNCCGasxsmmWgMFYs0LrA2SBg9XgWG2N1TpZYGMs+LHllmXRokX63//+5wK4UaNGuWldunTZo4ldixYtivsaWU3X/fffr6+++soFer7XTJkyRc8//7wLrp599ll17drVBXemZ8+e+uOPP1xtV0Ws1u7MM8909+19LOibPn26CyKffvpp7bfffm66jwWSFgDZulgNmr8FCxa4cv7yyy8aPHiwm2bbzwLCUN430HYAANQuNMsD6k/3mXAhuAoHa55ntUgVWT87uFqpCz+TWvUP7n1D8PHHH6tBgwauRsqCprPOOkt33nln8fP9+vUr0c9q1qxZWrJkiRo2bFhiOTk5OVq6dKnS09NdE7gDDjig+Dmr3bKgonTTQJ+ZM2e65oMWEAXLypCdna0jjjhij35hFviY+fPnlyiH8QViFenfv3+JANNqinw1TbYNvvnmG7fdSrNtUDq4spom2wYDBw4sntatW7fi5n3Bvi8AoP41y2uSHKfmDRO8twa7/jdMUIuGiSWmpybFKTo6+Gb/AGoPgqtwsFqdYJrnxQbZtMvm24vmfmWxfjxWw2MBVJs2bfbIEmgn+P6ysrI0aNAgvfHGG3ssy5rjVUZlmrdZOcykSZNc3yR/1jdsb5XO9Ge1dBZ8+t77uOOOC1gD1rp16yp7XwBA3WiWlxwfs0ewVOL+rluzlATFx0bzsSJyeTxS9lYpfdWu2+pdt1XeJoFbl6s+ILiKIBY8WS1KsKz2ZcKECa5pmtWqBGIBxrRp03TwwQe7xwUFBZoxY0aJmht/VjtmAcR3331X3CzQn6/mzBJl+PTp08cFUStXriyzxsv6e1lyDn9Tp07V3rL1sP5PlsAjmJT11hzRtsHvv//uAlNfzZulVA9FoO0AANizWd7mUjVKGzP2DJhsnoKiwC0qAomNjlJaBcGSb1pKAqdSgFOQJ2WsKRk0lQiiVgeXAK6O44hQnSzDiXXEq6ijns1XC1iCBUu2cMIJJ+juu+9Wu3btXMIFG9fphhtucI+vuuoqPfjgg65PUa9evfT444+7JBBlsSDFMt9deOGFxQktbJnWHM6SRHTs2NHV4FgTxqOPPtrVdFmzxOuvv17XXHONC8xGjBjhmiT++OOPLuiz5V122WWuv5Vl9bPkExbghSO7niXI+L//+z/XN8rW2fpCWbD01ltvub5U1sTRn20DCxotqYbVElrtlCXWsPXw9VsLRqDtEKhpIgDUN/mFRdqSlVdhDZPddoSpWZ738e6meY1plgfsWeuUs91bw1QcLK0sGThlrrcZK95yDVpKqe123drvurXzBl4TL6nzW57gqjpZ5zvLcFJHUkwmJye7rHyWHc8SVmRmZrpmeZYy3VeTZYGD9buyAMdStVvQZOnFLfgpiwUdlpr8b3/7m8tWaAMa22Njy7csejfddJMuuOACl3nPgqR77rnHNUW0rIHLli1zSR6sVsn3OluG1TBZAGaZ94YMGeKSRFh59oY1n7QgzraBJfCw5BoW+FjSCVvfQF577TWXkt5q8ywVu5V57ty5SkxMDPp9y9oOAFAXWT/c7dn5ewRHgR5v3ZEX0rKT4mLUohHN8oC9UpgvZa7zC55K/18t5Xm7aZQrJsEbKNm5bOngyW6N2kpxZZwPrZ1ZLz7EKE9ZmQciWEZGhlJTU12AULo5nCVzsNThnTt3DulkGZFr9erVLrugZRG0wLQ6sJ8CqA7ZeQUVBku+Znn5hTTLA2pMTvruIGl7qRonC6AssPIE0e87Oc0vePILmnxBVEpaSGOw7hFc/TuIhGeXfie12Ve1JTYojZorIMxs/CtLhGH9y6xWz5oTWnNIX780AKjNaJYH1DFFhd7gqHSCCP/HuRkVLycm3luz5AuWimufdj225+JDy1Rdn7vPlIXgCggzS3VvzRWt+aL1Fxs2bJjLuFg6OyAAVBea5QF1WG7Wngki/IMnSyLhCaL/YVKTPZvp+ddApbSQyujyUC0a163uM2UhuALCbPTo0e4GAFWNZnlAHWdDsGRtCJwgwgVQq7yJJCoSHSs1aiOldvCrbfILnqzWKaEOJMZqvKvGrA4juAIAoBahWR5Qj+Rll50gwvo+ZayVioIYWy0x1a/GqVRzPQtGLANfdMkMxqgZBFeVRB4Q1Gbsn0DtQrM8oJ7WOmVv3l3DFCiIKq+Jm09UtF9fpwApyu2WWH4SBdQeBFch8vWbyc7OdmMPAbWR7Z+Gfl5AFX/XyJYH1F/5Od7+THtk1/M9XiMVlpN8wSe+QeAEEb7gqWFrKYZT8vqCTzJENmisjbFkg976xoIKZXBYoKqvjltgZfun7aelBzkGUDGa5QERwEYislqlPRJE+P3fsSmIBUV5g6NACSJ8/61JH+eKEYPgqhJsYFjjC7CA2sYCK99+CoBmeUDEKciTMgIkh/CvgSrYWfFy4pLLzq7nap3aSLHx1bFGqCMIrirBaqpat26tFi1auLTbQG1iTQGpsUKkoFkeEKG1Tju3lQqWSo3tZBn4FMTA1ZYIIlCCCN99S19OrRNCQHC1F+wElpNYAAgvmuUBEa4w35tFr/TYTv7N9/J3VLyc2MTAfZx8wZMlkbBBaYEwIrgCgAhSWOTR9OVbtTEzRy0aJmpI56aKia76fqNkywNQbOf2kjVOpYOnzHXB1TqlNPcLnkqP79TBO+AstU6oZgRXABAhPpuzTnd9NE/r0nOKp7VOTdS44/pozD6tK7VMmuUBKKGwQMpaXypBRKm+TrkZFW+0mPhStU6l/7eV4sjajNqH4AoAIiSwuvz13/a4Frw+PcdNf/acgcUBFs3yAJQpN7OMBBG77ltzPk9hxRswqenuGqYS4zvtemy1UtHRfBCocwiuACACmgJajVWgRja+af94a6Y6NV2kzTvytHVHXkjLT4qLUYtGCWreIEHNG+66+d/fdWuWkqD4WE6WgFqrqNCbCCJQgghfDVROesXLiY7dNShuqT5OxcFTWyk+pTrWCKh2BFcAUM9ZHyv/poCB5BUUadHGrOLHsdFRSqsgWPJNS0ngpwSoE/J2lKxlKh08Wa1TURBZkBMbB0hP7tdszzLwRTPOIiITv4gAUM9Z8opg/O2Qrjph37YuYGqcFKfoakh0ASBMioq8g94GzK636/HOrRUvJypGatSmVHpyv+Z7ViOV2IiPDSgDwRUA1PPA6o2pK4Ka96DuzdWzVcMqLxOASsjfKaWvCZAgYlfzvYw1UmEQTXrjG+5Z0+TffK9BKymG00Ogsvj2AEA9ZKnP3/t9jetrlb6z/GY+Vj/VKtWblh1ADbBBcXdsDpAgwq/5XvbmipcTFS01bF0qQUSp8Z0SU6tjjYCIRXAFAPXM2u07det7f+ibhZvc433aNtIJA9rq/k/mu8f+iS18Df8sHXt1jHcFRKSCXG/NUqAEEb7HBUE0341LKSNBxK771pwvJq461ghAGQiuAKAe1Va9OX2VC6KycgtcZr6rR3XXpQd1UWxMtNo3TdpjnKtWeznOFRDxrNZp57ZS2fVK/bcMfMGwJnmBEkT4/ic1YVBcoJYjuAKAemDllmzd+O5s/bxsi3s8sENjPXzqAHVr0aB4HgugjujTymUPtL5YLRp6mwJSYwWUoyBPylwbOEGE75a/o+JNGJsUOEFEca1TWyk2gY8CqOMIrgCgjo9h9cpPf+rRzxdqZ36hG3Nq7OieOm9Yp4BBk00b2rVZjZQVqJW1TjZuU6AEEb7HmetKNaYtgw1661/TVKLpXnspuRm1TkAEILgCgDpqycZM3fDObP22crt7PLRLMz10Sn91aJZc00UDaofCAm9wFChBhC94ysuseDkx8Xsmhyhuvtfe29cpLqk61ghALUdwBQB1TH5hkf79/TI98dVi5RUWqUFCrG45urfOHNJeUVEkpUAEycnwq3FaWbKpngVQ1pzPU1TxcqxWKVDw5KuBSk6ToqOrY40A1HEEVwBQh8xdm+5qq+auzXCPD+3ZXPed1E9tGnPVHPVMUaGUuT5wggjffWvSV5HoOCm17Z5Bk///eGp7AYQHwRUA1AG5BYV65usl+te3S1VQ5FHj5DjdcWwfnbRfW2qrUDflZvmlJw+QZS9jrVRUUPFyEhsH7uPkC5watJCiY6pjjQCA4AoAarvfV25ztVWLN2a5x0f3a6W7jt9HzRuSWQy1VFGRtGPjruZ5pZrr+ZrvWfryikTFeLPo7TG2ky94aislNKyONQKAoFBzBQC11M68Qv3zq0V64YdlKvJIaQ3idc8J++iofoxJhRqWl+2tddojQcSu//ZcYV7Fy0lILZWe3L/PU3upYStqnQDUKQRXAFALTVu2xY1b9eeWbPf45P3a6vZj+6hJSnxNFw2RkJ58x+YACSL8Hmdvrng5UdFSw9aBs+v5HiemVscaAUC1IbgCgFokK7dAD326QP+ZusI9bp2aqPtP6qdDe7Wo6aKhvsjP2VXrVKqPk3968sLcipcTl+LXVC/A+E4WWMXEVccaAUCtQXAFALXE94s26eaJf2jN9p3u8ZlDOujmo3upUSInqAih1il7a+AEEb7me9YXqkJR3iZ5gRJE+GqgLJEEqf8BoASCKwCoYenZ+bp30jy9PWO1e9y+aZIeOrm/hnVLq+miobYpyPOrdSpjfKd8b1PScsUmlZ1dz26WRCKWJqgAECqCKwCoQV/MXa9b35+jTZm5rhLg/GGdNHZ0TyXHc3iOyFqnnO0BEkT4NdezcZ/kqXhZKS0C93HyBVHJTal1AoAqwK83ANSALVm5GvfhXH08e5173KV5ih45tb8GdWzK51FfFeZLmev8gqdSA+La/zxvuv1yxSSUnSDC7lutU1xidawRAKAUgisAqEYej0cfzV6nOz+cq6078hQTHaVLD+6iqw7vrsQ4Bjqt03LSSwZL/gkiXK3TWslTVPFyktNKBkulm++lNKfWCQBqKYIrAKgmGzJydOt7c/TV/A3uca9WDfXIqQPUrx3pqGu9okJvk7yysuvZLTe94uVEx3kHvg2UIMJX6xSfXB1rBACoAgRXAFANtVVv/7pa90yap8ycAsXFROnKw7rrspFdFR8bzfavDXKzAvdx8gVQlkTCU1jxcpKalJFdr4P3v/WFiuYzB4D6qsaDq2eeeUaPPPKI1q9frwEDBuipp57SkCFDypx//PjxevbZZ7Vy5UqlpaXp1FNP1QMPPKDERG/78jvvvFN33XVXidf07NlTCxYsqPJ1AYDSVm/LdunVf1jsHXR1QLtUPXzqAPVs1ZCNVV2KiqSsDYGDJ18NlCWSqEh0rNSoTckxnUr8byclNKiONQIA1FI1GlxNmDBB1157rZ577jkdcMABLnAaPXq0Fi5cqBYt9hww87///a9uuukmvfTSSxo2bJgWLVqk888/X1FRUXr88ceL5+vbt6+++uqr4sexsTUeQwKIMEVFHr0+bYUbEHhHXqESYqN13ZE9dOHwzoqNoeYirPKyS43pVHp8pzVSUX7Fy0lILdW/qVQNlI37FE2/OABA2Wo06rCA6JJLLtEFF1zgHluQNWnSJBc8WRBV2k8//aThw4frrLPOco87deqkM888U9OmTSsxnwVTrVq1Crocubm57uaTkZGxF2sFINIt37xDN74zW9P/3OoeD+nUVA+d2l+d01Jqumh1Mz35jk0B+jj51UBlb6l4OVHRUsM2fv2bfMHTruZ61g8qkb5vAIA6Glzl5eVpxowZuvnmm4unRUdHa9SoUfr5558DvsZqq15//XVNnz7dNR1ctmyZPvnkE/31r38tMd/ixYvVpk0b11Rw6NChrtlghw4dyiyLPV+6KSEAhKqwyKMXpyzTY18sUm5BkZLjY3TzUb109gEdFR0dxQYNJD9n16C4ZQVPa6TC3Re/yhTfYHcNU3Hw5Pe/YWsphlYMAICqVWO/NJs3b1ZhYaFatmxZYro9Lqt/lNVY2etGjBjhOogXFBTosssu0y233FI8jzUvfOWVV1w/q3Xr1rmg6aCDDtKcOXPUsGHgPg4W4FnzRP+aq/bt24dtXQHUfwvXZ+qGd2Zp1mpvxriDuqfp/pP6qX3TWpL5zQKX8mp4kpt5g5Jw1zrZe+6RIMKvyZ7VSlUoyhsc+TfX8yWI8N0SG5OeHABQ4+rUZbxvv/1W999/v/71r3+5IGrJkiW66qqrdM899+j222938xx11FHF8/fv39/N17FjR/3vf//TRRddFHC5CQkJ7gYAocorKNKz3y7V098sVn6hRw0TY3X7sX102qB2rj9orWABzdODpIJyaoBiE6QrZoQWYBXk7a51Kg6eVpZMT16ws+LlxCUHThDhq4Gy5nyx8cGXCwCASAuuLNNfTEyMNmzwjvfiY4/L6i9lAZQ1Abz44ovd4379+mnHjh269NJLdeutt7pmhaU1btxYPXr0cIEYAITTH6vTNfadWVqwPtM9HtW7pe47aR+1bOTNXlprWO1ReYGVsedtPl9wZbVOO7cFSA7hl57cMvDJU/H7N2gZOEGErwbK0pfXlkAUAIC6GFzFx8dr0KBBmjx5sk488UQ3raioyD2+4oorAr4mOzt7jwDKAjRjzQQDycrK0tKlS/folwUAlZWTX6gnJi/Wv79f5vpZNU2J153H99Vx/VvXntqqyvjhUSl/5+4AKn9Hxa+JTSw7u57vZrViAABEgBptFmj9nM477zwNHjzYJaiwVOxWE+XLHnjuueeqbdu2LuGEOe6441yGwf3226+4WaDVZtl0X5B1/fXXu8fWFHDt2rUaN26ce86yCgLA3pqxYqvGvjNbyzZ5A4/jBrTRncf1UbMG9SCAmP/RntOS0wIniPD9T0mj1gkAgNoQXJ1xxhnatGmT7rjjDjeI8L777qvPPvusOMmFDRTsX1N12223uavC9n/NmjVq3ry5C6Tuu+++4nlWr17tAqktW7a45y35xdSpU919AKis7LwCPfL5Qr3y05+uxVyLhgm698R9dGTf4Id9qBFW2PV/BDfvwPOkdoP9UpS3leKSqrqEAADUG1GestrTRTDLFpiamqr09HQ1atSoposDoIb9tGSzbpw4W6u2epMznD64nW49uo9Sk+NUa+VmSrP/J/36krRhTnCvufQ7qc2+VV0yAADqbWxQp7IFAkB1ysjJ1wOfLNCb01e6x20bJ+mBk/vp4B61uCZ8w1zplxe9gVWeN9GGYhKCGysKAADsFYIrAAjg6wUbdMvEOVqfkeMenzu0o24Y00sNEmrhYdMy/c37UPr1RWml3yDszbpLgy+UWvWTXj22JksIAEBEqIVnCQBQc7btyNM9H8/TxN/XuMedmiXroVP664AuzWrfx7JthTTjZem3/0jZm73TomKk3sdKgy+SOh/sTTZhmf8sY19F41zZQMIAAKDSCK4AYJdP/linOz6Yo81ZeYqOki4+qIuuGdVDSfHebKS1QlGhtPhLby2V/feNM2UD7Q46Xxp4rtSodcnXWLY/GyDYxrEqiwVWoQwgDAAA9kBwBSDibczM0bgP5urTOevdtujRsoEePnWA9m3fuPZsm6xN0u+vSb++IqV7+4A5XQ6V9r9Y6jFGiinnkG6BE8ETAABViuAKQMSyZKnv/b5Gd300T+k78xUbHaW/HdJVfz+smxJia0FtlSVztT5Uv7zg7VNVlO+dnthY2u8cb3+qZl1rupQAAGAXgisAEWnt9p269b0/9M3CTe5x3zaN9MipA9SnTS0YfiEnQ5o9wZv1b9P83dPbDpb2v0jqexLjTwEAUAsRXAGIuNqqN6ev0v2fzFdWboHiY6J11ajuuvTgLoqL2T1oeY2wwX59adTzd3inxSVL/U71JqhgDCoAAGo1gisAEWPllmzd+O5s/bzMm9hhYIfGevjU/urWomHNFSo/R5r3gbfp3+rpu6en9fD2pep/hpRUi/p+AQCAMhFcAaj3Cos8evWnP/XI5wu1M79QSXExGju6p84b1kkxlhawJmxdJv36svT769LOrd5p0bFS7+O8tVSdRnjTqAMAgDqD4ApAvbZkY5ZueGeWflu53T0e2qWZG7eqQ7Pkmkmjvuhzbxr1JV/tnt6o3e406g1bVn+5AABAWBBcAaiXCgqL9Pz3y/TE5MXKKyhSg4RY3XJ0b505pL2iqrtGKHPD7jTqGat3T+96uLfpX/cjy0+jDgAA6gR+zQHUO/PWZuiGd2dpzpoM9/jQns1130n91KZxUvWmUV/xo7cv1fyPpKIC7/SkprvSqF8gNe1SfeUBAABVjuAKQL2RW1CoZ75eon99u1QFRR6lJsVp3HF9dNJ+bauvtionXZr1lvTrS9KmBbuntxviTaPe50QpLrF6ygIAAGp3cHX33Xfr+uuvV3Jyyf4KO3fu1COPPKI77rgjnOUDgKD8vnKbbnhnthZvzHKPj9qnle46oa9aNKymQGbdLG8a9T/elvKzvdPiUqT+p3kTVLTuXz3lAAAANSbKY4O+hCAmJkbr1q1TixYtSkzfsmWLm1ZYWKi6LiMjQ6mpqUpPT1ejRrVgQFEAZdqZV6h/frVIL/ywTEUeKa1BvO4+YR8d3a911W+1/J3S3Pe8QdWaX3dPb97bW0vV/3QpMZVPDwCACIkNQq65slgsUPOaWbNmqWnTpqEuDgAqbdqyLW7cqj+3eGuKrPnfHcf2UZOU+KrdqluWepv9zXxD2rnNOy06TupzvLeWquMw0qgDABCBgg6umjRp4oIqu/Xo0aNEgGW1VVlZWbrsssuqqpwAUCwrt0APfbpA/5m6wj1u1ShR95+8jw7rVYVpzAsLpEWfedOoL/169/TU9rvTqDcoWaMPAAAiS9DB1fjx412t1YUXXqi77rrLVY35xMfHq1OnTho6dGhVlRMAnO8XbdLNE//Qmu073WNLrX7z0b3VKDGuarZQ5nppxqvSb69KGWt2TYySuh/hraWy/9ExfDoAACD44Oq8885z/zt37qxhw4YpLq6KTmQAIID07HzdO2me3p7hHSeqfdMkPXRyfw3rlhb+7WVdUZd/762lWjBpdxr15GbSfn/1plFv0onPCQAA7F2fq5EjR6qoqEiLFi3Sxo0b3X1/Bx98cKiLBIByfTF3vW57f442ZubKWiSfP6yTxo7uqeT4MI8msXO7NOtNb3+qzYt2T+8w1FtLZX2qYhP4tAAAQEAhn5lMnTpVZ511llasWOGaCfqzflj1IVsggNphS1au7vxonj6atdY97tI8RY+c2l+DOoY5ec7a372D/f7xrlTgbW6o+AZS/zO8Wf9a9g3v+wEAgHop5ODKklYMHjxYkyZNUuvWratvYE4AEcMu3Hw0e53u/HCutu7IU0x0lC49uIuuOry7EuPC1L8pL1uaO9GbRn3tb7unt+gr7X+hN7BKaBie9wIAABEh5OBq8eLFeuedd9StW7eqKRGAiLYhI0e3vjdHX83f4B73atVQj5w6QP3ahWm8qM1LdqdRz9nunRYTL/U50VtL1f4A0qgDAIDqCa4OOOAALVmyhOAKQNhrq97+dbXumTRPmTkFiouJ0pWHdddlI7sqPjZ679OoL/zE2/Rv+Xe7pzfu6E1OYUkqUqogMQYAAIgoQQVXs2fPLr5/5ZVX6rrrrtP69evVr1+/PbIG9u/fP/ylBFCvrd6W7dKr/7B4s3s8oF2qHj51gHq22stmeRlrd6dRz1y3a2KU1GO0tP/FUtfDpei9DNwAAAB2ifKUzkoRQHR0tOtbVdasvufqS0KLjIwMN45Xenq6GjVqVNPFAeqtoiKPXp+2wg0IvCOvUAmx0bruyB66cHhnxcZUMuixDKZWO+XSqH8ieXYdk1Kaewf6tQF/G3cI63oAAID6K5TYIKiaq+XLl4erbADgPa5s3qEb35mt6X9udY+HdGqqB0/ppy7NG1RuC2Vv9aZRtwQVW5funt5xuDT4Qqm3pVGPZ+sDAIAqE1Rw1bFjx6orAYCIUljk0YtTlumxLxYpt6BIyfExuumoXjrngI6Kjq5E9tE1M7wB1RxLo57jnRbfUBrwF29Q1bJP2NcBAAAgLAktPvzww4DTrUlgYmKiS3TRuXPnUBcLIAIs2pCpse/M1qxV3ix9B3VP0/0n9VP7psmhp1Gf8443qFo3c/f0lv28Gf/6nSYlVLIGDAAAoLqCqxNPPDFg/yv/flcjRozQ+++/ryZNmlS2XADqkfzCIj377VI99fVi5Rd61DAxVrcf20enDWoX2lh5mxbtSqP+Xyk33TstJkHqe5I3qGq3P2nUAQBAjQm5x/iXX36p/fff3/23Tl12s/uWov3jjz/W999/ry1btuj666+vmhIDqFPmrEnX8U//qMe/XOQCq1G9W+qra0fq9MHtgwusCvOlue9JrxwrPbO/NO1Zb2DVpJN0xN3StfOlk5+X2g8hsAIAAHWr5uqqq67Sv//9bw0bNqx42uGHH+6aBF566aWaO3euxo8frwsvvDDcZQVQh+TkF+rJyYv1/PfLXD+rpinxuvP4vjquf+vggqr01bvTqGd5BxRWVLTUY4y3lqrLYaRRBwAAdTu4Wrp0acAUhDZt2bJl7n737t21ebN3vBoAkWfGiq264Z3ZWrpph3t83IA2uvO4PmrWIKHiNOrLvvH2pVr0qeQp8k5PaSENOk8aeJ7UuH01rAEAAEA1BFeDBg3S2LFj9dprr6l58+Zu2qZNm3TDDTe45oJm8eLFat+eEyAg0mTnFeiRzxfqlZ/+lHXLbN4wQfeeuI9G921VwQu3Sr+/7u1Ptc1v6IdOB3kz/vU6ljTqAACg/gVXL774ok444QS1a9euOIBatWqVunTpog8++MA9zsrK0m233Rb+0gKotX5aslk3TpytVVt3useWrOK2Y/ooNTku8Ass+lr9q3ew3zkTpcJc7/SERtKAM71BVYte1bgGAAAAeyfKUzrtXxCKior0xRdfaNGiRe5xz549dcQRRyg6OuT8GHV+FGYg0mXk5OuBTxbozekr3eO2jZN0/8n9NLKHt2Z7D7lZ0h9ve4Oq9X/snt6qv7T/xVK/U6X4lGoqPQAAQPhig0oFV/UdwRUQnK8XbNAtE+dofYZ38N6/HthRNx7VSw0SAlSKb1zgDahmvSXlZninxSZKfU/2JqhoO4hsfwAAoE7HBkE1C3zyySddJkDLCGj3y/OPf/wjtNICqHO27cjTPR/P08Tf17jHnZol68FT+uvALs1KzliQJy34SPrlJWnFlN3Tm3bxNvvb92wpuWk1lx4AAKBqBFVz1blzZ/36669q1qyZu1/mwqKiijMG1mXUXAFl+/SPdbr9gznanJWn6CjpohGdde0RPZUUH7N7pu2rpBkvS7/9R9qxcXca9Z5He2upOh9CGnUAABCZNVfLly8PeB9A5NiUmas7PpijT+esd4+7t2igh0/tr/06NNmdRn3pZG8a9cWf706j3qDV7jTqqW1rcA0AAABqWbZAn7y8PBdode3aVbGxlV4MgFrOKrffn7lGd300T9uz8xUbHaW/HdJVfz+smxJiY6QdW6Tf/+Otqdr25+4Xdj7Ym6DCaqtiysgYCAAAUI+EHBVlZ2fryiuv1KuvvuoeW8ZAS8Nu09q2baubbrqpKsoJoAasS9+pWyb+oW8WbnKP+7ZppEdOHaA+rRtKq6ZLv7wgzXtfKszzviAx1duPyvpTpXXnMwMAABEl5ODq5ptv1qxZs/Ttt99qzJgxxdNHjRqlO++8k+AKqCe1VW9OX6UHPpmvzNwCxcdE66pR3XXpAc0VN/dt6YOXpA1zdr+gzX7S4IukfU6R4pNrsugAAAB1J7h6//33NWHCBB144IEugYVP3759tXTp0nCXD0A1W7klWzdNnK2flm5xjwd2aKzxh8arw7JnpfETpLzM3WnUbUwqC6raDuRzAgAAES/k4GrTpk1q0aLFHtN37NhRItgCULcUFnn06k9/6pHPF2pnfqEaxhXqyQGrdUj6U4qa8PPuGZt18wZU+54pJe1KZgEAAIDQg6vBgwdr0qRJro+V8QVUL7zwgoYOHcomBeqgJRuzdMM7s/Tbyu1qF7VJY9N+0rGFXylmjrf2SlExUq9jvAkqLFEFF1IAAAD2Pri6//77ddRRR2nevHkqKCjQE0884e7/9NNP+u6770JdHIAaVFBYpOe/X6anJi/U0KLf9WrCZB0c9buisnYNf9ewjTTofGnguVKj1nxWAAAA4QyuRowYoZkzZ+rBBx9Uv3799MUXX2jgwIH6+eef3WMAdcO8tRm67+3v1H/jx/oyZrLax3ozAjpdDvUO9tvjKCmGoRYAAACCEeWxtGCo9CjMQF2Tm1+g9z54V8mzXtGY6GmKjyp00z2JjRW13zneNOrNutZ0MQEAAOpcbBAbykKDQTAC1FI5GVr13SsqmPaC/lK0QorxTs5vtZ/iDrxUUX1PkuKSarqUAAAAdVbQwVXjxo3LzQZoFWD2fGGh9yo4gFpi/RwVTH9BhTMnqH1Rtpu0Uwna1Ol4dTjyCsW12bemSwgAABBZwdU333xTIpA6+uijXYbAtm3bVlXZAFRWfo407wPp1xelVdPcF91uS4raaHbrU3TY6VepQ7PmbF8AAICaCK5GjhxZ4nFMTIwbSLhLly7hLA+AvbF1uTTjZen316Vsbxr1fE+MPi8arE8SjtGpp5yhk3u3YhsDAABUAdKAAXVdUaG0+AvplxekJZOtbtlNXq80vZ5/qCYUHqJRQ/rrwaN7q1FiXE2XFgAAoN4iuALqqqyN0m+vSjNeldJXFU9e2GCIHt06Ql8X7ac2TRto/Mn9NbxbWo0WFQAAIBLsVXBVXoILAFXARk5Y8aP0y4vS/I+konzv9KQm+rP9Sbp22UD9trmp7Kt5/vBOGju6p5LjuYYCAABQHYI+6zr55JNLPM7JydFll12mlJSUEtMnTpwYvtIB2PWFS5dmTfAmqNi0YPdWaTdEmf3O1R1Luuu92d4+Vl2ap+jhU/prcKembD0AAIDaGFzZwFn+zjnnnKooDwB/62Z5a6n+eEfK3+GdFpci9T9NnsEX6qONzXXnh3O1dccWxURH6dKDu+iqw7srMW7XIFYAAACofcHVyy+/XLUlAbA7jfrc97y1VKt/2b1VmveSBl8kDThDG/ISdNv7c/TlvN/dU71aNdQjpw5Qv3YlL4IAAACg+tAZA6gttizdnUZ95zbvtOg4qc/x3qCq4zCXB/DtGat1z8fzlJlToLiYKF1xaHddfkhXxcdG1/QaAAAARDSCK6AmFRZIiz7z1lIt/Xr39NT20qDzpYHnSg1auEmrt2Xr5ol/6IfFm93jAe1S9fCpA9SzVcOaKj0AAAD8EFwBNSFzvfTba9KMV6SMNbsmRkndRkn7Xyx1P0KK9vabKiry6I1pK/Tgpwu0I69QCbHRuu7IHrpweGfFxlBbBQAAUFsQXAHVmUb9zx+8g/0umCQVFXinJzeT9vurt6aqaecSL1m+eYdufHe2pi/f6h4P6dRUD57ST12aN+BzAwAAqGUIroCqtnO7NOtN6deXpM2Ldk9vf6C0/0VSnxOk2IQSLyks8ujFKcv02BeLlFtQpOT4GN10VC+dc0BHRUczvhwAAEC9CK5ee+21cp8/99xz96Y8QP2x9vfdadQLdnqnxTeQ+p/uTVDRap+AL1u0IVNj35mtWau2u8cHdU/T/Sf1U/umydVZegAAAIQoyuOxtkrBa9KkSYnH+fn5ys7OVnx8vJKTk7V1q7f5Ul2WkZHhxvVKT09Xo0aNaro4qEvyd0pzJnqb/q39bff0Fn28tVT9z5ASAiegyC8s0rPfLtVTXy9WfqFHDRNjdfsxfXTa4HaKiqK2CgAAoLbHBiHXXG3btitFtJ/Fixfr8ssv19ixY0NdHFA/bF7ibfY38w0px1vjpJh4b5M/q6XqcKBUToA0Z026q62avy7DPR7Vu4XuO6mfWjZKrK41AAAAQG3oc9W9e3c9+OCDOuecc7RgwYJwLBKoG2nUF37iTaO+7Nvd0xt3kAZfKO17jtSgebmLyMkv1JOTF+v575e5flZNkuN05/F9dfyANtRWAQAARGpCi9jYWK1duzZciwNqr4y1u9OoZ67bNTFK6jHaW0vV7fDiNOrlmbFiq254Z7aWbtrhHh/bv7ULrNIalExuAQAAgHoaXH344YclHluXrXXr1unpp5/W8OHDw1k2oPawronLv9uVRv0TyVPonZ7SfHca9SYdg1pUdl6BHv18kV7+ablbbPOGCbr3xH00um+rql0HAAAA1K7g6sQTTyzx2DraN2/eXIcddpgee+yxcJYNqHk7t0kz/+vtT7Vlye7pHYZ5E1T0Pl6KjQ96cT8t2aybJv6hlVuz3eNTB7VzSStSk+OqovQAAACozcFVUVFR1ZQEqE3WzJB+eUmaY2nUc7zT4htKA/7i7U/Vsk9Ii8vIydcDnyzQm9NXusdtGyfp/pP7aWSP8vtkAQAAoO5gEGHAJy/bG0zZ2FTrZu7eLi37SftfKPU7XUpoEPL2+mbBRt3y3h9al+4N0v56YEfdeFQvNUjg6wcAAFCfVOrsbvXq1a7v1cqVK5WXl1fiuccffzxcZQOqx6ZF3mZ/s/4r5aTvTqPe9yRp/4uldvuXm0a9LNt25Omej+dp4u9r3ONOzZL14Cn9dWCXZuFeAwAAANTF4Gry5Mk6/vjj1aVLF5d2fZ999tGff/7pElsMHDiwakoJhFthvrRgkjdBxZ8/7J7epNPuNOoplQ+CPv1jnW7/YI42Z+UpOkq6aERnXXtETyXFV5xFEAAAABESXN188826/vrrddddd6lhw4Z699131aJFC5199tkaM2ZM1ZQSCJf0Nd4U6pZKPWu9d1pUtNRjjDeNetfDpOjoSi9+U2au7vhgjj6d41129xYN9PCp/bVfhybhWgMAAADUl+Bq/vz5evPNN70vjo3Vzp071aBBA91999064YQTdPnll1dFOYHKsyQsy77xNv1b+KlfGvUW0sBzvWnUG7ffqy1sNbfvz1yjuz6ap+3Z+YqNjtLlh3TVFYd1U0IstVUAAACRIOTgKiUlpbifVevWrbV06VL17dvXPd68eXP4SwhUVvZWaeYb3qBq67Ld0zsd5G361+vYkNKol2Vd+k7dMvEPfbNwk3vct00jV1vVt00qnx0AAEAECTm4OvDAAzVlyhT17t1bRx99tK677jr98ccfmjhxonsOqFE2Ku/qX6VfX5TmTJQKc73TExpJA870BlUteoXprTx6c/oqPfDJfGXmFig+JlpXjequSw/uoriYyjctBAAAQIQEV5YNMCsry923fld2f8KECerevTuZAlFz8nZIf7ztTaO+fvbu6a36ewf77XeaFJ8StrdbuSVbN02crZ+WbnGP9+vQWI+c2l/dWjQM23sAAACgbony2OV3lJCRkaHU1FSlp6erUaNGbJ3qtn2VlO0NWgJKbra7j9TGBbvSqL8p5WZ4p8UkSPuc4g2q2g6qVBr1shQWefTqT3/qkc8Xamd+oRLjojV2dC+dP6yTYiwtIAAAACI2NqjUOFfbt2/XO++84/pbjR07Vk2bNtVvv/2mli1bqm3btpUtN+ANrJ4eJBXsas4XiAVPo++T5r4vrZiye3rTLrvSqJ8tJTcN+9ZcsjFLN747WzNWbHOPh3ZppgdP6aeOzcJXIwYAAIC6K+SOIbNnz1aPHj300EMP6dFHH3WBlrE+V5amPVTPPPOMOnXqpMTERB1wwAGaPn16ufOPHz9ePXv2VFJSktq3b69rrrlGOTk5e7VM1CJWY1VeYGWsH9Un13sDK0ujbokp/vqedMUMadiVYQ+sCgqL9K9vl+joJ39wgVWDhFjdf1I/vXHxAQRWAAAAqHxwde211+r888/X4sWLXfDiY8ktvv/++5CWZX21bHnjxo1zNV8DBgzQ6NGjtXHjxoDz//e//9VNN93k5reU8C+++KJbxi233FLpZaKOSmomjbxRunqO9Jc39np8qrLMW5uhE//1ox7+bKHyCop0SM/m+uKag3XWAR0UTTNAAAAA7E2fK2tvaEFL165d3SDCs2bNUpcuXbRixQpXo1S6Fqk8Vqu0//776+mnn3aPi4qKXG3UlVde6YKo0q644goXVE2ePLl4mmUrnDZtmstgWJllBkKfqxq0dqb075EVz3fxZKnd4CorRm5BoZ75eon+9e1SFRR5lJoUp3HH9dFJ+7VVVBj7cAEAAKB2CyU2CPlSf0JCgnuD0hYtWqTmzZsHvRwbK2vGjBkaNWrU7sJER7vHP//8c8DXDBs2zL3G18xv2bJl+uSTT1ytWWWXaXJzc906+d9Qy0VXqrtgUGau2q7jnpqiJ79e4gKrMX1b6ctrD9bJA9sRWAEAAKBMIZ+hHn/88br77rv1v//9zz22q/grV67UjTfeqFNOOSXo5diAw4WFhS4Jhj97vGDBgoCvOeuss9zrRowY4cYYKigo0GWXXVbcLLAyyzQPPPCASyuPyJaTX6jHv1ykF35YpiKPlNYgXnefsI+O7te6posGAACAOiDkmqvHHnvMjW3VokUL7dy5UyNHjlS3bt1cE8H77rtPVenbb7/V/fffr3/961+uaaIl0Zg0aZLuueeevVquJeKwaj7fbdWqVWErM+qG6cu36qgnftC/v/cGVtb878trRhJYAQAAoOpqrqy94Zdffqkff/zR9beyQGvgwIElmuIFIy0tTTExMdqwYUOJ6fa4VatWAV9z++23669//asuvvhi97hfv37asWOHLr30Ut16662VWqavqaPdEHmycgv08GcL9NrPK9zjVo0Sdd9J++jw3iVrPwEAAICw11y99tprro/S8OHD9be//U033HCDC6ysv5M9F6z4+HgNGjSoRHIKSz5hj4cOHRrwNdnZ2a4PlT8Lpow1E6zMMlHL2ADBsRUEuva8zbeXfli8SaP/+X1xYHXmkPb64tqDCawAAABQPTVXF1xwgcaMGeOaBfrLzMx0z5177rlBL8tSpp933nkaPHiwhgwZ4sawspooW46xZdmgxNYnyhx33HF6/PHHtd9++7msgEuWLHG1WTbdF2RVtEzUco3be8er+vxWaf4HUq9jpINvKDmPBVY2XyWl78zXfZPm6X+/rnaP2zVJ0kOn9Nfwbml7W3oAAABEsJCDK6shCpSKevXq1a7JYCjOOOMMbdq0SXfccYfWr1+vfffdV5999llxQgpLlOFfU3Xbbbe597b/a9ascdkJLbDy7+tV0TJRBzRqK62a6r0/6AKpzb5hW/SX8zbo1vf+0MbMXNlufN7QTho7uqdSEqou+yAAAAAiQ9DjXFltkQU21s+qb9++io3dfTJqGfqWL1/uarR8WQTrMsa5qmErp0kvHSklNJLGLpVi4/d6kVuycnXXR/P04ay17nGX5il6+JT+GtypaRgKDAAAgPoqlNgg6Mv1J554ovs/c+ZMjR49Wg0aNCh+zvo6derUKaRU7ECZ5n/o/d9jzF4HVnbt4OPZ6zTuw7nauiNPMdFRuvTgLrrq8O5KjPM2JQUAAADCIejgaty4ce6/BVHW9C4xMTEsBQBKsIpUX3DV+7i92jgbMnJ02/tzXFNA06tVQz1y6gD1axda81UAAAAgGCF3NLFkEUCVWT9b2r5Sik2SuoWW3t+/turtGat1z8fzlJlToLiYKF1xaHddfkhXxceGnCATAAAAqJrgyvpX/fOf/3R9qyzhhKVg97d169ZQFwnsNm9XrVX3UVJ8cshbZvW2bN088Q/9sHizezygXaoePnWAerZqyFYGAABAlQr5Mv5dd93l0qFb00Dr1GWpz08++WSX1e/OO++smlIicsz/yPu/9/EhvayoyKP//PynG7fKAquE2GjdfFQvvXv5MAIrAAAA1M6aqzfeeEP/93//p2OOOcYFU2eeeaa6du2q/v37a+rUqfrHP/5RNSVF/bdpobR5oRQdJ/UYHfTLlm/eoRvfna3py721pvt3auLGrerSfHfSFQAAAKDWBVc2dlS/fv3cfcsYaLVX5thjj3UD+gKV5ktk0eUQKbHipBOFRR69NGW5Hv1ioXILipQcH6Mbx/TSXw/sqOjoPcdiAwAAAGpVcNWuXTutW7dOHTp0cDVWX3zxhQYOHKhffvlFCQkJVVNKRFiTwIqzBC7akKmx78zWrFXb3eMR3dL0wMn91L5p6P20AAAAgBoJrk466SRNnjxZBxxwgK688kqdc845evHFF11yi2uuuSYshUIE2rZCWjdLioqWeh1TXDNlTf02ZuaoRcNEDencVEUej577dqme/Hqx8gs9apgYq9uO6a3TB7d3g1wDAAAAdSa4evDBB4vvW1ILq8H6+eef1b17dx133N6NS4QI5qu16jhcSknTZ3PW6a6P5mldek7xLGkN4pUQG6M123e6x6N6t9C9J/ZTq1TGXAMAAEAdDK5KGzp0qLsB4WoSaIHV5a//Jk+pWTZnedP+p8TH6P6T++n4AW2orQIAAEDdCq4+/HBXooEgHH98aCm0AWWul1ZNcxuisOcxuuvZeXsEVv4aJMbq2P4EVgAAAKiDwdWJJ54Y1MKsz4sNMgyEZMHHkoVTbQdr+pakEk0BA9mQkev6Yg3t2owNDQAAgLoVXBUVFVV9SRC5/JoEWvKKYAQ7HwAAAFBdokOZ2ePxaPHixZo7d64KCgqqrlSIHNlbpeU/eO/3Ps5lBQxGsPMBAAAAtS64Wr58ufr3769evXq5/126dHFjWwF7ZeGnkqdQarmP1KyrS7feOjVRZSVVt+n2vM0HAAAA1MngauzYsa626vXXX9c777yj9u3b67LLLqva0iHiBg6OiY7SuOP6BJzVF3DZ8zYfAAAAUCdTsU+ZMsUFVSNGjHCPDzzwQLVr1047duxQSkpKVZYR9VVuprT0a+/93ruzTI7Zp7XuO2kf3fLenBKz23hWFljZ8wAAAECdDa42btzoBgr2ad26tZKSktz0zp07V1X5UJ8t/kIqzJWadpVa9C7xVFyMt1K1a/MU/ePw7q6PlTUFpMYKAAAAdT64sjTrWVlZLqDyiY6OVmZmpjIyMoqnNWrUKPylRP1vEhhVspnfj0s2u/9H7dNaJ+zbtiZKBwAAAFRNcGWZAnv06LHHtP3226/4PuNcIWj5OdKiL7z3+5QceNr2pSlLtrj7w7ulsVEBAABQv4Krb775pmpLgshifa3yd0iN2kptBpZ4auGGTG3OylVSXIwGdmxcY0UEAAAAqiS4GjlyZEgLBirbJHDKYm+TQOtjlRAbw4YEAABA/RtEGAiLwnxp4Sd7ZAks3d9qBE0CAQAAUIcQXKH6/fmDlLNdSk6TOhxY4qm8giJNW77V3ae/FQAAAOoSgivUXJPAXsdI0SWb/f2+cpuy8wqV1iBevVo15NMBAABAnUFwhepVVCjN/zhglkD/JoHDuqYpOrpkXywAAACgNiO4QvVaNV3asVFKSJU6HbzH01PobwUAAID6nC3w5JNPDnqBEydO3JvyIFKaBPYcI8XGl3gqIydfs1anu/vDuzO+FQAAAOqWoGquUlNTi2+NGjXS5MmT9euvvxY/P2PGDDfNngfK5PH4pWDfs0ng1KVbVFjkUZe0FLVtnMSGBAAAQP2ruXr55ZeL79944406/fTT9dxzzykmxpuMoLCwUH/7299c4AWUad1MKX2lFJcsdT2szP5WZAkEAABARPS5eumll3T99dcXB1bG7l977bXuOaBMvlqrbqOk+OQy+1sRXAEAACAigquCggItWLBgj+k2raioKFzlQn0OrvqcsMdT69J3aummHbIEgUO7Nqv+sgEAAADV0SzQ3wUXXKCLLrpIS5cu1ZAhQ9y0adOm6cEHH3TPAQFtXCBtXiTFxEvdj9zj6SmLvbVW/ds1VmpSHBsRAAAA9T+4evTRR9WqVSs99thjWrdunZvWunVrjR07Vtddd11VlBH1qdaqyyFSYqMy+1uN6EaWQAAAAERIcBUdHa0bbrjB3TIyMtw0ElmgQvM/LDNLoMfj0ZQlW9x9+lsBAAAgogYRtn5XX331ld58801FRUW5aWvXrlVWVla4y4f6YOtyaf1sKSpa6nn0Hk8v3JCpzVm5SoqL0cCOjWukiAAAAEC111ytWLFCY8aM0cqVK5Wbm6sjjjhCDRs21EMPPeQeW4p2oIQFH3v/dxwupTQrs7/VkM5NlRC7OwslAAAAUK9rrq666ioNHjxY27ZtU1LS7oFeTzrpJDeQMBBKlkBDfysAAABEZM3VDz/8oJ9++knx8fElpnfq1Elr1qwJZ9lQH2Ssk1ZN897vdcweT+cVFGna8q3uPv2tAAAAEFE1VzaWVWFh4R7TV69e7ZoHAgGbBLbbX2rUZo+N8/vKbcrOK1Rag3j1asX+AwAAgAgKro488kiNHz+++LEltLBEFuPGjdPRR++ZrAARztckMECWQP8mgcO6pinaRhAGAAAAIqVZoI1vNXr0aPXp00c5OTk666yztHjxYqWlpbnsgUCx7K3Sn1O893sfG3DDTGF8KwAAAERqcNWuXTvNmjVLEyZMcP+t1uqiiy7S2WefXSLBBaCFn0ieQqllP6lplz02SEZOvmatTnf3h3dn8GAAAABEWHDlXhQb64IpuwEVZwkM3CRw6tItKizyqEtaito2JjAHAABAhPW5iomJ0aGHHqqtW70Z3nw2bNjgngOcnAxp6dfe+72PK7e/FVkCAQAAEJHBlcfjcYMF21hXc+fO3eM5wFn8hVSYJzXrJjXvVW5/K4IrAAAARGRwZdkB3333XR133HEaOnSoPvjggxLPAXtkCQywX6xL36mlm3bIEgQO7dqMjQYAAIDIrLmy5n9PPPGEHn30UZ1xxhm69957qbXCbvk7pcVfltskcMpib61V/3aNlZoUx9YDAABAZCa08Ln00kvVvXt3nXbaafr+++/DVyrUbdbXKn+HlNpearNfuf2tRnQjSyAAAAAitOaqY8eOJRJXWHKLqVOnatWqVeEuG+p8k8DjAjYJtNrPKUu2uPv0twIAAEDE1lwtX758j2ndunXT77//7jIGIsIV5HnHtyqnSeCiDVnanJWrpLgYDezYuHrLBwAAANSWmquyJCYmulotRLg/f5By0qWU5lL7AwLO8sPiTe7/kM5NlRBL+n4AAABEUM1V06ZNtWjRIqWlpalJkyblZgUsPf4VIrRJYK9jpejAgRP9rQAAABCxwdU///lPNWzY0N0fP358VZcJdVVRobTg43KbBOYVFGnacm8ATn8rAAAARFxwdd555wW8D5Swapq0Y5OUmCp1Oijgxvl95TZl5xWqWUq8erXyBuwAAABAxARXGRkZQS+wUaNGe1Me1IcmgT2PlmLjy20SaLVW0TaCMAAAABBJwVXjxo3L7WflS69t8xQWFoarbKhLPJ6SKdjLMIXxrQAAABDJwdU333xT9SVB3bb2dyl9lRSXInU9LOAsGTn5mrU63d0f3p3BgwEAABCBwdXIkSOrviSo23y1Vt2PkOKSAs4ydekWFRZ51DktRW0bB54HAAAAiJhBhH2ys7O1cuVK5eXllZjev3//cJQLda5J4IcVNgkkBTsAAADqs5CDq02bNumCCy7Qp59+GvB5+lxFoE0LpC1LpJh4qfuRFfa3IgU7AAAA6qPoUF9w9dVXa/v27Zo2bZqSkpL02Wef6dVXX1X37t314Ye7ai8QmU0Cra9VYuBskevSd2rpph2yBIFDuzSr3vIBAAAAtbHm6uuvv9YHH3ygwYMHKzo6Wh07dtQRRxzhUrA/8MADOuaYY6qmpKi95lXcJHDKYm+tVb92jZWaHFddJQMAAABqb83Vjh071KJFC3e/SZMmrpmg6devn3777bfwlxC129Zl0oY/pKgY7/hWFfS3OqgbWQIBAABQP4UcXPXs2VMLFy509wcMGKDnn39ea9as0XPPPafWrVtXRRlRm83/2Pu/0wgpuWmZY6BNWbLF3ae/FQAAAOqrkJsFXnXVVVq3bp27P27cOI0ZM0ZvvPGG4uPj9corr1RFGVGbBZElcNGGLG3OylVSXIwGdmxcfWUDAAAAanNwdc455xTfHzRokFasWKEFCxaoQ4cOSkujyVdEyVgrrf7Fe7/XsWXO9sNib9PRIZ2bKiE2prpKBwAAANTuZoE+Nr6VNQ+0GquBAwcSWEWiBZO8/9sfIDUqu0ko41sBAAAgEkRXZvDgiy66SMnJyerbt68bSNhceeWVevDBB6uijKit5n1QYZPAvIIiTVu+1d2nvxUAAADqs5CDq5tvvlmzZs3St99+q8TExOLpo0aN0oQJE8JdPtRWO7ZIK36sMLiauWq7svMK1SwlXr1aNay+8gEAAAC1vc/V+++/74KoAw88UFFRUcXTrRZr6dKl4S4faquFn0ieIqlVf6lJpzJnm7Krv5XVWkXbCMIAAABAPRVyzZWNa+Ub56r0+Ff+wRYiJUvg8eXONmXX+FYjGN8KAAAA9VzIwdXgwYM1adKuRAZScUD1wgsvaOjQoeEtHWqnnHRp2bcVNgnMyMnXrNXp7v7w7mSSBAAAQP0WcrPA+++/X0cddZTmzZungoICPfHEE+7+Tz/9pO+++65qSonaZfGXUmGelNZDatGrzNmmLduqwiKPOqelqG3jpGotIgAAAFDra65GjBihmTNnusCqX79++uKLL1wzwZ9//tmNe4UIEESWQP/+VjQJBAAAQCQIuebKdO3aVf/3f/+3x/R33nlHp556ajjKhdoqL1ta8lVwwdWu/lakYAcAAEAkCKnmymqr5syZo0WLFpWY/sEHH2jAgAE6++yzw10+1DZLv5bys6XUDlLrfcucbV36Ti3dtEOWIHBol2bVWkQAAACgVgdXFlR169bNBVG9e/fWySefrA0bNmjkyJG68MILXT8sUrFHUpbA4yybSZmz/bhki/vfr11jpSbHVVfpAAAAgNrfLPDGG290wdXTTz+tN998093mz5+viy66SJ999pmSkkhYUO8V5EkLPwupv9VBpGAHAABAhAg6uPrll19c8op9991XBx10kAuubrnlFv31r3+t2hKi9vjzeyk3XUppIbUfUuZsHo9HU3bVXNHfCgAAAJEi6GaBmzdvVps2bdz91NRUpaSk6MADD6zKsqG2medrEnisFB1T5myLNmRpc1auEuOiNbBj4+orHwAAAFAXaq5ssODMzEwlJia6mgl7vHPnTmVkZJSYr1GjRlVRTtS0okJpwaSQsgQO6dxMCbFlB2EAAABARNZcWUDVo0cPNWnSRE2bNlVWVpb2228/99hujRs3dv8r45lnnlGnTp1c4HbAAQdo+vTpZc57yCGHuMCu9O2YY44pnuf888/f4/kxY8ZUqmzYZeVUKXuzlNhY6nRQuZuF/lYAAACIREHXXH3zzTdVUoAJEybo2muv1XPPPecCq/Hjx2v06NFauHChG5y4tIkTJyovL6/48ZYtW1wGw9NOO63EfBZMvfzyy8WPExISqqT8EZclsOfRUkzZ2f/yCoo0bflWd5/+VgAAAIgkQQdXlnK9Kjz++OO65JJLdMEFF7jHFmRNmjRJL730km666aY95rdaM39vvfWWkpOT9wiuLJhq1apVlZQ54ng80vyPgmoSOHPVdmXnFapZSrx6tWpYPeUDAAAA6togwuFmNVAzZszQqFGjdhcoOto9/vnnn4Naxosvvqi//OUvLsGGv2+//dbVfPXs2VOXX365q+EqS25urus75n+Dn7W/SRlrpLgUqeuhQfW3GtYtTdE2gjAAAAAQIWo0uLIMhIWFhWrZsmWJ6fZ4/fr1Fb7e+mbZ4MYXX3zxHk0CX3vtNU2ePFkPPfSQvvvuOzfIsb1XIA888IDLgOi7tW/ffi/XrJ5mCexxpBRX/nhm9LcCAABApAq6WWBtZLVW/fr105AhJcdcsposH3u+f//+6tq1q6vNOvzww/dYzs033+z6fflYzRUBln+TwA+DahKYkZOvWavT3f3h3dP24pMFAAAA6p4arblKS0tTTEyMNmzYUGK6Pa6ov9SOHTtcf6uLLrqowvfp0qWLe68lS5YEfN76Z1kKef8bdtk4X9q6TIpJkLofWe5mmbZsqwqLPOqclqK2jcuv4QIAAADqmxoNruLj4zVo0CDXfM+nqKjIPR46dGi5r3377bddX6lzzjmnwvdZvXq163PVunXrsJQ7ovhqrboeJiWUn6Dix139rYZ3a1YdJQMAAADqXrPAk08+OegFWqr0UFhzvPPOO0+DBw92zfssFbvVSvmyB5577rlq27at6xdVukngiSeeqGbNSp7I2/hbd911l0455RRX+7V06VLdcMMN6tatm0vxjhAFmSXQ/LB4k/s/oltzNjMAAAAiTlDBlSV58B9M+L333nPTLCAylvFv+/btIQVhPmeccYY2bdqkO+64wyWx2HffffXZZ58VJ7lYuXKlyyDoz8bAmjJlir744os9lmfNDGfPnq1XX33VlalNmzY68sgjdc899zDWVai2LJU2zJGiYqSeR5U767r0nVq6aYcsQeDQLtRcAQAAIPJEeSxaCsGNN96orVu3uvGoLJAxloXvb3/7m+ur9Mgjj6ius4QWFjymp6dHdv+rKeOlr8ZJXQ6Rzv2g3FnfmbFa1789SwPaN9YHfx9ebUUEAAAAaktsEHKfKxvc9/rrry8OrIzdt+Z99hwis0mgLwX7CPpbAQAAIEKFHFwVFBRowYIFe0y3aZaMAvVE+hppza9WuSn1OrbcWa3yc8oS7yDN9LcCAABApAp5nCtLNGHpzy1RhG98qWnTpunBBx8sTkKBemDBx97/7Q+QGpafFn/RhixtzspVYly0BnZsXD3lAwAAAOp6cPXoo4+6LHyPPfaY1q1b56ZZivOxY8fquuuuq4oyorY3CdyVgn1I52ZKiN3dXBQAAACIJCEHV5a5z1Kb2806d5mITvpQH+3YLK340Xu/d/lNAv37Wx3ULa2qSwYAAADUr0GErd/VV199pTfffFNRUVFu2tq1a90YU6gHFkySPEVS6wFSk07lzppXUKRpy7e6+8MJrgAAABDBQq65WrFihcaMGePGn8rNzdURRxyhhg0b6qGHHnKPLUU7IqdJ4MxV25WdV6hmKfHq1aph1ZcNAAAAqC81V1dddZUbPHjbtm1KSkoqnn7SSSdp8uTJ4S4fqltOurTsW+/93scH3d9qWLc0RdsIwgAAAECECrnm6ocfftBPP/2k+Pj4EtM7deqkNWvWhLNsqAmLPpeK8qW0nlLznhXOTn8rAAAAoJI1VzaWVWFh4R7TV69e7ZoHoo6b/2HQTQIzcvI1a3W6uz+8O8ksAAAAENlCDq6OPPJIjR8/vvixJbSwRBbjxo3T0UcfHe7yoTrlZUuLvwo6uJq2bKsKizzqnJaito13NxEFAAAAIlHIzQJtfKvRo0erT58+ysnJ0VlnnaXFixcrLS3NZQ9EHbbkK6lgp9S4gzdTYAV+3NXfani3ZtVQOAAAAKCeBVft2rXTrFmzNGHCBPffaq0uuuginX322SUSXKAuZwk83qokK5z9h13jW43o1ryqSwYAAADUv+DKvSg21gVTdkM9UZAnLfos6CaB69J3aummHbIEgUO7UHMFAAAAhNznKiYmRoceeqi2bvUOHOuzYcMG9xzqqOXfSbkZUoOWUrshFc7+45It7n+/do2VmhxXDQUEAAAA6llw5fF43GDBNtbV3Llz93gOdTxLYK9jpejooPtbjaC/FQAAAFC54MqyA7777rs67rjjNHToUH3wwQclnkMdVFQoLZgUdJNAC6J9gwfT3woAAADYi5ora/73xBNP6NFHH9UZZ5yhe++9l1qrumzFT1L2FimpidRpRIWzL9qQpU2ZuUqMi9bAjo2rpYgAAABAvUxo4XPppZeqe/fuOu200/T999+Hr1SomSyBPY+WYiruP+WrtRrSuZkSYulnBwAAAFSq5qpjx44lEldYcoupU6dq1apVbNG6qKjILwV7xU0CDf2tAAAAgDDUXC1fvnyPad26ddPvv//uMgaijln7m5S5VopvIHU5tMLZ8wqKNHWZN1Mg/a0AAACAvai5KktiYqKr1UIdzRLY/UgpLrHC2Weu2q7svEI1S4lXr1YNq758AAAAQH2quWratKkWLVqktLQ0NWnSpNysgKXHv0ItZqnzQ2wS6OtvNaxbmqJtBGEAAAAAwQdX//znP9WwobeWYvz48cG8BHXBhrnS1mVSTIK35ioI9LcCAAAA9iK4Ou+88wLeRx3nq7XqdriU0KDC2TNy8l2zQDOie/OqLh0AAABQ/4KrjIyMoBfYqFGjvSkPqlOITQKnLduqwiKPOqelqG3jpKotGwAAAFAfg6vGjRuX28/KN7iwzVNYWBiusqEqbVkqbZwrRcdKPcaE1CRweLdmfDYAAABAZYKrb775JpjZUBezBHY6SEpuGlIyixHd0qqyZAAAAED9Da5GjhxZ9SVBrW4SuC59p5ZszJIlCBzaheAKAAAA2OtBhH2ys7O1cuVK5eXllZjev3//yi4S1SV9tbRmhqQoqdexQb3kxyXegYP7tWus1OS4Ki4gAAAAEAHB1aZNm3TBBRfo008/Dfg8fa7qgPkfe/93OFBq2DKol5CCHQAAAChftEJ09dVXa/v27Zo2bZqSkpL02Wef6dVXX1X37t314Ye7+vGgXjUJtGQlvv5Ww+lvBQAAAISn5urrr7/WBx98oMGDBys6OlodO3bUEUcc4VKwP/DAAzrmmGNCXSSqU9YmaeVPIQVXizZkaVNmrhLjojWoY5OqLR8AAAAQKTVXO3bsUIsWLdz9Jk2auGaCpl+/fvrtt9/CX0KE18JJkqdIar2v1LhDUC/x1VoN6dxMCbExfCIAAABAOIKrnj17auHChe7+gAED9Pzzz2vNmjV67rnn1Lp161AXh1reJNDQ3woAAACogmaBV111ldatW+fujxs3TmPGjNEbb7yh+Ph4vfLKK6EuDtVp53Zp2Xfe+31OCOol+YVFmrrMmylwRLfmVVk6AAAAILKCq3POOaf4/qBBg7RixQotWLBAHTp0UFoa4x/Vaos+l4rypea9pLTuQb3k95XblZ1XqGYp8erVqmGVFxEAAACIuHGufJKTkzVw4MDwlAZVa/6HITcJ9PW3GtYtTdE2gjAAAACA8ARXlpb7nXfe0TfffKONGzeqqKioxPMTJ04MdZGoDnk7pCWTvfd7Hx/0y+hvBQAAAFRRcGXjXFkSi0MPPVQtW7ZUVBS1GXXCkq+kgp1S445Sq35BvSQjJ18zV21390d0p78VAAAAENbg6j//+Y+rnTr66KNDfSlqS5bAIAPiacu2qrDIo85pKWrbOKlqywcAAABEWir21NRUdenSpWpKg6pRkOtNZhFClkD/JoHDuzXjkwEAAADCHVzdeeeduuuuu7Rz585QX4qaYunXczOkBq2ktoNDTmYxohtZIAEAAICwNws8/fTT9eabb6pFixbq1KmT4uLiSjz/22+/hbpIVFuWwGOl6ODi6XXpO7VkY5YsQeDQLgRXAAAAQNiDq/POO08zZsxw412R0KIOKCyQFn5SiSyB3oGD+7VrrNTkkgE0AAAAgDAEV5MmTdLnn3+uESNGhPpS1ISVP0nZW6SkJlLH4UG/jBTsAAAAQBX3uWrfvr0aNWoU6stQ01kCex4jxcQGPZaZr7/VcPpbAQAAAFUTXD322GO64YYb9Oeff4b6UlQ3G+B5/sfe+32CbxK4aEOWNmXmKjEuWoM6Nqm68gEAAACR3CzQ+lplZ2era9euSk5O3iOhxdatW8NZPuyNNTOkzLVSfEOp88igX+artRrSuZkSYmP4DAAAAICqCK7Gjx8f6ktQ01kCexwpxSUG/TL6WwEAAABVHFzl5+fru+++0+23367OnTtX4u1QbTye3f2tQsgSmF9YpKnLvJkC6W8FAAAAVFGfK2sC+O6774byEtSUDXOkbcul2ESp26igX/b7yu3KzitUs5R49W5F4hIAAACgyhJanHjiiXr//fdDfRmqm6/WquvhUkKDkPtbDeuWpmgbQRgAAABA1fS56t69u+6++279+OOPGjRokFJSUko8/49//CPURaIqg6sQsgQa+lsBAAAA1RRcvfjii2rcuLFmzJjhbv6ioqIIrmqDzUukjfOk6Fipx+igX5aZk6+Zq7a7+/S3AgAAAKo4uFq+fHmoL0FNZQnsfLCUFPw4VVOXbVVhkUed01LUrkly1ZUPAAAAqIdC7nPlz+PxuBtqmUpkCfRvEji8W7OqKBUAAABQr1UquHrttdfUr18/JSUluVv//v31n//8J/ylQ+i2r5LW/maNNKVex4T0Ul8yixHd0tjyAAAAQFU3C3z88cfdOFdXXHGFhg8f7qZNmTJFl112mTZv3qxrrrkm1EUinBZ87P3fYajUoEXQL1ufnqMlG7NkCQKHdiG4AgAAAKo8uHrqqaf07LPP6txzzy2edvzxx6tv37668847Ca7qaJZAX61Vv3aNlZocVxUlAwAAAOq1kJsFrlu3TsOGDdtjuk2z51CDsjZKK37y3u91bEgvJQU7AAAAUM3BVbdu3fS///1vj+kTJkxwY2ChBi2YZGlGpDb7SY3bB/0yS0riq7kiBTsAAABQTc0C77rrLp1xxhn6/vvvi/tc2YDCkydPDhh0ofZnCVy8MUubMnOVGBetQR2DT90OAAAAYC9qrk455RRNmzZNaWlpev/9993N7k+fPl0nnXRSqItDuOzcJi3/rlLB1Q+LvbVWQzo3U0JsDJ8JAAAAUB01V2bQoEF6/fXXK/NSVJVFn0tFBVLz3lJat5BeSn8rAAAAoIYHEUbdzxKYX1ikqcu2uPv0twIAAACqoeYqOjpaUVFR5c5jzxcUFOxFcVApuVnSkq+893sfF9JLZ67aruy8QjVLiVfvVo34AAAAAICqDq7ee++9Mp/7+eef9eSTT6qoqKiy5cDesMCqIEdq0klquU+l+lsN65amaBtBGAAAAEDVBlcnnHDCHtMWLlyom266SR999JHOPvts3X333ZUrBcKXJbCC2sXS6G8FAAAA1GCfq7Vr1+qSSy5Rv379XDPAmTNn6tVXX1XHjh3DVCwErSDXm8yiElkCM3PyXbNAQ38rAAAAoBqDq/T0dN14441uIOG5c+e6sa2s1mqffUJrioYwWvatlJcpNWwttR0U0kunLduqwiKPOqelqF2TZD4WAAAAoDqaBT788MN66KGH1KpVK7355psBmwmiBsz/cHcii+jQKiKnLPH2txrerVlVlAwAAACIKEEHV9a3KikpydVaWRNAuwUyceLEcJYP5SkskBZ8Uqksgf7B1YhuaWxnAAAAoLqCq3PPPbfCVOyoZit+lHZulZKaSh2GhfTS9ek5WrIxS5YgcGgXgisAAACg2oKrV155Za/fDFWUJbDXMVJM0B9liSyB/do1VmpyHB8NAAAAUBPZAlEL2Jhi/inYK90kkP5WAAAAQDgQXNVVa36VstZLCY2kLiNDeqnH4/FLZkGTQAAAACAcCK7qepbAHqOl2ISQXrp4Y5Y2ZeYqMS5agzo2qZryAQAAABGG4Kou8nikeX4p2EP0w2JvrdWQzs2UEBsT7tIBAAAAEYngqi5a/4e0fYUUmyR1GxXyy33JLOhvBQAAAIQPwVVd5Etk0e1wKT4lpJfmFxZp6rIt7j79rQAAAIDwIbiqy/2tKpElcOaq7crOK1SzlHj1btUo/GUDAAAAIhTBVV2zaZG0aYEUHedNZlHJ/lbDuqUp2kYQBgAAABAWBFd1zYJdTQIt/XpS45BfTn8rAAAAoB4HV88884w6deqkxMREHXDAAZo+fXqZ8x5yyCGKiora43bMMceUGMfpjjvuUOvWrZWUlKRRo0Zp8eLFqhf2IktgZk6+axZo6G8FAAAA1LPgasKECbr22ms1btw4/fbbbxowYIBGjx6tjRs3Bpx/4sSJWrduXfFtzpw5iomJ0WmnnVY8z8MPP6wnn3xSzz33nKZNm6aUlBS3zJycHNVp21dK62ZKUdFSz93BZLCmLduqwiKPOjVLVrsmyVVSRAAAACBS1Xhw9fjjj+uSSy7RBRdcoD59+riAKDk5WS+99FLA+Zs2bapWrVoV37788ks3vy+4slqr8ePH67bbbtMJJ5yg/v3767XXXtPatWv1/vvvq06b/7H3f4dhUoPmIb98ii8Fe/e0cJcMAAAAiHg1Glzl5eVpxowZrtmeT3R0tHv8888/B7WMF198UX/5y19c7ZRZvny51q9fX2KZqamprrlhWcvMzc1VRkZGiVvtzhIYepPAEsFVN4IrAAAAoF4FV5s3b1ZhYaFatmxZYro9tgCpItY3y5oFXnzxxcXTfK8LZZkPPPCAC8B8t/bt26vWydwgrZzqvd/72JBfvj49R0s2ZikqShraheAKAAAAqHfNAveG1Vr169dPQ4YM2avl3HzzzUpPTy++rVq1SrXOwknW6FFqO0hKbVfpLIH926YqNTmuCgoIAAAARLYaDa7S0tJcMooNGzaUmG6PrT9VeXbs2KG33npLF110UYnpvteFssyEhAQ1atSoxK0+ZQk09LcCAAAA6nFwFR8fr0GDBmny5MnF04qKitzjoUOHlvvat99+2/WVOuecc0pM79y5swui/Jdpfagsa2BFy6y1srdKf/7gvd/7+JBfbkk+fMEVKdgBAACAqhGrGmZp2M877zwNHjzYNe+zTH9WK2XZA825556rtm3bun5RpZsEnnjiiWrWrFmJ6Tbm1dVXX617771X3bt3d8HW7bffrjZt2rj564Ttq6TsLbsfL/pcKiqQmnSRcjO9zzcOvl/Y4o1Z2pSZq8S4aA3s0KRqygwAAABEuBoPrs444wxt2rTJDfprCSf23XdfffbZZ8UJKVauXOkyCPpbuHChpkyZoi+++CLgMm+44QYXoF166aXavn27RowY4ZZpgxTXehY4PT1IKsjd87lty6R/j5RiE6QrZgQdYE1Z7K212r9TUyXGxYS7xAAAAACsosdjbcZQgjUjtKyBltyi2vtfrZ3pDaAqcul3Upt9g1rkha/8oq8XbNQtR/fSpQd33fsyAgAAABEiI4TYoE5nC0TF8guLNHWZt4kh/a0AAACAqkNwVc/NXLVd2XmFapYSr96tamEWRAAAAKCeILiq53z9rYZ1S1N0dFRNFwcAAACotwiu6rni8a26lcyqCAAAACC8CK7qscycfNcs0NDfCgAAAKhaBFf12LRlW1VY5FGnZslq1yS5posDAAAA1GsEV7VNcjPvOFblsedtvmCbBHZPC1fpAAAAANTWQYRRig0MbAMEZ3vTpwdkgVUQAwjv7m9FcAUAAABUNYKr2sgCpyCCp/KsT8/Rko1ZioqShnYhuAIAAACqGs0C66kfd9Va9W+bqtTkuJouDgAAAFDvEVzV8+CK/lYAAABA9SC4qoc8Hk9xfytSsAMAAADVg+CqHlq8MUsbM3OVGBetgR2a1HRxAAAAgIhAcFUPTVnsrbXav1NTJcbF1HRxAAAAgIhAcFWP+1sdxPhWAAAAQLUhuKpn8guLNHWZd4ws+lsBAAAA1Yfgqp6ZuWq7duQVqmlKvHq3alTTxQEAAAAiBsFVPe1vNaxrM0VHR9V0cQAAAICIQXBVz9DfCgAAAKgZBFf1SGZOvn5ftd3dp78VAAAAUL0IruqRacu2qrDIo07NktWuSXJNFwcAAACIKARX9ciUXSnYqbUCAAAAqh/BVT0MrhjfCgAAAKh+BFf1xPr0HC3ZmKWoKGlol7SaLg4AAAAQcQiu6lmWwP5tU5WaHFfTxQEAAAAiDsFVPQuu6G8FAAAA1AyCq3rA4/EU97ca0Z0mgQAAAEBNILiqBxZvzNLGzFwlxkVrYIcmNV0cAAAAICIRXNUDUxZ7a63279RUiXExNV0cAAAAICIRXNWj/lYjutEkEAAAAKgpBFd1XH5hkaYu2+Lu098KAAAAqDkEV3XczFXbtSOvUE1T4tW7VaOaLg4AAAAQsQiu6kl/q2Fdmyk6OqqmiwMAAABELIKretLf6iBSsAMAAAA1iuCqDsvMydfvq7a7+wweDAAAANQsgqs6bNqyrSos8qhTs2S1a5Jc08UBAAAAIhrBVR02ZVeTQGqtAAAAgJpHcFWH0d8KAAAAqD0Iruqo9ek5WrwxS1FR0tAuDB4MAAAA1DSCqzpea9W/bapSk+NqujgAAABAxCO4quPBFf2tAAAAgNqB4KoO8ng8xcksRjC+FQAAAFArEFzVQdbXamNmrhLjojWwQ5OaLg4AAAAAgqu6acpib63V/p2aKjEupqaLAwAAAIDgqm73txrRjSyBAAAAQG1Bs8A6Jr+wSFOXbXH36W8FAAAA1B4EV3XMzFXbtSOvUE1T4tW7VaOaLg4AAACAXQiu6mh/q2Fdmyk6OqqmiwMAAABgF4KrOob+VgAAAEDtRHBVh2Tm5Ov3VdvdffpbAQAAALULwVUdMm3ZVhUWedSpWbLaNUmu6eIAAAAA8ENwVYdM2ZWCfTgp2AEAAIBah+CqDqG/FQAAAFB7EVzVERsycrR4Y5aioixTIIMHAwAAALUNwVUdS8Hev22qUpPjaro4AAAAAEohuKpjTQLpbwUAAADUTgRXdYDH4ylOZjGCZBYAAABArURwVQcs2ZiljZm5SoyL1sCOTWq6OAAAAAACILiqA37Y1d9q/05NlRgXU9PFAQAAABBAbKCJqB1swODpy7fq3d9Wu8fDujar6SIBAAAAKAPBVS312Zx1uuujeVqXnlM87cUpy9U5LUVj9mldo2UDAAAAsCeaBdbSwOry138rEViZLVl5bro9DwAAAKB2IbiqhU0BrcbKE+A53zR73uYDAAAAUHsQXNUy1seqdI2VPwup7HmbDwAAAEDtQXBVy2zMzAnrfAAAAACqB8FVLdOiYWJY5wMAAABQPQiuapkhnZuqdWqiosp43qbb8zYfAAAAgNqD4KqWiYmO0rjj+rj7pQMs32N73uYDAAAAUHsQXNVCNo7Vs+cMVKvUkk3/7LFNZ5wrAAAAoPZhEOFaygKoI/q0clkBLXmF9bGypoDUWAEAAAC1E8FVLWaB1NCuzWq6GAAAAACCQLNAAAAAAAgDgisAAAAACAOCKwAAAAAIA4IrAAAAAAgDgisAAAAACAOCKwAAAAAIA4IrAAAAAAgDgisAAAAACAOCKwAAAAAIA4IrAAAAAAgDgisAAAAACAOCKwAAAAAIA4IrAAAAAAiD2HAspL7xeDzuf0ZGRk0XBQAAAEAN8sUEvhihPARXAWRmZrr/7du3D/dnAwAAAKCOxgipqanlzhPlCSYEizBFRUVau3atGjZsqKioqGqJhi2QW7VqlRo1alTl74f6gf0G7DvgmIPajt8q1Id9x8IlC6zatGmj6Ojye1VRcxWAbbR27dqputmOU9M7D+oe9huw74BjDmo7fqtQ1/edimqsfEhoAQAAAABhQHAFAAAAAGFAcFULJCQkaNy4ce4/wH4DjjmojfitAvsNOOZUjIQWAAAAABAG1FwBAAAAQBgQXAEAAABAGBBcAQAAAEAYEFwBAAAAQBgQXNUCzzzzjDp16qTExEQdcMABmj59ek0XCTXo+++/13HHHedGAY+KitL777+/xyjhd9xxh1q3bq2kpCSNGjVKixcvLjHP1q1bdfbZZ7tB9xo3bqyLLrpIWVlZ1bwmqE4PPPCA9t9/fzVs2FAtWrTQiSeeqIULF5aYJycnR3//+9/VrFkzNWjQQKeccoo2bNhQYp6VK1fqmGOOUXJyslvO2LFjVVBQwIdZTz377LPq379/8SCdQ4cO1aefflr8PPsMgvHggw+636urr76afQfluvPOO92+4n/r1atXvTrmEFzVsAkTJujaa691qdh/++03DRgwQKNHj9bGjRtrumioITt27HD7gQXdgTz88MN68skn9dxzz2natGlKSUlx+4wdkHwssJo7d66+/PJLffzxxy5gu/TSS6txLVDdvvvuO/eDNHXqVPe55+fn68gjj3T7k88111yjjz76SG+//babf+3atTr55JOLny8sLHQ/WHl5efrpp5/06quv6pVXXnHBPOqndu3auRPjGTNm6Ndff9Vhhx2mE044wR0/DPsMKvLLL7/o+eefd0G6P/YdlKVv375at25d8W3KlCn1a7/xoEYNGTLE8/e//734cWFhoadNmzaeBx54oEbLhdrBvqLvvfde8eOioiJPq1atPI888kjxtO3bt3sSEhI8b775pns8b94897pffvmleJ5PP/3UExUV5VmzZk01rwFqysaNG91+8N133xXvJ3FxcZ633367eJ758+e7eX7++Wf3+JNPPvFER0d71q9fXzzPs88+62nUqJEnNze3BtYCNaFJkyaeF154gX0GFcrMzPR0797d8+WXX3pGjhzpueqqq9x0jjcoy7hx4zwDBgwI+Fx92W+ouapBFnXb1UJr1uUTHR3tHv/88881WTTUUsuXL9f69etL7DOpqamuOalvn7H/1hRw8ODBxfPY/LZvWU0XIkN6err737RpU/ffjjVWm+W/71hTjA4dOpTYd/r166eWLVsWz2O1ohkZGcU1Gai/7IrwW2+95Wo7rXkg+wwqYrXlVovgf1wx7Dsoj3VlsK4PXbp0cS1trJlffdpvYmu6AJFs8+bN7sfMfwcx9njBggU1Vi7UXhZYmUD7jO85+29tkP3Fxsa6k2zfPKjfioqKXN+H4cOHa5999nHT7LOPj493gXd5+06gfcv3HOqnP/74wwVT1rTY+ji899576tOnj2bOnMk+gzJZIG7dGaxZYGkcb1AWuxhszfh69uzpmgTeddddOuiggzRnzpx6s98QXAFAPbyabD9U/u3YgbLYSY4FUlbb+c477+i8885zfR2AsqxatUpXXXWV699pybiAYB111FHF962fngVbHTt21P/+9z+XpKs+oFlgDUpLS1NMTMweWVDscatWrWqsXKi9fPtFefuM/S+dEMWy6FgGQfar+u+KK65wSUy++eYbl6zAxz57a4q8ffv2cvedQPuW7znUT3aluFu3bho0aJDLOmkJdZ544gn2GZTJmm/Z78zAgQNdywi7WUBuyZbsvtUkcLxBMKyWqkePHlqyZEm9OeYQXNXwD5r9mE2ePLlEcx57bE00gNI6d+7sDh7++4y1M7a+VL59xv7bgcl+/Hy+/vprt2/ZFSLUT5b/xAIra9Jln7ftK/7sWBMXF1di37FU7dbW3X/fsSZi/sG5XZm2FN3WTAyRwY4Vubm57DMo0+GHH+6OFVbj6btZP1/rP+O7z/EGwbBhYpYuXeqGl6k3v1M1nVEj0r311lsu09srr7zisrxdeumlnsaNG5fIgoLIy770+++/u5t9RR9//HF3f8WKFe75Bx980O0jH3zwgWf27NmeE044wdO5c2fPzp07i5cxZswYz3777eeZNm2aZ8qUKS6b05lnnlmDa4Wqdvnll3tSU1M93377rWfdunXFt+zs7OJ5LrvsMk+HDh08X3/9tefXX3/1DB061N18CgoKPPvss4/nyCOP9MycOdPz2WefeZo3b+65+eab+QDrqZtuuslllFy+fLk7nthjyyz6xRdfuOfZZxAs/2yB7Dsoy3XXXed+p+yY8+OPP3pGjRrlSUtLcxlu68sxh+CqFnjqqafcjhQfH+9Ss0+dOrWmi4Qa9M0337igqvTtvPPOK07Hfvvtt3tatmzpAvPDDz/cs3DhwhLL2LJliwumGjRo4NKTXnDBBS5oQ/0VaJ+x28svv1w8jwXgf/vb31yq7eTkZM9JJ53kAjB/f/75p+eoo47yJCUluR88+yHMz8+vgTVCdbjwwgs9HTt2dL8/doJixxNfYGXYZ1DZ4Ip9B4GcccYZntatW7tjTtu2bd3jJUuW1Kv9Jsr+1HTtGQAAAADUdfS5AgAAAIAwILgCAAAAgDAguAIAAACAMCC4AgAAAIAwILgCAAAAgDAguAIAAACAMCC4AgAAAIAwILgCAAAAgDAguAIA1EuHHHKIrr766lqznL115513at99963pYgAAykFwBQAIq/PPP19RUVHuFh8fr27duunuu+9WQUFBrd7S3377rSvz9u3bS0yfOHGi7rnnnip///fee08HHnigUlNT1bBhQ/Xt27dEUHf99ddr8uTJVV4OAEDlxe7FawEACGjMmDF6+eWXlZubq08++UR///vfFRcXp5tvvrnObbGmTZtW+XtY0HTGGWfovvvu0/HHH++CvHnz5unLL78snqdBgwbuBgCovai5AgCEXUJCglq1aqWOHTvq8ssv16hRo/Thhx+657Zt26Zzzz1XTZo0UXJyso466igtXry4+LWvvPKKGjdurPfff1/du3dXYmKiRo8erVWrVpWoHTvxxBNLvKfV8lgTvrL85z//0eDBg12tkJXtrLPO0saNG91zf/75pw499FB338plwY29R6BmgcGW//PPP1fv3r1dQGTB5rp168os20cffaThw4dr7Nix6tmzp3r06OHW75lnnimzWaCvdtD/1qlTp+Ln58yZ48pm79+yZUv99a9/1ebNm8v51AAAe4vgCgBQ5ZKSkpSXl+fuW9Dy66+/umDr559/lsfj0dFHH638/Pzi+bOzs10tzmuvvaYff/zRNdX7y1/+sldlsOVb875Zs2a5wM0CKl8A1b59e7377rvu/sKFC10g9MQTTwRcTrDlf/TRR11A9/3332vlypWuWV9ZLNibO3euC4iCZWX03ZYsWeKaXx588MHuOdtehx12mPbbbz9X1s8++0wbNmzQ6aefHvTyAQCho1kgAKDKWOBhTd6sFufKK690NTwWlFjANGzYMDfPG2+84YIbC3hOO+00N80ClaeffloHHHCAe/zqq6+6WqDp06dryJAhlSrLhRdeWHy/S5cuevLJJ7X//vsrKyvL1e74mv+1aNHC1TwFEkr5n3vuOXXt2tU9vuKKK1y/s7LYtvnhhx/Ur18/V9tnfa+OPPJInX322a4WsKyAzLeNTznlFNdX6/nnn3fTbNtZYHX//fcXz//SSy+5ci5atMjVjAEAwo+aKwBA2H388ccuYLEmfdY0zfoTWbO2+fPnKzY2tjhoMs2aNXNN4ew5H5vHAh+fXr16uYDHf55QzZgxQ8cdd5w6dOjgmgaOHDnSTbdapWAFW35rLugLrEzr1q2LmyAGkpKSokmTJrkaqNtuu81tu+uuu84FklYLVp5bbrnF1aB98MEHrobQWO3cN998U9xPy262Dc3SpUuDXl8AQGiouQIAhJ31X3r22WddtsA2bdq4gCScoqOjXY2NP/9meaXt2LHD9duym9U0NW/e3AVV9tjXXDGcLHmHP+sPVbq8gVhAZreLL75Yt956q6thmjBhgi644IKA87/++uv65z//6TIdtm3btni61cZZIPnQQw/t8RoL9AAAVYOaKwBA2FlNjPUBsloi/8DKmvZZSvZp06YVT9uyZYvr59SnT5/iaTaP9RXyseetH5G93lhwVDpBxMyZM8ssz4IFC9z7PPjggzrooINcLU7pmiQLBE1hYWGZywm2/OFgySmsBswCw0CstsqCMGsKaM0I/Q0cOND14bJl2Ofgf7PPBgBQNQiuAADVxrL/nXDCCbrkkks0ZcoU13ztnHPOcbUuNt2/5sf6IVkQY835LImEBRC+/laWrMGCL0t4Yf2gxo0bV24yCAvyLHh66qmntGzZMtdvqvTYVdbXyWqYrEnjpk2bXO1PZcsfKmsyecMNN7gaqOXLl+v33393fcSsNu6II47YY/7169frpJNOckk+rPbNHtvNym0s9f3WrVt15pln6pdffnFNAa3fm9WAlRc8AgD2DsEVAKBa2fhXgwYN0rHHHquhQ4e65nI2FpZ/UzqrsbnxxhtdunRLUW59hqx5nI8FFLfffrsLSKxvVmZmpkuPXhar6bIU6W+//barYbIaLMvm588CpLvuuks33XSTS11uSSgqW/5QWf8vC/psHaxWzfqpWbD0xRdfuP5cgWriLPufJfqwZn6+m6+fmjXFtKQbFkhZYgxLlGHp5K3fmjWpBABUjShPMI3AAQCoJhYEWSBgzQABAKhLuHwFAAAAAGFAcAUAAAAAYUCzQAAAAAAIA2quAAAAACAMCK4AAAAAIAwIrgAAAAAgDAiuAAAAACAMCK4AAAAAIAwIrgAAAAAgDAiuAAAAACAMCK4AAAAAQHvv/wMa+1HThQMJQwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program testingPredictionOfHeightOfPlateau2.py\n",
    "# The Underdog Model predicts the height of the Plateau \n",
    "# for a given length of target sequence and population size.\n",
    "# We test the prediction by comparing the predicted height\n",
    "# with the observed height for each length of target sequence and population size.\n",
    "# The observed heights of the plateaus are obtained from the previous experiment.\n",
    "# By the AI of Visual Studio Code with the help of the Author.\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Resources and parameters for the experiments.\n",
    "# Resources and parameters for the experiments.\n",
    "population_sizesk7 = np.array([5, 50, 200, 500])\n",
    "# Complexity is given by the length of the target sequence, \n",
    "# which is the string of 1s that the algorithm has to match.\n",
    "lengths_of_target_sequences = np.array([50, 500, 1500, 2500, 5000, 10000])\n",
    "#Results from the previous experiment, which are the observed heights of the plateaus for each population size and length of target sequence.\n",
    "observed_normalized_plateau_heights = np.array([[1.0, 0.848, 0.7086666666666667, 0.6624, 0.6058, 0.5816],  [1.0, 1.0,   0.8746666666666667, 0.7896, 0.7218, 0.6554], [1.0, 1.0,   0.9353333333333333, 0.8636, 0.76622, 0.6881],  [1.0, 1.0,   0.9613333333333334, 0.8848, 0.7916, 0.7149]])\n",
    "include_clone_populationk6 = True    \n",
    "include_mutate_allk7 = True    \n",
    "include_crossover_allk7 = True    \n",
    "mutation_rate = 0.01    \n",
    "lambda_s = 0.7 #Original value = 0.9216, but we can also test other values, such as 0.5 or 1.5, to see how it affects the prediction.\n",
    "lambda_r = 0.015  \n",
    "\n",
    "def print_parametersk7(population_sizesk7, lengths_of_target_sequences, observed_normalized_plateau_heights, mutation_rate, lambda_s, lambda_r):\n",
    "    print(f\"Lab: Simple Genetic Algorithm\")\n",
    "    print(f\"Purpose: compare diverse plateaus for different combinations of resources and complexity\")\n",
    "    print(f\"length_of_target_sequences = {lengths_of_target_sequences}\")\n",
    "    print(f\"population_sizesk7 = {population_sizesk7}\")\n",
    "    print(f\"observed_normalized_plateau_heights = {observed_normalized_plateau_heights}\")\n",
    "    #print(f\"include_clone_populationk7 = {include_clone_populationk7}\")\n",
    "    #print(f\"include_mutate_allk7 = {include_mutate_allk7}\")\n",
    "    #print(f\"include_crossover_allk7 = {include_crossover_allk7}\")\n",
    "    print(f\"mutation_rate = {mutation_rate}\")\n",
    "    print(f\"lambda_s = {lambda_s}\")\n",
    "    print(f\"lambda_r = {lambda_r}\")\n",
    "\n",
    "def draw_slopes_intersections_and_cross_indexesk4(T, A, B, D, population_size, length_of_target_sequence):\n",
    "    # Draw the slopes, intersections, and cross indices for the Underdog Model.\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    x = np.linspace(0, 1, 100)\n",
    "    y = (2 * T * A + B + D**0.5) / (2 * (T**2 + B))\n",
    "    plt.plot(x, y, label='Predicted height of plateau (Underdog Model)')\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.title(f'Slopes, Intersections, and Cross Indices for Population Size {population_size} and Target Length {length_of_target_sequence}')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# Prediction of the Underdog Model for the height of the plateau for each population size.\n",
    "def predicted_normalized_height_underdog_modelk7(population_size, length_of_target_sequence, mutation_rate, lambda_s, lambda_r ):\n",
    "   \n",
    "    # Easy tasks are solved by the algorithm, and the best fitness quickly reaches the maximum, which is the length of the target sequence.\n",
    "    # The plateau is reached when the best fitness is close to the maximum, and the algorithm struggles to find the optimal solution. \n",
    "    # The height of the plateau is the normalized best fitness when the plateau is reached, which is the last value of the normalized best fitness history.\n",
    "    # The Underdog Model predicts the height of the plateau based on the population size, the length of the target sequence, the mutation rate, and two parameters lambda_s and lambda_r that are related to the selection and recombination processes of the algorithm. \n",
    "    # The formula for the predicted height of the plateau is derived from the analysis of the la Evoluciónary dynamics of the algorithm, and it involves the following steps:   \n",
    "        # $x = \\frac{M_t}{H} = p_t $ =  Height of the normalized plateau\n",
    "\n",
    "    # $A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $\n",
    "    \n",
    "    # $B = \\lambda_r^2 (H - T) \\ln m$\n",
    "\n",
    "    # $D = B (T^2 - 4 \\lambda_s^2 T \\ln m + B)$.\n",
    "\n",
    "    # Predicted height = x = (2TA + B - sqrt(D)) / (2(T^2 + B))\n",
    "    # $x = \\frac{M_t}{H}  = \\frac{(2TA+B) \\pm \\sqrt{D}}{2(T^{2}+B)} = p_t$\n",
    "    \n",
    "    H = length_of_target_sequence\n",
    "    T = mutation_rate * length_of_target_sequence\n",
    "    m = population_size\n",
    "    A = T/2 + lambda_s * ( (T * np.log(m)) ** 0.5 )\n",
    "    B = (lambda_r ** 2) * (H - T) * np.log(m)\n",
    "    D = B * (T**2 - 4 * (lambda_s ** 2) * T * np.log(m) + B)\n",
    "    if D < 0:\n",
    "        print(f\"D: {D} Warning: Discriminant D is negative for population size {population_size} and target length {length_of_target_sequence}. Adjusting D to zero to avoid complex numbers.\")\n",
    "        D = 0  # Adjust D to zero to avoid complex numbers\n",
    "    denominator = 2 * (T**2 + B)\n",
    "    if denominator == 0:\n",
    "        print(f\"Denominator: {denominator} Warning: Denominator is zero for population size {population_size} and target length {length_of_target_sequence}. Adjusting to a small value to avoid division by zero.\")\n",
    "        denominator = 1e-10  # Adjust to a small value to avoid division by zero\n",
    "    #draw_slopes_intersections_and_cross_indexesk4(T, A, B, D, population_size, length_of_target_sequence)\n",
    "    predicted_normalized_height = (2 * T * A + B + D ** 0.5) / denominator\n",
    "    if predicted_normalized_height > 1.0:\n",
    "        print(f\"Predicted normalized height: {predicted_normalized_height} Warning: Predicted normalized height is greater than 1 for population size {population_size} and target length {length_of_target_sequence}. Adjusting to 1.0.\")\n",
    "        predicted_normalized_height = 1.0  # Adjust to 1.0 to ensure it does not exceed the maximum possible value\n",
    "    return predicted_normalized_height\n",
    "\n",
    "\n",
    "\n",
    "def main(population_sizesk7, lengths_of_target_sequences, observed_normalized_plateau_heights, mutation_rate, lambda_s, lambda_r):\n",
    "    print_parametersk7(population_sizesk7, lengths_of_target_sequences, observed_normalized_plateau_heights, mutation_rate, lambda_s, lambda_r)   \n",
    "   \n",
    "    predicted_heights = []\n",
    "    relative_errors = []\n",
    "    for i in range(len(population_sizesk7)):\n",
    "        predicted_normalized_plateau_heights_popSize = []\n",
    "        for j in range(len(lengths_of_target_sequences)):\n",
    "            population_size = population_sizesk7[i]\n",
    "            length_of_target_sequence = lengths_of_target_sequences[j]\n",
    "            predicted_height = predicted_normalized_height_underdog_modelk7(population_size, length_of_target_sequence, mutation_rate, lambda_s, lambda_r )\n",
    "            predicted_normalized_plateau_heights_popSize.append(predicted_height)\n",
    "            relative_error = abs(predicted_height - observed_normalized_plateau_heights[i][j])/observed_normalized_plateau_heights[i][j]\n",
    "            relative_errors.append(relative_error)\n",
    "            print(f\"Population size {population_size}, Target length {length_of_target_sequence}, Observed height {observed_normalized_plateau_heights[i][j]}: Predicted height of plateau = {predicted_height:.4f}, Relative error = {relative_error:.4f}\")      \n",
    "        predicted_heights.append(predicted_normalized_plateau_heights_popSize)\n",
    "    total_relative_error = sum(relative_errors) \n",
    "    print(f\"\\nTotal relative error between predicted heights and observed heights: {total_relative_error:.4f}\")\n",
    "    \n",
    "    # Draw the normalized plateau heights and predicted heights as function of population size.\n",
    "    # One graphic for each length of target sequence.\n",
    "    \n",
    "    for j in range(len(lengths_of_target_sequences)):\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        observed_normalized_plateau_height = [observed_normalized_plateau_heights[i][j] for i in range(len(population_sizesk7))]\n",
    "        plt.plot(population_sizesk7, observed_normalized_plateau_height, 'o-', label=f'Observed height ')\n",
    "        predicted_heights_for_length = [predicted_heights[i][j] for i in range(len(population_sizesk7))]\n",
    "        plt.plot(population_sizesk7, predicted_heights_for_length, 's-', label=f'Predicted height ')\n",
    "        plt.xlabel('Population Size')\n",
    "        plt.ylabel('Normalized Realized Plateau Height')\n",
    "        plt.title(f'Tendency of Normalized Plateau Heights for length of target sequences {lengths_of_target_sequences[j]}')\n",
    "        plt.legend() \n",
    "\n",
    "    '''\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    for j in range(len(lengths_of_target_sequences)):\n",
    "        observed_normalized_plateau_height = [observed_normalized_plateau_heights[i][j] for i in range(len(population_sizesk7))]\n",
    "        plt.plot(population_sizesk7, observed_normalized_plateau_height, 'o-', label=f'Target length {lengths_of_target_sequences[j]}')\n",
    "        predicted_heights_for_length = [predicted_heights[i][j] for i in range(len(population_sizesk7))]\n",
    "        plt.plot(population_sizesk7, predicted_heights_for_length, 's-', label=f'Predicted heights for Target length {lengths_of_target_sequences[j]}')\n",
    "    plt.xlabel('Population Size')\n",
    "    plt.ylabel('Normalized Realized Plateau Height')\n",
    "    plt.title('Tendency of Normalized Plateau Heights for different lengths of target sequences')\n",
    "    plt.legend() \n",
    "    '''\n",
    "if __name__ == \"__main__\":\n",
    "    main(population_sizesk7, lengths_of_target_sequences, observed_normalized_plateau_heights, mutation_rate, lambda_s, lambda_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d745b9",
   "metadata": {},
   "source": [
    "*Nuestras observaciones. *\n",
    "1. Nuestra fórmula produce valores imaginarios intermedios para números de tamaño poblacional bajo debido a la raíz cuadrada de un valor negativo. Para remediar esto, lo redefinimos como cero. \n",
    "2. En cualquier caso, nuestra fórmula predice un valor de la altura de la meseta mayor que 1. Así, la altura se redujo a 1. \n",
    "3. Nuestros valores para los parámetros de este programa fueron $ \\lambda_s = 0,7$ y $ \\lambda_r = 0,015$, que eran diferentes de los encontradas en un programa anterior $ \\lambda_s = 0,805$ y $ \\lambda_r = 0,009$. La diferencia no es tan grande, pero es preocupante ver que los valores óptimos varían de un entorno experimental a otro. \n",
    "4. Nuestro fracaso con números de tamaño de población bajo apunta a un problema anterior de la misma naturaleza que se resolvió con coeficientes variables. \n",
    "5. La varianza fue alta para poblaciones pequeñas y baja en los demás casos. \n",
    "6. Las predicciones y observaciones coinciden en buena medida. Se ha utilizado una amplia gama de combinaciones para disminuir la probabilidad de encajar por pura suerte. Parece, sin embargo, que el ajuste se puede mejorar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1193a104",
   "metadata": {},
   "source": [
    "**Ejercicio.** Compara los  historiales de la mejor aptitud observada con los previstos por el modelo Underdog con el fin de encontrar la mejor asignación a los parámetros del modelo del Perdedor. Usar cambios a mano (Evolución rudimentaria) o Evolución plena. Con ese objetivo, se debe reducir el error total de las predicciones respecto a lo que se observa en las simulaciones. Por lo tanto, debes probar diferentes asignaciones a los parámetros del modelo Underdog y elegir la mejor. Si eliges Evolución, eso se hará automática y recursivamente. \n",
    "> \n",
    ">*Answer.* Elegimos usar la Evolución para refinar la asignación de valores a los cuatro parámetros del modelo Underdog. \n",
    ">\n",
    "> Nuestro compromiso es entender la Evolución, específicamente, cómo se utilizan los recursos para combatir la complejidad. Los recursos vienen en la forma del tamaño de la población y las generaciones del Proceso Evolutivo, mientras que la complejidad viene dada por la tarea de emparejar una cadena binaria objetivo de una longitud predefinida. Cuanto mayor es la longitud, mayor es el costo en generaciones para alcanzar un estado estable, que puede ser diferente de la perfección. Si se aumentan los recursos, el estado estable o la meseta tomar un valor mayor. Se puede encontrar perfección, pero el costo es exponencial en la longitud de la entrada. Hemos desarrollado y probado el modelo Underdog de la batalla de la Evolución contra la complejidad, cuyo ingrediente distintivo consiste en fluctuaciones, desviaciones del valor medio de diversas observables. Hemos encontrado que las predicciones de este modelo son bastante buenas, pero cuatro parámetros recibieron asignaciones que fueron hechas por un enfoque semi-empírico, parte de la teoría, parte del ensayo y error. Este proceso es tedioso y no es más que una Evolución Rudimentaria. Por lo tanto, aquí pedimos ayuda a la Evolución, que es el experto en prueba y error. Esto se hace en el siguiente programa. Trabajos anteriores han demostrado que la Evolución puede producir resultados pobres contra alta complejidad. Pero, aquí, el gráfico de la mejor aptitud a lo largo de un Proceso Evolutivo es bastante simple. Por ello, nuestra apuesta es que la Evolución nos ayudaría en este caso. \n",
    "> \n",
    "> Para evitar un buen ajuste debido a un pequeño conjunto de pruebas, utilizamos diversas combinaciones de las dos variables independientes mencionadas (tamaño de la población, longitud de la secuencia objetivo), tal como se hizo en el programa anterior.  Cada pareja produce un historial de fitness observado más un historial de fitness predicho por el modelo Underdog, lo que genera diferencias. Esto mide la discrepancia entre lo observado y lo predicho. El error se define como la suma punto por punto del valor absoluto de la diferencia entre lo observado y lo predicho por el modelo Underdog. Sumamos sobre todas las parejas para obtener un error total para una asignación dada. Las asignaciones compiten para disminuir este error.  El proceso es lento, por lo que restringimos la Evolución para probar 10 cromosomas que codifican por 10 asignaciones que son evolucionadas para ver qué sucede. Para la rutina de optimización maestra, que es responsable de la optimización de las asignaciones, usamos un tamaño poblacional de 10 cromosomas y ejecutamos cada algoritmo genético subsiguiente durante 100 generaciones. \n",
    "> \n",
    "> El código resultante será complejo porque necesitamos ensamblar varias piezas de trabajos anteriores. Para empezar, necesitamos un par codificador-decodificador, específicamente para números de la forma $1,23456789$, que corresponde a la forma de todos los valores encontrados de los parámetros, que giran alrededor de 1. El fragmento de código correspondiente es el siguiente:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d70402b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program simpleEncoderDecoder.py\n",
    "# For numbers of the form 1.234567890 exclusively.\n",
    "\n",
    "def decoderjk9(s):\n",
    "    ''' The input is a number as string, outputs the number as a float. \n",
    "         Valid for numbers of the form 1.23456789 only, with a length of chromosome part of 10. '''\n",
    "    # A point is intercalated after the first char\n",
    "    ss = s[:1] + '.' + s[1:]\n",
    "    n = float(ss) # Convert to float\n",
    "    return  n\n",
    "\n",
    "\n",
    "\n",
    "def encoderjk9(float_number, length_of_chromosomes):\n",
    "    ''' The input is a paramter as a float, of the form 1.23456789,\n",
    "        outputs a string of digits. '''\n",
    "    #number to string\n",
    "    n_str = str(float_number) \n",
    "    \n",
    "    if float_number == 0:\n",
    "        return '0' * length_of_chromosomes\n",
    "    # Remove the decimal point        \n",
    "    n_str_noPoint = n_str.replace(\".\", \"\") \n",
    "    n = int(n_str_noPoint) # Convert to integer\n",
    "    print (f\"Float number: {float_number}, Number as string: {n_str}, string without point: {n_str_noPoint},  number without decimal point: {n}\")\n",
    "    # If the string is too long, it is trimmed to fit the length of the chromosome part.\n",
    "    n_str_adjusted = n_str_noPoint\n",
    "    if len(n_str_adjusted) > length_of_chromosomes - 1:\n",
    "        n_str_adjusted = str(n_str_noPoint)[:length_of_chromosomes]\n",
    "    # If the string is too short, it is completed with zeros to fit the length of the chromosome part.\n",
    "    while len(n_str_adjusted) < length_of_chromosomes:\n",
    "        n_str_adjusted = n_str_adjusted + '0'\n",
    "    return  n_str_adjusted\n",
    "   \n",
    "\n",
    "def test_decoder_encoder(myNumber, length_of_chromosome):\n",
    "    s = str(myNumber)\n",
    "    print(f\"Number: {myNumber}, string representation: {s}\")\n",
    "    encoded = encoderjk9(myNumber, length_of_chromosome)\n",
    "    decoded = decoderjk9(encoded)\n",
    "    print(f\"Original number: {myNumber}\")\n",
    "    print(f\"Encoding chromosome: {encoded}\")\n",
    "    print(f\"Decoded number: {decoded}\")\n",
    "\n",
    "# Test the encoder and decoder functions\n",
    "coef = 1.23456789\n",
    "test_decoder_encoder(coef, 10)  \n",
    "\n",
    "print()\n",
    "\n",
    "coef = 0.9053574604251853\n",
    "test_decoder_encoder(coef, 10)\n",
    "\n",
    "print()\n",
    "\n",
    "\n",
    "\n",
    "coef = 0.7\n",
    "length_of_chromosome = 10\n",
    "test_decoder_encoder(coef, length_of_chromosome)\n",
    "\n",
    "print()\n",
    "\n",
    "coef = 0.74\n",
    "length_of_chromosome = 10\n",
    "test_decoder_encoder(coef, length_of_chromosome)\n",
    "\n",
    "print()\n",
    "\n",
    "coef = 6.74383838392010347456484849\n",
    "length_of_chromosome = 10\n",
    "test_decoder_encoder(coef, length_of_chromosome)\n",
    "\n",
    "print()\n",
    "\n",
    "coef = 0.015\n",
    "length_of_chromosome = 10\n",
    "test_decoder_encoder(coef, length_of_chromosome)\n",
    "\n",
    "print()\n",
    "\n",
    "coef = 0.00153\n",
    "length_of_chromosome = 10\n",
    "test_decoder_encoder(coef, length_of_chromosome)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c876cc",
   "metadata": {},
   "source": [
    "**Ejercicio.** Ejecuta el programa anterior y verifica que sea correcto. \n",
    "\n",
    "*El programa. * Después de muchas pruebas y cambios, el programa completo que Evolution utiliza para optimizar las asignaciones de parámetros es el siguiente. Se necesitan 40 GB de RAM en un escritorio para que este programa funcione sin problemas. Recomendamos usar Colab. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e402399f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lab: Two Genetic Algorithms, one master, the other slave\n",
      "Purpose: Use Evolution to fine tune the parameters of the Underdog Model.\n",
      "The slave GA solves the problem of matching a target sequence of 1s, and produces best fitness histories for different combinations  of population sizes and lengths of target sequences.\n",
      "Experiment 1781867216.80214\n",
      "Parameters for the slave GA, which solves the problem of matching a target sequence of 1s:\n",
      "binary_alphabet = 01\n",
      "lengths_of_target_sequences = [   50   500  1500  2500  5000 10000]\n",
      "population_sizes = [  5  50 200 500]\n",
      "include_clone_population = True\n",
      "include_mutate_all = True\n",
      "include_crossover_all = True\n",
      "mutation_rate = 0.01\n",
      "recombination_rate = 0.01\n",
      "Parameters of the theoretical Underdog Model\n",
      "The Underdog Model predicts the behavior of the slave genetic algorithm, which solves the problem of matching a target sequence of 1s. The initial values of parameters are:\n",
      "c = 1.29\n",
      "k = 1.22\n",
      "lambda_k = 0.9053574604251853\n",
      "lambda_T = 1.1357816691600546\n",
      "lambda_s = 0.78\n",
      "lambda_r = 0.015\n",
      "mu = 0.01\n",
      "Parameters for the master GA.\n",
      "The master GA fine tunes the parameters of the Underdog Model by minimizing the error between the predicted best fitness histories and the observed best fitness histories in the simulations of the slave GA, which solves the problem of matching a target sequence of 1s.\n",
      "number_of_generations 1000\n",
      "digit_alphabet = 0123456789\n",
      "length_of_chromosomesjk10 = 10\n",
      "population_sizejk10 = 50\n",
      "include_clone_populationjk10 = True\n",
      "include_mutate_alljk10 = True\n",
      "include_crossover_alljk10 = True\n",
      "mutation_ratejk10 = 0.01\n",
      "recombination_ratejk10 = 0.01\n",
      "\n",
      "testOneAssignationjk10**\n",
      "lambda_k, lambda_T, lambda_s, lambda_r: 0.9053574604251853, 1.1357816691600546, 0.78, 0.015\n",
      "Total error between predicted and observed histories for all combinations: 517.5474\n",
      "masterGAjk10**\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 1,   best chromosome: 0905357460113578166907800000000015000000, smallest error: 517.5474494265208\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 2,   best chromosome: 0905357460013578166907800000000015000000, smallest error: 460.9067522161778\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 3,   best chromosome: 0905357460013578166907800000000015000000, smallest error: 460.9067522161778\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 4,   best chromosome: 0905357460093578166907800000000015000000, smallest error: 459.9423471137068\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 5,   best chromosome: 0905357460093538166907800000000015000000, smallest error: 459.827506670149\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 6,   best chromosome: 1905757460053538166907800000000015000000, smallest error: 415.55106245137375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 7,   best chromosome: 0905757460053538166907800000000015000000, smallest error: 352.34232142842774\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 8,   best chromosome: 0905757460053538166907830000000015000010, smallest error: 351.60334908299734\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 9,   best chromosome: 0905757460053538166907830000000013000010, smallest error: 351.4157536507586\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 10,   best chromosome: 0905757460053538166907830000000013000010, smallest error: 351.4157536507586\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 11,   best chromosome: 0905757460053538166907830000000013070810, smallest error: 351.3678041768287\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 12,   best chromosome: 0905757460053538166907830000000013070810, smallest error: 351.3678041768287\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 13,   best chromosome: 0505750460053538166907830000000013070810, smallest error: 329.9372830033418\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 14,   best chromosome: 0505750460053538166907830000000013070810, smallest error: 329.9372830033418\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 15,   best chromosome: 0505750460053538166907830000000013070810, smallest error: 329.9372830033418\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 16,   best chromosome: 0505750460053538166907830000000013070810, smallest error: 329.9372830033418\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 17,   best chromosome: 0505750460053538166907830000000013070810, smallest error: 329.9372830033418\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 18,   best chromosome: 0505750460053538166007830000000013070810, smallest error: 329.93728107079875\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 19,   best chromosome: 0505750460053538166007830000000013070810, smallest error: 329.93728107079875\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 20,   best chromosome: 0505750460153538166007830000000013070810, smallest error: 634.4822403992985\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 21,   best chromosome: 0505750460053538166007830000000013070810, smallest error: 329.93728107079875\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 22,   best chromosome: 0505750460053538166007830000000013070810, smallest error: 329.93728107079875\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 23,   best chromosome: 0505750460053538166007830000000013070810, smallest error: 329.93728107079875\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 24,   best chromosome: 0505750460053538666007830000000013270810, smallest error: 329.65762053117703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 25,   best chromosome: 0505750460053538666007830000000013270810, smallest error: 329.65762053117703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 26,   best chromosome: 0505750460053538666007830000000013270810, smallest error: 329.65762053117703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 27,   best chromosome: 0505750460053538666007830000000013270810, smallest error: 329.65762053117703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 28,   best chromosome: 0505750460053538666007830000000013270810, smallest error: 329.65762053117703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 29,   best chromosome: 0505750460053538666007830000000013290810, smallest error: 329.63157574903653\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 30,   best chromosome: 0505750460053538666007830000000013290810, smallest error: 329.63157574903653\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 31,   best chromosome: 0505750460053538666007830000000013290810, smallest error: 329.63157574903653\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 32,   best chromosome: 0505750460053538666007830000000013290810, smallest error: 329.63157574903653\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 33,   best chromosome: 0505750460053538666007830000000013290810, smallest error: 329.63157574903653\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 34,   best chromosome: 0505750460053538646007830000000013290810, smallest error: 329.6315337509235\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 35,   best chromosome: 0505750460053518646007830000000013290810, smallest error: 329.5895442933672\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 36,   best chromosome: 0505750460053518646007830000000013290810, smallest error: 329.5895442933672\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 37,   best chromosome: 1505750460053518646007830000000013290810, smallest error: 389.44702417019215\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 38,   best chromosome: 0505750460053518646007830000000013290810, smallest error: 329.5895442933672\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 39,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 40,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 41,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 42,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 43,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 44,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 45,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 46,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 47,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 48,   best chromosome: 0405790460053518646007830000000013290810, smallest error: 327.62972178285213\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 49,   best chromosome: 0400790460053518646007830000000013290810, smallest error: 327.574778466707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 50,   best chromosome: 0400790460053518646007830000000013290810, smallest error: 327.574778466707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 51,   best chromosome: 0400790460053518646007834000000013290810, smallest error: 327.2438856806387\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 52,   best chromosome: 0400790460053518646007834000000013290810, smallest error: 327.2438856806387\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 53,   best chromosome: 0400790460053518646007834000000013290890, smallest error: 327.2437502424497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 54,   best chromosome: 0400790460033518646007834000000013290890, smallest error: 327.4741439616872\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 55,   best chromosome: 0400790460033518646007834000000013290890, smallest error: 327.4741439616872\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 56,   best chromosome: 0400790460033518646007834000000013290890, smallest error: 327.4741439616872\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 57,   best chromosome: 0400790460033538646007834900000013290890, smallest error: 327.613538402274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 58,   best chromosome: 0400790460033538646007834900000013290890, smallest error: 327.613538402274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 59,   best chromosome: 0400790460033538646007834900000013290890, smallest error: 327.613538402274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 60,   best chromosome: 0400790460033538646007834900000013290890, smallest error: 327.613538402274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 61,   best chromosome: 0400790460033938646007834900000013290890, smallest error: 326.41889487416637\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 62,   best chromosome: 0400790460033938646007834900000013290890, smallest error: 326.41889487416637\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 63,   best chromosome: 0400790460033938646007834900000013290890, smallest error: 326.41889487416637\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 64,   best chromosome: 0400790460033938646007834900000013230890, smallest error: 326.01503808561165\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 65,   best chromosome: 0400790460033938646007734900000013230890, smallest error: 309.6993020296059\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 66,   best chromosome: 0400790460033938646007734900000013230890, smallest error: 309.6993020296059\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 67,   best chromosome: 0400790460033938646007734900000013230890, smallest error: 309.6993020296059\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 68,   best chromosome: 0400790460033938646007734900000013230890, smallest error: 309.6993020296059\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 69,   best chromosome: 0410790460033938646007734900000013230890, smallest error: 309.68913877360455\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 70,   best chromosome: 0410790460033938646007734900000013230890, smallest error: 309.68913877360455\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 71,   best chromosome: 0410790460033938646707734900000013230890, smallest error: 309.6891378137752\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 72,   best chromosome: 1410790460033938646707734900000013230890, smallest error: 367.24030434418165\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 73,   best chromosome: 0410790460033938646707734900000013230890, smallest error: 309.6891378137752\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 74,   best chromosome: 0410790460033938646707734900000013230890, smallest error: 309.6891378137752\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 75,   best chromosome: 0410790460033938646707734900000013230890, smallest error: 309.6891378137752\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 76,   best chromosome: 0410790460033938646707734900000013230890, smallest error: 309.6891378137752\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 77,   best chromosome: 0410794460033938646707734900000013230890, smallest error: 309.68913567849694\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 78,   best chromosome: 0410794460033938646707734900000013230890, smallest error: 309.68913567849694\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 79,   best chromosome: 0410792460033938646707734900000010230890, smallest error: 306.6113816467673\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 80,   best chromosome: 0410792460033938646707734900000010230890, smallest error: 306.6113816467673\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 81,   best chromosome: 0410792460033938646707734900000010270890, smallest error: 306.55794910400357\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 82,   best chromosome: 0410792460033938646707734900000010270890, smallest error: 306.55794910400357\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 83,   best chromosome: 0410792160033938646707734907000010270890, smallest error: 306.5577415147544\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 84,   best chromosome: 1410792160033938646707734907000010270890, smallest error: 355.3222364276497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 85,   best chromosome: 0410792860033938646707734907000010270890, smallest error: 306.5577329130654\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 86,   best chromosome: 0411792860033938646707734907000010270890, smallest error: 306.545444627584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 87,   best chromosome: 0412792860033938646707734907000010270890, smallest error: 306.5332282858844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 88,   best chromosome: 0412792860033938646707734907000010270890, smallest error: 306.5332282858844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 89,   best chromosome: 0412792860033138646707734907000010270890, smallest error: 306.32295238680547\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 90,   best chromosome: 0412792860033138646707734907000010270898, smallest error: 306.3229460077887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 91,   best chromosome: 0412792860033138646407734907000010270898, smallest error: 306.3229459771532\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 92,   best chromosome: 0412792860033138646407734907000010270898, smallest error: 306.3229459771532\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 93,   best chromosome: 0412792860033138646407734907000010270898, smallest error: 306.3229459771532\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 94,   best chromosome: 0412792860033138616407734907000010270898, smallest error: 306.32294291557497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 95,   best chromosome: 0412792860033138616407734907000010270898, smallest error: 306.32294291557497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 96,   best chromosome: 0412792860033138616407734907000010270898, smallest error: 306.32294291557497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 97,   best chromosome: 0412792860033138616407734907000010270898, smallest error: 306.32294291557497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 98,   best chromosome: 0412792860033138616407734907000010270898, smallest error: 306.32294291557497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 99,   best chromosome: 0412792860033138616407734907000010270898, smallest error: 306.32294291557497\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 100,   best chromosome: 0412792860033138616407734908000010270898, smallest error: 306.32293186096143\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 101,   best chromosome: 0412792860033138616407734908000010270898, smallest error: 306.32293186096143\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 102,   best chromosome: 0412792860033138616407734908000010270898, smallest error: 306.32293186096143\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 103,   best chromosome: 0412792860033138616407734908000010270898, smallest error: 306.32293186096143\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 104,   best chromosome: 0412792860033138616407734908000010270899, smallest error: 306.32293106358384\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 105,   best chromosome: 0412792860033138616407734908000010270899, smallest error: 306.32293106358384\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 106,   best chromosome: 0412792860033138616407734908000010270899, smallest error: 306.32293106358384\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 107,   best chromosome: 0412792860033138616407734908000010270899, smallest error: 306.32293106358384\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 108,   best chromosome: 0412792860033138616407734908000010270899, smallest error: 306.32293106358384\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 109,   best chromosome: 0412792860033138616407734908090010270899, smallest error: 306.3229300686543\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 110,   best chromosome: 0412792860033138616407734908090010270899, smallest error: 306.3229300686543\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 111,   best chromosome: 0412792860033138616407734908090010270899, smallest error: 306.3229300686543\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 112,   best chromosome: 0412792860033138616407734908090010270899, smallest error: 306.3229300686543\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 113,   best chromosome: 0412792861033138616407734908990010270899, smallest error: 306.32292010733795\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 114,   best chromosome: 0412792861033134616407734908990010270899, smallest error: 306.3225118952442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 115,   best chromosome: 0412792861033134616407734908990010270899, smallest error: 306.3225118952442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 116,   best chromosome: 0412792861033134616407734908990010270899, smallest error: 306.3225118952442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 117,   best chromosome: 0412792861033134616407734908990010270899, smallest error: 306.3225118952442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 118,   best chromosome: 0412792861033134616407734908990010270899, smallest error: 306.3225118952442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 119,   best chromosome: 0412792861033134616407734908990010270879, smallest error: 306.32252784342325\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 120,   best chromosome: 0412792861033134616407734908990010270879, smallest error: 306.32252784342325\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 121,   best chromosome: 0412792861033134616407734908990010270879, smallest error: 306.32252784342325\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 122,   best chromosome: 0412792861033134616407734908990010270879, smallest error: 306.32252784342325\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 123,   best chromosome: 0412792861033134616407734908990010270879, smallest error: 306.32252784342325\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 124,   best chromosome: 0412792861033134616407734908990010270879, smallest error: 306.32252784342325\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 125,   best chromosome: 0412792861033114616407734908990010270879, smallest error: 306.3205281770811\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 126,   best chromosome: 0412792861033114616407734908990010270879, smallest error: 306.3205281770811\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 127,   best chromosome: 0412792861033114616407744908990010270879, smallest error: 306.31883065531514\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 128,   best chromosome: 0412792861033114616407744908990010270879, smallest error: 306.31883065531514\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 129,   best chromosome: 1412792861033114616407744908990010270879, smallest error: 356.31107920229806\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 130,   best chromosome: 0412792861033114616407744908990010270879, smallest error: 306.31883065531514\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 131,   best chromosome: 0412792861033114616407744908990010270879, smallest error: 306.31883065531514\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 132,   best chromosome: 1412792861033114616407744908990010270879, smallest error: 356.31107920229806\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 133,   best chromosome: 0412792861033114916407744908990010270879, smallest error: 306.3187639083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 134,   best chromosome: 0412792861033114916407744908990010270879, smallest error: 306.3187639083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 135,   best chromosome: 0412792861033114916407754968990010270879, smallest error: 306.4829555677809\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 136,   best chromosome: 0412792861033114916407754968990010270879, smallest error: 306.4829555677809\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 137,   best chromosome: 0412792861033114916407754968990010270879, smallest error: 306.4829555677809\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 138,   best chromosome: 0412792881033114916407754968990010270879, smallest error: 306.4829553446684\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 139,   best chromosome: 0412792881033114916607754968990010270879, smallest error: 306.48295523968346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 140,   best chromosome: 0412792881033114916607754968990010270879, smallest error: 306.48295523968346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 141,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 142,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 143,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 144,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 145,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 146,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 147,   best chromosome: 0412796881033114916607754968990010270879, smallest error: 306.4829106173622\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 148,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 149,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 150,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 151,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 152,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 153,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 154,   best chromosome: 1412796881033114916607754068990010270879, smallest error: 357.2032719082511\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 155,   best chromosome: 0412796881033114916607754068990010270879, smallest error: 306.46013819358024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 156,   best chromosome: 0412796881033114918607754068990010270879, smallest error: 306.46013724062675\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 157,   best chromosome: 0412796881033114918607754068990010270879, smallest error: 306.46013724062675\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 158,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 159,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 160,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 161,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 162,   best chromosome: 1412796881033114918607753068990010270879, smallest error: 357.0986760949334\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 163,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 164,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 165,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 166,   best chromosome: 0412796881033114918607753068990010270879, smallest error: 306.4382479908395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 167,   best chromosome: 0412796881033114918607752068990010270879, smallest error: 306.41792442338146\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 168,   best chromosome: 0412796881033114918607752068990010270879, smallest error: 306.41792442338146\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 169,   best chromosome: 0412796881033114918607752068990010270879, smallest error: 306.41792442338146\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 170,   best chromosome: 0412796881033114918607752068990010270878, smallest error: 306.41792431006576\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 171,   best chromosome: 0412796881033114918607752068990010270778, smallest error: 306.41791297844395\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 172,   best chromosome: 0412796881033114218607752068990010240778, smallest error: 306.41545437857246\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 173,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 174,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 175,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 176,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 177,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 178,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 179,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 180,   best chromosome: 0412796881033114218607752061990010240778, smallest error: 306.4153234406134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 181,   best chromosome: 1412796881033124218607752061990010240778, smallest error: 356.8886630282765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 182,   best chromosome: 0412796881033125218607752061990010240778, smallest error: 306.4110547444625\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 183,   best chromosome: 0412796881033125218607752061990010240778, smallest error: 306.4110547444625\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 184,   best chromosome: 0412796881033125218607752061970010240778, smallest error: 306.4110543713353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 185,   best chromosome: 1412796881033125218607752061970010240778, smallest error: 356.8878367892501\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 186,   best chromosome: 0412796881033125218607752061970010240778, smallest error: 306.4110543713353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 187,   best chromosome: 0412796887033125218607752061970010240778, smallest error: 306.41105430222433\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 188,   best chromosome: 0412796887033125218607752061970010240778, smallest error: 306.41105430222433\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 189,   best chromosome: 0412796887033125218607752061970010240778, smallest error: 306.41105430222433\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 190,   best chromosome: 0412796887033125218607752061970010240778, smallest error: 306.41105430222433\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 191,   best chromosome: 0412796887033126218607752061970010240778, smallest error: 306.41066870361465\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 192,   best chromosome: 0412796887033126218607752061970010240778, smallest error: 306.41066870361465\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 193,   best chromosome: 0412796887033176218607752061970010240778, smallest error: 306.393944838241\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 194,   best chromosome: 0412796887033176218607752061970010240778, smallest error: 306.393944838241\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 195,   best chromosome: 0412796887033176218607752061970010240778, smallest error: 306.393944838241\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 196,   best chromosome: 0412796887033176218607752061970010247778, smallest error: 306.3938964585794\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 197,   best chromosome: 0412796887033176218607752061970010247778, smallest error: 306.3938964585794\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 198,   best chromosome: 0412796887033176218607752061970010247778, smallest error: 306.3938964585794\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 199,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 200,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 201,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 202,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 203,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 204,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 205,   best chromosome: 0482796887033176218607752061970010247778, smallest error: 305.8920949864209\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 206,   best chromosome: 0482796887033176238607752061970010247778, smallest error: 305.8920888216719\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 207,   best chromosome: 0482796887033176238607752061970010247774, smallest error: 305.8920878861283\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 208,   best chromosome: 0482796887033176238607752061970010247774, smallest error: 305.8920878861283\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 209,   best chromosome: 1482796887033176238607752061970010247774, smallest error: 361.50107602559376\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 210,   best chromosome: 0482796887033176238607752061970010247774, smallest error: 305.8920878861283\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 211,   best chromosome: 0482796887033176238607752061370010247774, smallest error: 305.89207531418293\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 212,   best chromosome: 0482796887033176238607752061370010247774, smallest error: 305.89207531418293\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 213,   best chromosome: 0482796887033176238607752061370010247774, smallest error: 305.89207531418293\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 214,   best chromosome: 0482796887033176238607752061370010247774, smallest error: 305.89207531418293\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 215,   best chromosome: 0482796887033176238607752061370010247774, smallest error: 305.89207531418293\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 216,   best chromosome: 0482796887033176238607752061370010247774, smallest error: 305.89207531418293\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 217,   best chromosome: 0482796887033176238607752061070010217774, smallest error: 305.8856398790075\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 218,   best chromosome: 0482796887033176238607752061070010217774, smallest error: 305.8856398790075\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 219,   best chromosome: 0482796887033176238607752061070010217774, smallest error: 305.8856398790075\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 220,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 221,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 222,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 223,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 224,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 225,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 226,   best chromosome: 0482796887033176238607752061070010210774, smallest error: 305.8842675892531\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 227,   best chromosome: 1482796887033176238607752061070010210774, smallest error: 361.379276454729\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 228,   best chromosome: 0482796887033176238607756061070010810774, smallest error: 306.53613538980454\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 229,   best chromosome: 0482796887033176238607756061070010810774, smallest error: 306.53613538980454\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 230,   best chromosome: 0482796887033176238607756061070010810714, smallest error: 306.5360516716183\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 231,   best chromosome: 0482796887033176238607756061070010810714, smallest error: 306.5360516716183\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 232,   best chromosome: 0482796887033176238607756061070010810714, smallest error: 306.5360516716183\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 233,   best chromosome: 0482796887033176238607756061070010810714, smallest error: 306.5360516716183\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 234,   best chromosome: 0482796887033176238607756061070010810114, smallest error: 306.5352144864066\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 235,   best chromosome: 0482796887033176238607756061020010810114, smallest error: 306.53521171193205\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 236,   best chromosome: 0482796885033176251607756061020010810115, smallest error: 306.5352032891482\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 237,   best chromosome: 0482796885033176251607757061020010810115, smallest error: 306.5924942329996\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 238,   best chromosome: 0482796885033176251607757061020010810115, smallest error: 306.5924942329996\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 239,   best chromosome: 0482796885033176251607757061020010810115, smallest error: 306.5924942329996\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 240,   best chromosome: 0482796885033176251607757061020010810115, smallest error: 306.5924942329996\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 241,   best chromosome: 0482796885033176251607757061010010810115, smallest error: 306.5924936488296\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 242,   best chromosome: 0482396885033176251607757061010010810115, smallest error: 306.59220737028295\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 243,   best chromosome: 0482396885033176251607757061010010810115, smallest error: 306.59220737028295\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 244,   best chromosome: 0482396885033176251607757061010010810115, smallest error: 306.59220737028295\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 245,   best chromosome: 0482306885036176251607757061010010810115, smallest error: 306.18918902481755\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 246,   best chromosome: 0482306885036176251607757061010010810115, smallest error: 306.18918902481755\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 247,   best chromosome: 0482306885036176251607757061010010810125, smallest error: 306.1891821119725\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 248,   best chromosome: 0482306885036176251607757061010010810825, smallest error: 306.18869824590394\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 249,   best chromosome: 0482306885036176251607757461010010810825, smallest error: 306.17916811606415\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 250,   best chromosome: 0482306885036176251607757461010010810825, smallest error: 306.17916811606415\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 251,   best chromosome: 0482306885036176251607757461010010810825, smallest error: 306.17916811606415\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 252,   best chromosome: 0482306885036176251607757461010010810825, smallest error: 306.17916811606415\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 253,   best chromosome: 0482306885036176251607757461010010810825, smallest error: 306.17916811606415\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 254,   best chromosome: 0482306885036176251607757461010010810825, smallest error: 306.17916811606415\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 255,   best chromosome: 0482306885036176251607757161010010830825, smallest error: 306.1728434369251\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 256,   best chromosome: 0482306885036176251607757161010010830825, smallest error: 306.1728434369251\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 257,   best chromosome: 0482306885036176251607757161010010830825, smallest error: 306.1728434369251\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 258,   best chromosome: 0482306885036176251607757161010010830827, smallest error: 306.1728421292571\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 259,   best chromosome: 0482306885036176251607757161010010830827, smallest error: 306.1728421292571\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 260,   best chromosome: 0482306585036176251607757161010010830827, smallest error: 306.172842069848\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 261,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 262,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 263,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 264,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 265,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 266,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 267,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 268,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 269,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 270,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 271,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 272,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 273,   best chromosome: 0482306585036076251607757161010010830827, smallest error: 306.1096420796364\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 274,   best chromosome: 0482306505036076251607757161010010830827, smallest error: 306.1096420622352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 275,   best chromosome: 0482306505036076251607757161010010830827, smallest error: 306.1096420622352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 276,   best chromosome: 0482306505036076251607757161010010830827, smallest error: 306.1096420622352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 277,   best chromosome: 0482306505036076251607757161610010830827, smallest error: 306.10963000470656\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 278,   best chromosome: 0482306505036076251607757161610010830827, smallest error: 306.10963000470656\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 279,   best chromosome: 0482306505036076251607757161610010830827, smallest error: 306.10963000470656\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 280,   best chromosome: 0482306505036076251607757161610010830827, smallest error: 306.10963000470656\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 281,   best chromosome: 0482306505036076251607757161610010830827, smallest error: 306.10963000470656\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 282,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 283,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 284,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 285,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 286,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 287,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 288,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 289,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 290,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 291,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 292,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 293,   best chromosome: 0482306505036076251607757161610010830857, smallest error: 306.10961259667545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 294,   best chromosome: 0482306405036076251607757161610010830857, smallest error: 306.10961257492534\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 295,   best chromosome: 0482306405036076251607757161610010830887, smallest error: 306.10959516690326\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 296,   best chromosome: 0482306405036076251607757161610010830887, smallest error: 306.10959516690326\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 297,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 298,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 299,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 300,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 301,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 302,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 303,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 304,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 305,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 306,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 307,   best chromosome: 0482306405036076251607757161650010830887, smallest error: 306.1095943630707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 308,   best chromosome: 0482306493036076251607757161650010830887, smallest error: 306.10959438221056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 309,   best chromosome: 0482306493036076251607757161650010830887, smallest error: 306.10959438221056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 310,   best chromosome: 0482306593036076251607757861650010830887, smallest error: 306.09603193479273\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 311,   best chromosome: 0482306593036076251607757861650010830887, smallest error: 306.09603193479273\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 312,   best chromosome: 0482306593036076251607757861650010830887, smallest error: 306.09603193479273\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 313,   best chromosome: 0482306593036076251607757861650010860888, smallest error: 306.08176000915887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 314,   best chromosome: 0482306593036076251607757861650010860888, smallest error: 306.08176000915887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 315,   best chromosome: 0482326593036076251607757861650010860888, smallest error: 306.0817689274691\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 316,   best chromosome: 0482326593036076251607757861650010860888, smallest error: 306.0817689274691\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 317,   best chromosome: 0482326193036076251607757861650010860888, smallest error: 306.0817687491041\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 318,   best chromosome: 0482323193036076251607757861650010860888, smallest error: 306.08176741136737\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 319,   best chromosome: 0482323193036076251607757861650010860888, smallest error: 306.08176741136737\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 320,   best chromosome: 0482323193036078251607757861650010890888, smallest error: 306.0706191393287\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 321,   best chromosome: 0482323192036078251607757861650010890888, smallest error: 306.07061913866505\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 322,   best chromosome: 0482323192036078251607757861650010890888, smallest error: 306.07061913866505\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 323,   best chromosome: 0482323192036078251607757861650010890888, smallest error: 306.07061913866505\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 324,   best chromosome: 0482321192036078251607757861650010890888, smallest error: 306.07061781156216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 325,   best chromosome: 0482321192036078251107757861650010891888, smallest error: 306.07025235030466\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 326,   best chromosome: 1482321192036077251107757861650010891888, smallest error: 362.6752012326667\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 327,   best chromosome: 0482321192036077251107757861670010891888, smallest error: 306.06974687926214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 328,   best chromosome: 0482321192036077251107757861770010891888, smallest error: 306.06974554272165\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 329,   best chromosome: 0482321192036077251107757861770010891888, smallest error: 306.06974554272165\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 330,   best chromosome: 0482321192036077251107757861770010891888, smallest error: 306.06974554272165\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 331,   best chromosome: 0482321192036077251107757881770010891888, smallest error: 306.06947823468516\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 332,   best chromosome: 0482321192036077251107757881770010891888, smallest error: 306.06947823468516\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 333,   best chromosome: 0482311192036077251107757881770010891888, smallest error: 306.0694712007799\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 334,   best chromosome: 0482311192036077251107757881770010891888, smallest error: 306.0694712007799\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 335,   best chromosome: 0482311192036077251107757881770010891888, smallest error: 306.0694712007799\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 336,   best chromosome: 0482311192036077251107757881770010891888, smallest error: 306.0694712007799\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 337,   best chromosome: 0482311192036077251107757881770010891888, smallest error: 306.0694712007799\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 338,   best chromosome: 0482311192036077251307757885770010891888, smallest error: 306.06941784007387\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 339,   best chromosome: 0482311190036077251307757885770010891888, smallest error: 306.0694178386665\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 340,   best chromosome: 0482311190036077251307757885770010891888, smallest error: 306.0694178386665\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 341,   best chromosome: 0482311190036077221307757885770010891888, smallest error: 306.06940269935893\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 342,   best chromosome: 0482311190036077221307757885770010891888, smallest error: 306.06940269935893\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 343,   best chromosome: 0482311190036077221307757885770010891888, smallest error: 306.06940269935893\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 344,   best chromosome: 0482311160036077221307757885770010891888, smallest error: 306.0694026782569\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 345,   best chromosome: 0482311160036077221307757885770010891888, smallest error: 306.0694026782569\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 346,   best chromosome: 0482311160036072221307757885770010891888, smallest error: 306.06689108545356\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 347,   best chromosome: 0482311160036072221307757885770010891888, smallest error: 306.06689108545356\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 348,   best chromosome: 0482311160036072221307757885770010891888, smallest error: 306.06689108545356\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 349,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 350,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 351,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 352,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 353,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 354,   best chromosome: 1482311160036072221307757886770010891888, smallest error: 362.67528240231707\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 355,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 356,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 357,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 358,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 359,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 360,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 361,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 362,   best chromosome: 0482311160036072221307757886770010891888, smallest error: 306.0668779247056\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 363,   best chromosome: 0482311160036072221307757886770010893888, smallest error: 306.0661662684592\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 364,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 365,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 366,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 367,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 368,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 369,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 370,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 371,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 372,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 373,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 374,   best chromosome: 0482311169036072221307757886770010893888, smallest error: 306.0661662749109\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 375,   best chromosome: 0482311169036072221307757886970010893888, smallest error: 306.0661636675554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 376,   best chromosome: 0482311169036072221307757886970010893888, smallest error: 306.0661636675554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 377,   best chromosome: 0482311169036072221307757886970010893888, smallest error: 306.0661636675554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 378,   best chromosome: 0482311149036072221307757886970010893888, smallest error: 306.0661636532216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 379,   best chromosome: 0482311149036072221307757886970010893888, smallest error: 306.0661636532216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 380,   best chromosome: 0482311149036072221307757886970010893888, smallest error: 306.0661636532216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 381,   best chromosome: 0482311149036072221307757886970010893888, smallest error: 306.0661636532216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 382,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 383,   best chromosome: 1482311149036072221307757886770010893888, smallest error: 362.67987943012196\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 384,   best chromosome: 1482311149036072221307757886770010893888, smallest error: 362.67987943012196\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 385,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 386,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 387,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 388,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 389,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 390,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 391,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 392,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 393,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 394,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 395,   best chromosome: 0482311149036072221307757886770010893888, smallest error: 306.06616626057473\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 396,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 397,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 398,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 399,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 400,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 401,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 402,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 403,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 404,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 405,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 406,   best chromosome: 0482311149036072221307757886770010896888, smallest error: 306.06511471725145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 407,   best chromosome: 0482311149036072221307757886720010896888, smallest error: 306.06511536151504\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 408,   best chromosome: 0482311149036052221307757886720010896888, smallest error: 306.0552331215519\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 409,   best chromosome: 0482311149036052221307757886720010896888, smallest error: 306.0552331215519\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 410,   best chromosome: 0482311149036052221307757886720010896888, smallest error: 306.0552331215519\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 411,   best chromosome: 0482311149036052221307757886720010896888, smallest error: 306.0552331215519\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 412,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 413,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 414,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 415,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 416,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 417,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 418,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 419,   best chromosome: 0482311148036052221307757886720010896888, smallest error: 306.05523312083034\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 420,   best chromosome: 0482311148036056221307757886720010896888, smallest error: 306.0572037338058\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 421,   best chromosome: 0482311148036056221307757886720010896888, smallest error: 306.0572037338058\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 422,   best chromosome: 0482311148036056221307757886720010896888, smallest error: 306.0572037338058\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 423,   best chromosome: 0482311148036056221307757886720010896888, smallest error: 306.0572037338058\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 424,   best chromosome: 0482311148036046221307757886720010896888, smallest error: 306.0522814932028\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 425,   best chromosome: 0482311148036046221307757886720010896888, smallest error: 306.0522814932028\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 426,   best chromosome: 0482311148036046221307757896720010896888, smallest error: 306.05215569682804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 427,   best chromosome: 0482311148036046221107757896720010896888, smallest error: 306.05215559870476\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 428,   best chromosome: 0482311148036046221107757896720010896888, smallest error: 306.05215559870476\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 429,   best chromosome: 0482311148036042221107757896720010896888, smallest error: 306.05019353870307\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 430,   best chromosome: 0482311148036042221107757896720010896888, smallest error: 306.05019353870307\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 431,   best chromosome: 0482311148036042221107757896720010896888, smallest error: 306.05019353870307\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 432,   best chromosome: 0482311148036042221107757896720010896888, smallest error: 306.05019353870307\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 433,   best chromosome: 0482311148036041221107757896720010896878, smallest error: 306.0497073461107\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 434,   best chromosome: 0482311148036041221107757896720010896878, smallest error: 306.0497073461107\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 435,   best chromosome: 1482311148036041211107757896720010896878, smallest error: 362.68368209190726\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 436,   best chromosome: 0482311148036041211107757896720010896878, smallest error: 306.04970245022133\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 437,   best chromosome: 0482311148036041211107757896720010896878, smallest error: 306.04970245022133\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 438,   best chromosome: 0482311148036041211107757896720010896878, smallest error: 306.04970245022133\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 439,   best chromosome: 0482311148036041211107757896720010896878, smallest error: 306.04970245022133\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 440,   best chromosome: 0482311148036041211107757896720010896878, smallest error: 306.04970245022133\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 441,   best chromosome: 0482311148036040211107757896720010896878, smallest error: 306.04921285814305\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 442,   best chromosome: 0482311148036040211107757896720010896878, smallest error: 306.04921285814305\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 443,   best chromosome: 0482311148036040211107757896720010896878, smallest error: 306.04921285814305\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 444,   best chromosome: 0482311148036040211107757896720010896878, smallest error: 306.04921285814305\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 445,   best chromosome: 0482311148036040211107757896720010896878, smallest error: 306.04921285814305\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 446,   best chromosome: 0482311148036040211107757896720010896878, smallest error: 306.04921285814305\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 447,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 448,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 449,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 450,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 451,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 452,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 453,   best chromosome: 0482311148036040211107758896720010896878, smallest error: 306.03804299278715\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 454,   best chromosome: 0482311148036040211107758896750010896878, smallest error: 306.03804269915156\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 455,   best chromosome: 0482311148036040211107758896750010846878, smallest error: 306.05407294007983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 456,   best chromosome: 1482311148036040311107757896750010846878, smallest error: 362.5692109031422\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 457,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 458,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 459,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 460,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 461,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 462,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 463,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 464,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 465,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 466,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 467,   best chromosome: 0482311148036040311107757896750010846878, smallest error: 306.0684262515618\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 468,   best chromosome: 0482311148036040011107757896750010846878, smallest error: 306.068269061765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 469,   best chromosome: 0482311148036040011107757896750010846878, smallest error: 306.068269061765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 470,   best chromosome: 0482311148036040011107757896750010846878, smallest error: 306.068269061765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 471,   best chromosome: 0482311148036040011107757896750010846878, smallest error: 306.068269061765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 472,   best chromosome: 0482311148036040011107757896750010846878, smallest error: 306.068269061765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 473,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 474,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 475,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 476,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 477,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 478,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 479,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 480,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 481,   best chromosome: 0482011148036040011107757896750010846878, smallest error: 306.0681452806976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 482,   best chromosome: 1482011148036030011107757896750010846878, smallest error: 362.54828058102186\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 483,   best chromosome: 0482011148036030011107757896750010846878, smallest error: 306.06293722264485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 484,   best chromosome: 0482011148036030011107757896750010846878, smallest error: 306.06293722264485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 485,   best chromosome: 0482011148036030011107757896750010886878, smallest error: 306.04746595096367\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 486,   best chromosome: 0482011148036030011107757896750010886878, smallest error: 306.04746595096367\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 487,   best chromosome: 0482011148036030011107757896750010886878, smallest error: 306.04746595096367\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 488,   best chromosome: 0482011141036030011107757896750010886878, smallest error: 306.0474659468435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 489,   best chromosome: 0482011141036030011107757896750010886878, smallest error: 306.0474659468435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 490,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 491,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 492,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 493,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 494,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 495,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 496,   best chromosome: 0482011141036030011107757896750010866878, smallest error: 306.0547656596242\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 497,   best chromosome: 0482011141036030011107757896770010866878, smallest error: 306.05476538805954\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 498,   best chromosome: 0482011141036030011107757896770010866878, smallest error: 306.05476538805954\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 499,   best chromosome: 0482011141036030011107756896770010866878, smallest error: 306.0695295803582\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 500,   best chromosome: 0482011140036030011107756896770010866878, smallest error: 306.06952957998584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 501,   best chromosome: 0482011140036030011107756896770010866878, smallest error: 306.06952957998584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 502,   best chromosome: 0482011140036030011107756896770010866878, smallest error: 306.06952957998584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 503,   best chromosome: 0482011140036030011107756896770010866878, smallest error: 306.06952957998584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 504,   best chromosome: 0482011140036030011107756896770010866878, smallest error: 306.06952957998584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 505,   best chromosome: 1482011140036030011107756896770010866878, smallest error: 362.54084024981336\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 506,   best chromosome: 0482011140036030011107756896770010866878, smallest error: 306.06952957998584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 507,   best chromosome: 0482011140036030011107756896770010876878, smallest error: 306.0651480568353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 508,   best chromosome: 0482011140036030011107756896770010876878, smallest error: 306.0651480568353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 509,   best chromosome: 0482011140036030011107756896770010876878, smallest error: 306.0651480568353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 510,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 511,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 512,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 513,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 514,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 515,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 516,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 517,   best chromosome: 0482011140036030011107756896770010873878, smallest error: 306.06644320228776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 518,   best chromosome: 0482011140036000011107756896770010873878, smallest error: 306.0508611723693\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 519,   best chromosome: 0482011140036000011107756896770010873878, smallest error: 306.0508611723693\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 520,   best chromosome: 0482011140036000011107756896970010873878, smallest error: 306.05085828741323\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 521,   best chromosome: 0482011140036000011107756896970010873878, smallest error: 306.05085828741323\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 522,   best chromosome: 0482011140036000011107756896970010873878, smallest error: 306.05085828741323\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 523,   best chromosome: 0482011140036000011107756896970010873878, smallest error: 306.05085828741323\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 524,   best chromosome: 0482011140036000011107756896970010873878, smallest error: 306.05085828741323\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 525,   best chromosome: 0482011140036000011107756896970010873878, smallest error: 306.05085828741323\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 526,   best chromosome: 0482011140036000001107756896970010873878, smallest error: 306.050853192916\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 527,   best chromosome: 0482011140036000001107756896970010873878, smallest error: 306.050853192916\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 528,   best chromosome: 0482011140036000001107756896970010973878, smallest error: 306.02011653993924\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 529,   best chromosome: 0482011140036000001107756896970010973878, smallest error: 306.02011653993924\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 530,   best chromosome: 0482011140036000001107756896970010973878, smallest error: 306.02011653993924\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 531,   best chromosome: 0482011140036000001107756896970010973878, smallest error: 306.02011653993924\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 532,   best chromosome: 0482011140036000001107756896970010973878, smallest error: 306.02011653993924\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 533,   best chromosome: 0482011140036000001107756896970010973878, smallest error: 306.02011653993924\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 534,   best chromosome: 0482011140036000001107756896970010978878, smallest error: 306.0190202271426\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 535,   best chromosome: 0482011140036000001107756896970010978878, smallest error: 306.0190202271426\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 536,   best chromosome: 0482011140036000001107756896970010978878, smallest error: 306.0190202271426\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 537,   best chromosome: 0482011140036000001107756896970010978878, smallest error: 306.0190202271426\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 538,   best chromosome: 0482011140036000001107756896970010978878, smallest error: 306.0190202271426\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 539,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 540,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 541,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 542,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 543,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 544,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 545,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 546,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 547,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 548,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 549,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 550,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 551,   best chromosome: 0482011140036000001007756896970010978878, smallest error: 306.0190201835343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 552,   best chromosome: 0482011140035000001007756896970010978878, smallest error: 305.8674023445664\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 553,   best chromosome: 0482011140035000005007756896970010978878, smallest error: 305.8674018497391\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 554,   best chromosome: 0482011140035010005007756896970010978878, smallest error: 305.86618302902235\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 555,   best chromosome: 0482011140035010005007756896970010978878, smallest error: 305.86618302902235\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 556,   best chromosome: 0482011140035010005007756836970010978878, smallest error: 305.8648755471358\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 557,   best chromosome: 1482011140035010005007756836970010978878, smallest error: 363.0524809297791\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 558,   best chromosome: 0482011140035010005007756836970010978878, smallest error: 305.8648755471358\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 559,   best chromosome: 0482011140035010005007756836970010978878, smallest error: 305.8648755471358\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 560,   best chromosome: 0482011140035010005007756836970010978878, smallest error: 305.8648755471358\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 561,   best chromosome: 0482011140035010005007756836970010978478, smallest error: 305.86465984026495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 562,   best chromosome: 0482011140035010005007756836970010978478, smallest error: 305.86465984026495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 563,   best chromosome: 0482011140035010005007751836970010978478, smallest error: 305.7767355093586\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 564,   best chromosome: 0482011140035010005007751836970010978478, smallest error: 305.7767355093586\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 565,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 566,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 567,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 568,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 569,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 570,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 571,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 572,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 573,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 574,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 575,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 576,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 577,   best chromosome: 0482011140035010005007751836570010978478, smallest error: 305.77673061792035\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 578,   best chromosome: 1482011140035060005007751836570010978478, smallest error: 362.59153663873695\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 579,   best chromosome: 0482011140035060005007751836570010978478, smallest error: 305.77864262537616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 580,   best chromosome: 0482011140035060005007751836570010978478, smallest error: 305.77864262537616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 581,   best chromosome: 0482011140035060005007751836570010978478, smallest error: 305.77864262537616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 582,   best chromosome: 0482011140035060005007751836570010978478, smallest error: 305.77864262537616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 583,   best chromosome: 0482011140035060005007751836570010978478, smallest error: 305.77864262537616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 584,   best chromosome: 0482011140035060005007751836570010978478, smallest error: 305.77864262537616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 585,   best chromosome: 1402011140035060005007751836570010978478, smallest error: 357.3236412919775\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 586,   best chromosome: 1402011140035060005007751836570010978478, smallest error: 357.3236412919775\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 587,   best chromosome: 0402011140035060005007751836570010978478, smallest error: 306.18451652507025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 588,   best chromosome: 0402011140035060005007751836570010978478, smallest error: 306.18451652507025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 589,   best chromosome: 0402011140035060005007751836570010978478, smallest error: 306.18451652507025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 590,   best chromosome: 0402011140035060005007751836570010978478, smallest error: 306.18451652507025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 591,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 592,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 593,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 594,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 595,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 596,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 597,   best chromosome: 0402011140035000005007751836570010978478, smallest error: 306.1823151721883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 598,   best chromosome: 0402012140035000005007751836570010978478, smallest error: 306.1823051229808\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 599,   best chromosome: 0402012140035000005007751836570010978478, smallest error: 306.1823051229808\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 600,   best chromosome: 1402012140035000005007751836570010978478, smallest error: 357.3447405026086\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 601,   best chromosome: 0402012140035000005007751832570010978478, smallest error: 306.1822910125086\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 602,   best chromosome: 0402012140035000005007751832570010978478, smallest error: 306.1822910125086\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 603,   best chromosome: 0402012147035000005007751832570010978478, smallest error: 306.182290942162\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 604,   best chromosome: 0402012147035000805007751432570010978478, smallest error: 306.1810292522966\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 605,   best chromosome: 0432012147035000805007751432570010978478, smallest error: 305.9358020051744\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 606,   best chromosome: 0432012147035000805007751432170010978478, smallest error: 305.9357996767112\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 607,   best chromosome: 0432012147035000805007751432170010978478, smallest error: 305.9357996767112\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 608,   best chromosome: 0432012147035000805007751432170010978470, smallest error: 305.9357993471858\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 609,   best chromosome: 0432012147035000805007751432170010978470, smallest error: 305.9357993471858\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 610,   best chromosome: 0432012147035000805007751432170010978470, smallest error: 305.9357993471858\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 611,   best chromosome: 0432012147035000805007751432170010978470, smallest error: 305.9357993471858\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 612,   best chromosome: 1432012147035000805007751432170010978470, smallest error: 359.29364017071833\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 613,   best chromosome: 0432012147035000805007751432160010978470, smallest error: 305.93579928897844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 614,   best chromosome: 0432012147035000705007751432160010978470, smallest error: 305.9357953147545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 615,   best chromosome: 0432012147035000705007751432160010978470, smallest error: 305.9357953147545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 616,   best chromosome: 0432012147035000705007751432160010978470, smallest error: 305.9357953147545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 617,   best chromosome: 0432012147035000705007751432160010978470, smallest error: 305.9357953147545\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 618,   best chromosome: 0432082147035000705007751432160010978470, smallest error: 305.93538002514913\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 619,   best chromosome: 0432089147035000700007751432160010978470, smallest error: 305.9353382974\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 620,   best chromosome: 0432089147035000700007751432160010978470, smallest error: 305.9353382974\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 621,   best chromosome: 0432089147035000700007751432160010978470, smallest error: 305.9353382974\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 622,   best chromosome: 0432089147035000700007751432160010978470, smallest error: 305.9353382974\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 623,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 624,   best chromosome: 1439089147035000700007751432160010978470, smallest error: 359.7606261631048\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 625,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 626,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 627,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 628,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 629,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 630,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 631,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 632,   best chromosome: 0439089147035000700007751432160010978470, smallest error: 305.89485976051265\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 633,   best chromosome: 0439789147035000700007751432160010978470, smallest error: 305.89101523136515\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 634,   best chromosome: 1439789147035006700007751432160010978470, smallest error: 359.80455414981947\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 635,   best chromosome: 0439789147035006700007751432160010978470, smallest error: 305.8912500064559\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 636,   best chromosome: 0439789147035006700007751432160010978470, smallest error: 305.8912500064559\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 637,   best chromosome: 0439789547035006700007751432160010978470, smallest error: 305.8912478366354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 638,   best chromosome: 0439789547035006700007751432160010978470, smallest error: 305.8912478366354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 639,   best chromosome: 0439789547035006700007751432160010978470, smallest error: 305.8912478366354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 640,   best chromosome: 0439789547035006700007751432160010978470, smallest error: 305.8912478366354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 641,   best chromosome: 0439789547035006700007751432160010978470, smallest error: 305.8912478366354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 642,   best chromosome: 1439789547035006700007751432110010978470, smallest error: 359.8045764708595\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 643,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 644,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 645,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 646,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 647,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 648,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 649,   best chromosome: 0439789547035006700007751432110010978470, smallest error: 305.89124751905587\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 650,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 651,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 652,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 653,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 654,   best chromosome: 1439789547035006700007751432110010973470, smallest error: 359.7895572986946\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 655,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 656,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 657,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 658,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 659,   best chromosome: 0439789547035006700007751432110010973470, smallest error: 305.8909324450319\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 660,   best chromosome: 0439789547035006700007751422110010973470, smallest error: 305.8908694440636\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 661,   best chromosome: 0439789547035006700007751422010010973470, smallest error: 305.8908688140398\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 662,   best chromosome: 0439789547035006700007751422010010973470, smallest error: 305.8908688140398\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 663,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 664,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 665,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 666,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 667,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 668,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 669,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 670,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 671,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 672,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 673,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 674,   best chromosome: 0439789547035006700007751422010010963470, smallest error: 305.8903407423311\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 675,   best chromosome: 0439789947035006700007751422210010963430, smallest error: 305.8903378437729\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 676,   best chromosome: 0439789947035006780007751422210010963430, smallest error: 305.8903413916357\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 677,   best chromosome: 0439789947035006780007751422210010963430, smallest error: 305.8903413916357\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 678,   best chromosome: 0439789947035006780007751422210010963430, smallest error: 305.8903413916357\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 679,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 680,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 681,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 682,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 683,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 684,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 685,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 686,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 687,   best chromosome: 0439789947035006780207751422210010963430, smallest error: 305.89034140047863\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 688,   best chromosome: 0439789977035006780207751422210010963430, smallest error: 305.89034123740964\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 689,   best chromosome: 0439789977035006780207751422210010963430, smallest error: 305.89034123740964\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 690,   best chromosome: 0439789977035006780207751422210010963430, smallest error: 305.89034123740964\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 691,   best chromosome: 1439789977035006780207751422210010963430, smallest error: 359.7587167949231\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 692,   best chromosome: 0439789977035006780007751422210010963430, smallest error: 305.89034122856594\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 693,   best chromosome: 0439789977035006780007751422210010963430, smallest error: 305.89034122856594\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 694,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 695,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 696,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 697,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 698,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 699,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 700,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 701,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 702,   best chromosome: 0439789977035006180007751422210010963430, smallest error: 305.8903146196485\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 703,   best chromosome: 0439789977035006186007751422210010963430, smallest error: 305.8903148857569\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 704,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 705,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 706,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 707,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 708,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 709,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 710,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 711,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 712,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 713,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 714,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 715,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 716,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 717,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 718,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 719,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 720,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 721,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 722,   best chromosome: 0439789977035006186007751022210010963430, smallest error: 305.88795116521\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 723,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 724,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 725,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 726,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 727,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 728,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 729,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 730,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 731,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 732,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 733,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 734,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 735,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 736,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 737,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 738,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 739,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 740,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 741,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 742,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 743,   best chromosome: 0439789977035006186007750022210010963430, smallest error: 305.8835318537375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 744,   best chromosome: 0489789977035006186007750022210010963430, smallest error: 305.758550035363\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 745,   best chromosome: 0489789977035006186007750022210010963430, smallest error: 305.758550035363\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 746,   best chromosome: 0489789977035006181007750022210010963430, smallest error: 305.75854955862263\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 747,   best chromosome: 0489789977035006181007750022210010963430, smallest error: 305.75854955862263\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 748,   best chromosome: 0489789976035006181007750022210010963430, smallest error: 305.7585495574241\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 749,   best chromosome: 0489789976035006181007750022210010963430, smallest error: 305.7585495574241\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 750,   best chromosome: 0489789966035006181007750022210010963430, smallest error: 305.75854954544525\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 751,   best chromosome: 0489789966035006181007750022210010943430, smallest error: 305.7550870036703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 752,   best chromosome: 0489789966035006181007750022210010943430, smallest error: 305.7550870036703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 753,   best chromosome: 0489789966035006181007750022210010943430, smallest error: 305.7550870036703\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 754,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 755,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 756,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 757,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 758,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 759,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 760,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 761,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 762,   best chromosome: 0489784966035006181007750022210010943430, smallest error: 305.75508182285125\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 763,   best chromosome: 0489784926035006181007750022210010943430, smallest error: 305.75508178140484\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 764,   best chromosome: 0489784926035006181007750022210010943430, smallest error: 305.75508178140484\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 765,   best chromosome: 0489784926035006181007750022210010943430, smallest error: 305.75508178140484\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 766,   best chromosome: 0489784926035006181007750022210010943430, smallest error: 305.75508178140484\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 767,   best chromosome: 0489784926035006181007750022210010943430, smallest error: 305.75508178140484\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 768,   best chromosome: 0489784926035006181007750022210010943430, smallest error: 305.75508178140484\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 769,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 770,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 771,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 772,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 773,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 774,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 775,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 776,   best chromosome: 0489784926035006181007750022210010923430, smallest error: 305.75362965034856\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 777,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 778,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 779,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 780,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 781,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 782,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 783,   best chromosome: 0489784926035006181007750012210010923430, smallest error: 305.7535926473804\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 784,   best chromosome: 0489784956035006181007750012200010923430, smallest error: 305.75359263612467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 785,   best chromosome: 0489784956035006181007750012200010923430, smallest error: 305.75359263612467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 786,   best chromosome: 0489784956035006181007750012200010923430, smallest error: 305.75359263612467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 787,   best chromosome: 0489784956035006181007750012200010923430, smallest error: 305.75359263612467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 788,   best chromosome: 0489784956035006181007750012200010923430, smallest error: 305.75359263612467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 789,   best chromosome: 0489784956035006181007750012200010923430, smallest error: 305.75359263612467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 790,   best chromosome: 0489784956035006181007750083200010923430, smallest error: 305.75385535515016\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 791,   best chromosome: 0489784956035006171007750083200010923430, smallest error: 305.753853521353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 792,   best chromosome: 0489784956035006171007750083200010923430, smallest error: 305.753853521353\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 793,   best chromosome: 0489784956035000171007750083200010923430, smallest error: 305.7527558431708\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 794,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 795,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 796,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 797,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 798,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 799,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 800,   best chromosome: 1489704956035000171007750083200010923430, smallest error: 362.81439265735054\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 801,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 802,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 803,   best chromosome: 0489704956035000171007750083200010923430, smallest error: 305.7526871382472\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 804,   best chromosome: 0486704956035000171007750083200010923430, smallest error: 305.7502774283136\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 805,   best chromosome: 0486704956035000171007750083200010923430, smallest error: 305.7502774283136\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 806,   best chromosome: 0486704956035000971007750083200010923430, smallest error: 305.7504243704449\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 807,   best chromosome: 0486704956035000971007750083200010923430, smallest error: 305.7504243704449\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 808,   best chromosome: 0486704956035000971007750083200010923430, smallest error: 305.7504243704449\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 809,   best chromosome: 0486704953035000171007750083200010923430, smallest error: 305.7502774261442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 810,   best chromosome: 0486704953035000171007750083200010923430, smallest error: 305.7502774261442\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 811,   best chromosome: 0486704950035000171007750083200010923430, smallest error: 305.75027742397685\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 812,   best chromosome: 0486704950035000171007750083200010923430, smallest error: 305.75027742397685\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 813,   best chromosome: 0486704950035000171007750083200010920430, smallest error: 305.7501519509327\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 814,   best chromosome: 0486703950035000171007750083200010920430, smallest error: 305.7501512283266\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 815,   best chromosome: 0486703950035000171007750083200010920430, smallest error: 305.7501512283266\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 816,   best chromosome: 0480703950035000171007750083200010924430, smallest error: 305.7497993349347\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 817,   best chromosome: 0480703950035000171007750083200010924430, smallest error: 305.7497993349347\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 818,   best chromosome: 0480703950035000171007750083200010924430, smallest error: 305.7497993349347\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 819,   best chromosome: 0480703950035000171007750083200010924430, smallest error: 305.7497993349347\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 820,   best chromosome: 0480703950035000171007750081200010924430, smallest error: 305.7497941440814\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 821,   best chromosome: 0480703950035000171007750081200010924430, smallest error: 305.7497941440814\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 822,   best chromosome: 0480703950035000171007750081200010924430, smallest error: 305.7497941440814\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 823,   best chromosome: 0480703950035000171007750081200010924430, smallest error: 305.7497941440814\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 824,   best chromosome: 0480703950035000171007750061200010924430, smallest error: 305.74974223532723\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 825,   best chromosome: 0480703950035000171007750061200010924430, smallest error: 305.74974223532723\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 826,   best chromosome: 0480703950035000171007750061200010924430, smallest error: 305.74974223532723\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 827,   best chromosome: 0480703950035000171007750061200010924430, smallest error: 305.74974223532723\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 828,   best chromosome: 1480703950035000171007750061200010924430, smallest error: 362.2266403159255\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 829,   best chromosome: 0480703950035000171007750061200010924430, smallest error: 305.74974223532723\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 830,   best chromosome: 0480703952035000171007750011200010924430, smallest error: 305.7496173542317\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 831,   best chromosome: 0480703952035000171007750011200010924430, smallest error: 305.7496173542317\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 832,   best chromosome: 0480703952035000171007750011200010924430, smallest error: 305.7496173542317\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 833,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 834,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 835,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 836,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 837,   best chromosome: 1480703952035000151007750011200010924430, smallest error: 362.2229551331054\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 838,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 839,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 840,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 841,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 842,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 843,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 844,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 845,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 846,   best chromosome: 0480703952035000151007750011200010924430, smallest error: 305.749613710335\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 847,   best chromosome: 0480703952035000151007750911200010924430, smallest error: 305.75405141354435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 848,   best chromosome: 0480703952035000151007750911200010924430, smallest error: 305.75405141354435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 849,   best chromosome: 0480703952035000151007750911200010924430, smallest error: 305.75405141354435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 850,   best chromosome: 0480703952035000151007750911200010924430, smallest error: 305.75405141354435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 851,   best chromosome: 0480703952035000151007750911200010924330, smallest error: 305.75403588836576\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 852,   best chromosome: 0480703952035000151007750911200010924330, smallest error: 305.75403588836576\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 853,   best chromosome: 0480703952035000151007750911200010924330, smallest error: 305.75403588836576\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 854,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 855,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 856,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 857,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 858,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 859,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 860,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 861,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 862,   best chromosome: 0480713952035000151007750911200010924330, smallest error: 305.75403270272164\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 863,   best chromosome: 0480713952035000151007750511200010924330, smallest error: 305.751438841309\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 864,   best chromosome: 0480713952035000151007750511200010924330, smallest error: 305.751438841309\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 865,   best chromosome: 0480713952035000151007950511200010924330, smallest error: 334.41628759674563\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 866,   best chromosome: 0480713952038000151007950511200010924330, smallest error: 325.8475320326135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 867,   best chromosome: 0480713952038000151007950511200010924330, smallest error: 325.8475320326135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 868,   best chromosome: 0480713952038000151007950511200010924330, smallest error: 325.8475320326135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 869,   best chromosome: 0480713952038000151007950511200010924330, smallest error: 325.8475320326135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 870,   best chromosome: 0480713952038000151007930511200010924330, smallest error: 321.8578615578726\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 871,   best chromosome: 0480713952038000151007930511200010924330, smallest error: 321.8578615578726\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 872,   best chromosome: 0480713952038000151007930511200010924330, smallest error: 321.8578615578726\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 873,   best chromosome: 0480713952038000151007930511200010924330, smallest error: 321.8578615578726\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 874,   best chromosome: 0480713952038000151007930511200010924330, smallest error: 321.8578615578726\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 875,   best chromosome: 0480713952038000151007910511200010924330, smallest error: 318.17150900530356\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 876,   best chromosome: 0480713952038000141007910511200010924330, smallest error: 318.1715305726172\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 877,   best chromosome: 0480713952038000141007910511200010924330, smallest error: 318.1715305726172\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 878,   best chromosome: 0480713952038000141007910511200010904330, smallest error: 318.06506603800756\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 879,   best chromosome: 0480713952038000141007910511200010904330, smallest error: 318.06506603800756\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 880,   best chromosome: 0480713952038000741007910511200010904330, smallest error: 318.0637742558355\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 881,   best chromosome: 0480713952038000741007910511200010904330, smallest error: 318.0637742558355\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 882,   best chromosome: 0480713057038000741007910511200010904330, smallest error: 318.0637534125562\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 883,   best chromosome: 0480713057038000741007910511200010904330, smallest error: 318.0637534125562\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 884,   best chromosome: 0480713057038000741007910511200010904330, smallest error: 318.0637534125562\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 885,   best chromosome: 0480713057038000741007910511200010904330, smallest error: 318.0637534125562\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 886,   best chromosome: 0480713057038000741007910511200010904231, smallest error: 318.06322710934234\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 887,   best chromosome: 0480713057038000741007910511200010904231, smallest error: 318.06322710934234\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 888,   best chromosome: 0480713057038000741007910511200010904231, smallest error: 318.06322710934234\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 889,   best chromosome: 0480713057038000741007910511200010904231, smallest error: 318.06322710934234\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 890,   best chromosome: 0480713057038000741007910511200010904231, smallest error: 318.06322710934234\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 891,   best chromosome: 0430713057038000741007910511200010904231, smallest error: 317.13732084649615\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 892,   best chromosome: 0430713057038000741007910511200010904231, smallest error: 317.13732084649615\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 893,   best chromosome: 0430713057038000741407910511200010904231, smallest error: 317.13731998983735\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 894,   best chromosome: 0430713057038000741407910511200010904231, smallest error: 317.13731998983735\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 895,   best chromosome: 0430713057038000742407910511200010904231, smallest error: 317.137317848156\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 896,   best chromosome: 0430713057038000742407910511200010900231, smallest error: 317.11723159019346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 897,   best chromosome: 0430713057038000742407910511200010900231, smallest error: 317.11723159019346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 898,   best chromosome: 0430713057038000742407910511200010900231, smallest error: 317.11723159019346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 899,   best chromosome: 0330713057038000742407910511200010900231, smallest error: 316.552340378377\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 900,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 901,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 902,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 903,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 904,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 905,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 906,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 907,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 908,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 909,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 910,   best chromosome: 0330713057038000742407910111200010900231, smallest error: 316.4900591098064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 911,   best chromosome: 0330713057038000749407910111200010900231, smallest error: 316.490044107642\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 912,   best chromosome: 0330713057038000749407910111200010900231, smallest error: 316.490044107642\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 913,   best chromosome: 0330713057038000749407910111200010900231, smallest error: 316.490044107642\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 914,   best chromosome: 0330713057038000749407910111200010900231, smallest error: 316.490044107642\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 915,   best chromosome: 0330713057038000749407910111200010900231, smallest error: 316.490044107642\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 916,   best chromosome: 0330713057038000749407910111200010900231, smallest error: 316.490044107642\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 917,   best chromosome: 0330713057038000749407910111200010200231, smallest error: 313.59932076932427\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 918,   best chromosome: 0330713057038000749407910111200010200231, smallest error: 313.59932076932427\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 919,   best chromosome: 0330713057038000749407910111200010200231, smallest error: 313.59932076932427\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 920,   best chromosome: 0330763057038000749407910111200010200231, smallest error: 313.59903263435143\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 921,   best chromosome: 0331763057038000749407910111200010200231, smallest error: 313.5932945204895\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 922,   best chromosome: 0331763057038000749407910111200010200231, smallest error: 313.5932945204895\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 923,   best chromosome: 0331763057038000749407910111200010200231, smallest error: 313.5932945204895\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 924,   best chromosome: 0331763057038000749407910111200010200231, smallest error: 313.5932945204895\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 925,   best chromosome: 0331763057038000749407910111200010200201, smallest error: 313.5931820806495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 926,   best chromosome: 0331763057038000749407910111200010200201, smallest error: 313.5931820806495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 927,   best chromosome: 0331763057038000749407910111200010200201, smallest error: 313.5931820806495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 928,   best chromosome: 0331763057038000749407910111200010200201, smallest error: 313.5931820806495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 929,   best chromosome: 0331763057038000749407910111200010200201, smallest error: 313.5931820806495\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 930,   best chromosome: 0331769057038000749407910111200010200201, smallest error: 313.5931481853463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 931,   best chromosome: 0331769057038000749407910111200010200201, smallest error: 313.5931481853463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 932,   best chromosome: 0331769057038000749407910111200010200201, smallest error: 313.5931481853463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 933,   best chromosome: 0331769057038000749407910111200010200201, smallest error: 313.5931481853463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 934,   best chromosome: 0331769057038000749407910111200010200201, smallest error: 313.5931481853463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 935,   best chromosome: 0331769057038000749407910111200010200201, smallest error: 313.5931481853463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 936,   best chromosome: 0331769857038000749407910111200010200201, smallest error: 313.5931436659712\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 937,   best chromosome: 0331769857038000749407910111200010200201, smallest error: 313.5931436659712\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 938,   best chromosome: 1331769857038000749407910111200010200201, smallest error: 367.6521593505933\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 939,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 940,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 941,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 942,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 943,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 944,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 945,   best chromosome: 0331769847038000749407910111200010200201, smallest error: 313.5931437224638\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 946,   best chromosome: 0337769847038000749407910111200010200201, smallest error: 313.5607260030515\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 947,   best chromosome: 0337769847038000749607910111200010200201, smallest error: 313.5607256357292\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 948,   best chromosome: 0337769847038000749607910111200010200201, smallest error: 313.5607256357292\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 949,   best chromosome: 0337769847038000749607910111200010200201, smallest error: 313.5607256357292\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 950,   best chromosome: 0337769847038000749607910111200010200201, smallest error: 313.5607256357292\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 951,   best chromosome: 0338769847038000749607910111200010200201, smallest error: 313.55632061725225\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 952,   best chromosome: 0388769847038000749607910111200010200201, smallest error: 313.52334936945704\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 953,   best chromosome: 0388769847038000749607910111200010200201, smallest error: 313.52334936945704\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 954,   best chromosome: 0388749847038000749607910111200010200201, smallest error: 313.5232650255429\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 955,   best chromosome: 0388749847038060749607910111200010200201, smallest error: 313.41253540888783\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 956,   best chromosome: 0388749847038060749607910111200010200201, smallest error: 313.41253540888783\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 957,   best chromosome: 0388749847038060749607910111200010200201, smallest error: 313.41253540888783\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 958,   best chromosome: 0388749847038060749607910111200010200201, smallest error: 313.41253540888783\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 959,   best chromosome: 0388749847038060749607910111200010200201, smallest error: 313.41253540888783\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 960,   best chromosome: 0388749847038060749607910111200010200201, smallest error: 313.41253540888783\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 961,   best chromosome: 0388749847038080742607910111200010200201, smallest error: 313.37580942856374\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 962,   best chromosome: 0388749847038080742607910111200010200201, smallest error: 313.37580942856374\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 963,   best chromosome: 0388749847038080742607910111200010200201, smallest error: 313.37580942856374\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 964,   best chromosome: 0388749847038080742607910111200010200201, smallest error: 313.37580942856374\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 965,   best chromosome: 1388749847038080742607910111200010200201, smallest error: 371.3562592576832\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 966,   best chromosome: 0388749847038080744607910111200010200201, smallest error: 313.37580576018706\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 967,   best chromosome: 0388749847038080744607910111200010200201, smallest error: 313.37580576018706\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 968,   best chromosome: 0388749847038080744607910111200010200201, smallest error: 313.37580576018706\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 969,   best chromosome: 0388749847038080794607910111200010200201, smallest error: 313.3757140513223\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 970,   best chromosome: 0388749847038080795607910111200010200201, smallest error: 313.37571221711676\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 971,   best chromosome: 0388749847038080795607910111200010200201, smallest error: 313.37571221711676\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 972,   best chromosome: 0388749847038080795607910111200010200201, smallest error: 313.37571221711676\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 973,   best chromosome: 0388749847038080795607910111200010200201, smallest error: 313.37571221711676\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 974,   best chromosome: 0388749847038090795607910111200010200201, smallest error: 313.3574133092785\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 975,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 976,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 977,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 978,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 979,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 980,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 981,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 982,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 983,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 984,   best chromosome: 0388749847038790795607910131200010200201, smallest error: 312.1580610036352\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 985,   best chromosome: 1388749247038790795607910131200010200201, smallest error: 369.87573667000225\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 986,   best chromosome: 0388749247038790795607910131200010200201, smallest error: 312.15805886367866\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 987,   best chromosome: 0388749247038790795607910131200010200201, smallest error: 312.15805886367866\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 988,   best chromosome: 0388749247038790795607910131200010200201, smallest error: 312.15805886367866\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 989,   best chromosome: 0388749247038790795607910131200010200201, smallest error: 312.15805886367866\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 990,   best chromosome: 0388749247038790795607910131200010200201, smallest error: 312.15805886367866\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 991,   best chromosome: 0388749247038790795607910131200010200201, smallest error: 312.15805886367866\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 992,   best chromosome: 0388449247038790795607910131200010200201, smallest error: 312.1569894155052\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 993,   best chromosome: 0388449247038790795607910131200010200201, smallest error: 312.1569894155052\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 994,   best chromosome: 0388449247038790795607910131200010200201, smallest error: 312.1569894155052\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 995,   best chromosome: 1388449247038790795607910131200010200201, smallest error: 369.8555020483076\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 996,   best chromosome: 0388449147038090795607910131200010200201, smallest error: 313.3589280742498\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 997,   best chromosome: 0388449147038090795607910131200010200201, smallest error: 313.3589280742498\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 998,   best chromosome: 0388449147038090795607910131200010200201, smallest error: 313.3589280742498\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 999,   best chromosome: 0388449127038090795607910131200010200201, smallest error: 313.35892799146745\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 1000,   best chromosome: 0388449127038090795607910131200010200201, smallest error: 313.35892799146745\n",
      "smallest_error_history [np.float64(517.5474494265208), np.float64(460.9067522161778), np.float64(460.9067522161778), np.float64(459.9423471137068), np.float64(459.827506670149), np.float64(415.55106245137375), np.float64(352.34232142842774), np.float64(351.60334908299734), np.float64(351.4157536507586), np.float64(351.4157536507586), np.float64(351.3678041768287), np.float64(351.3678041768287), np.float64(329.9372830033418), np.float64(329.9372830033418), np.float64(329.9372830033418), np.float64(329.9372830033418), np.float64(329.9372830033418), np.float64(329.93728107079875), np.float64(329.93728107079875), np.float64(634.4822403992985), np.float64(329.93728107079875), np.float64(329.93728107079875), np.float64(329.93728107079875), np.float64(329.65762053117703), np.float64(329.65762053117703), np.float64(329.65762053117703), np.float64(329.65762053117703), np.float64(329.65762053117703), np.float64(329.63157574903653), np.float64(329.63157574903653), 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",
      "text/plain": [
       "<Figure size 1500x1800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The final best chromosome is 0388449127038090795607910131200010200201\n",
      " Encoded values: lambda_k 0.388449127, lambda_T 0.380907956, lambda_s 0.79101312, lambda_r 0.010200201\n",
      " Total cost =  50000 chromosomes\n",
      " Final error =  313.35892799146745 \n",
      "Execution time: 4 hours, 37 minutes, 57 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Program errorMinimizationForTheUnderdogModel_2.py\n",
    "\n",
    "# This program is designed to fine tune the parameters of the Underdog Model.\n",
    "# We have two genetic algorithms:\n",
    "# A master GA that test assignations of parameters of the Underdog Model,\n",
    "# evolving the assignation of parameters that minimizes the global error\n",
    "# between the predicted best fitness histories and\n",
    "# the observed best fitness histories produced by\n",
    "# the slave genetic algorithm that solves the problem of matching a target sequence of 1s.\n",
    "# The second GA is a slave that tests one assignation of parameters.\n",
    "# To that aim, the same set of parameters is used\n",
    "# to run various GAs over various combinations of population sizes and lengths of target sequences.\n",
    "# The best fitness history of each la Evoluciónary Process is recorded and compared\n",
    "# with the prediction of the Underdog Model.\n",
    "# Summing over all the combinations of population sizes and lengths of target sequences,\n",
    "# we get a total error that must be minimized by the master GA.\n",
    "\n",
    "# The following changes with respect to the previous program were incorporated:\n",
    "# 1. In the previous program, for each assignation,\n",
    "# a new la Evoluciónary Process was run.\n",
    "# Here, we run the first assignation to the full,\n",
    "# memorize the best fitness histories of\n",
    "# the corresponding Slave la Evoluciónary Processes,\n",
    "# and never again run them anew. With this information,\n",
    "# we run the Master GA to optimize parameters for the Underdog Model.\n",
    "# So, only the values of the parameters evolve.\n",
    "# 2. We use a population size of 1000 and 100 generations for the Master GA.\n",
    "# 3. Mutation and recombination rates were given the value 0.01\n",
    "# that causes almost no drift, jumps followed by more jumps that end in no la Evolución,\n",
    "# in gathering small progress.\n",
    "# 4. The possible values of parameters run from 0 to 2.\n",
    "# This kills imaginary numbers and nans and improves efficiency of the code.\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import random\n",
    "import time\n",
    "\n",
    "# Parameters for the Genetic Algorithm\n",
    "# Apart from initialization parameters, which are the same as in the previous program,\n",
    "# the parameters for the grid of population sizes and lengths of target sequences are different,\n",
    "# to test the predictions of the Underdog Model for a wider range of resources and complexity.\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "# Parameters for the SLAVE GENETIC ALGORITHM, which matches a binary string.\n",
    "\n",
    "'''\n",
    "# Small experiment: needs 35 gigabytes, and 6 minutes of CPU\n",
    "# Parameters for the data grid with a smaller range of population sizes \n",
    "# and lengths of target sequences, to test the program before running the extended range.\n",
    "# A grid is defined by the population sizes and the lengths of target sequences.\n",
    "population_sizesk10 = np.array([10, 20, 30])\n",
    "# Complexity is given by the length of the target sequence, \n",
    "# which is the string of 1s that the algorithm has to match.\n",
    "lengths_of_target_sequencesk10 = np.array([500, 1000, 2000])\n",
    "numbersOfGenerationsk10 = np.array([500, 500, 500])  \n",
    "'''\n",
    "\n",
    "# Large experiment: needs 40 GB of RAM and one hour of CPU.\n",
    "# Parameters for the datagrid with a larger range of population sizes and \n",
    "# lengths of target sequences, \n",
    "# to test the predictions of the Underdog Model \n",
    "# for a wider range of resources and complexity.\n",
    "# Resources and parameters for the experiments.\n",
    "# A grid is defined by the population sizes and the lengths of target sequences.\n",
    "population_sizesk10 = np.array([5, 50, 200, 500])\n",
    "# Complexity is given by the length of the target sequence,\n",
    "# which is the string of 1s that the algorithm has to match.\n",
    "lengths_of_target_sequencesk10 = np.array([50, 500, 1500, 2500, 5000, 10000])\n",
    "numbersOfGenerationsk10 = np.array([1000, 1000, 1000, 1000, 1000, 1000])\n",
    "\n",
    "mutation_rate = 0.01\n",
    "recombination_rate = 0.01\n",
    "binary_alphabet = '01'\n",
    "include_clone_populationk10 = True\n",
    "include_mutate_allk10 = True\n",
    "include_crossover_allk10 = True\n",
    "print_best_fitnessk10 = False\n",
    "include_draw_all_historiesk10 = False\n",
    "show_graphk10 = False\n",
    "\n",
    "# Parameters for the UNDERDOG MODEL, which predicts the behavior of the slave genetic algorithm.\n",
    "c = 1.29\n",
    "# k = 1.5 # good fitting for m = 200, H = 800\n",
    "# k =1.6 #change is irrelevant\n",
    "k = 1.22  # Almost perfect for m = 30, H = 800\n",
    "# $\\mu = $ mutation rate, constant\n",
    "mu = mutation_rate  # Example mutation rate\n",
    "# Initial, seed values of parameters\n",
    "lambda_k_o = ( 1.0 / k) ** 0.5  # This parameter allows to adjust the initial number of matched sites I, by multiplying the second term of the formula of I, which is the variability in the number of matched sites.\n",
    "lambda_T_o = c ** 0.5  # This parameter allows to adjust the number of touched sites T, by multiplying the second term of the formula of T, which is the variability in the number of touched sites.\n",
    "lambda_s_o = 0.78  # Observed good value in the previous program\n",
    "lambda_r_o = 0.015  # This parameter allows to adjust the number of right untouched sites r, by multiplying the second term of the formula of r, which is the variability in the number of right untouched sites.\n",
    "\n",
    "# Parameters for the MASTER GENETIC ALGORITHM,\n",
    "# which fine tunes the parameters of the Underdog Model\n",
    "# by minimizing the error between the predicted best fitness histories and\n",
    "# the observed best fitness histories in the simulations.\n",
    "number_of_generationsjk10 = 1000\n",
    "digit_alphabet = '0123456789'\n",
    "length_of_chromosomesjk10 = 10\n",
    "population_sizejk10 = 50\n",
    "# Include cloning of the fittest\n",
    "include_clone_populationjk10 = True\n",
    "# Include mutation\n",
    "include_mutate_alljk10 = True\n",
    "# Include recombination\n",
    "include_crossover_alljk10 = True\n",
    "mutation_ratejk10 = 0.01\n",
    "recombination_ratejk10 = 0.01\n",
    "\n",
    "# Boolean flag to print the generation, best chromosome and fitness.\n",
    "# This is the ancestral line of the solution.\n",
    "print_best_fitness = False\n",
    "print_parameters = True\n",
    "show_graph = True\n",
    "\n",
    "\n",
    "def print_parameters_for_slave_GAk10():\n",
    "    print(f\"Lab: Two Genetic Algorithms, one master, the other slave\")\n",
    "    print(f\"Purpose: Use la Evolución to fine tune the parameters of the Underdog Model.\")\n",
    "    print(f\"The slave GA solves the problem of matching a target sequence of 1s,\"\n",
    "          + f\" and produces best fitness histories for different combinations \"\n",
    "          + f\" of population sizes and lengths of target sequences.\")\n",
    "    print(f\"Experiment {start_time}\")\n",
    "    print(f\"Parameters for the slave GA, which solves the problem of matching a target sequence of 1s:\")\n",
    "    print(f\"binary_alphabet = {binary_alphabet}\")\n",
    "    print(f\"lengths_of_target_sequences = {lengths_of_target_sequencesk10}\")\n",
    "    print(f\"population_sizes = {population_sizesk10}\")\n",
    "    print(f\"include_clone_population = {include_clone_populationk10}\")\n",
    "    print(f\"include_mutate_all = {include_mutate_allk10}\")\n",
    "    print(f\"include_crossover_all = {include_crossover_allk10}\")\n",
    "    if include_mutate_allk10:\n",
    "        print(f\"mutation_rate = {mutation_rate}\")\n",
    "    if include_crossover_allk10:\n",
    "        print(f\"recombination_rate = {recombination_rate}\")\n",
    "\n",
    "\n",
    "def print_parameters_for_underdog_modelk10():\n",
    "    print(\"Parameters of the theoretical Underdog Model\")\n",
    "    print(\"The Underdog Model predicts the behavior of the slave genetic algorithm, \" \n",
    "          \"which solves the problem of matching a target sequence of 1s. \" \n",
    "          \"The initial values of parameters are:\")\n",
    "    print(f\"c = {c}\")\n",
    "    print(f\"k = {k}\")\n",
    "    print(f\"lambda_k = {lambda_k_o}\")\n",
    "    print(f\"lambda_T = {lambda_T_o}\")\n",
    "    print(f\"lambda_s = {lambda_s_o}\")\n",
    "    print(f\"lambda_r = {lambda_r_o}\")\n",
    "    print(f\"mu = {mu}\")\n",
    "\n",
    "\n",
    "def print_parameters_for_master_GAk10():\n",
    "    print(f\"Parameters for the master GA.\")\n",
    "    print(f\"The master GA fine tunes the parameters of the Underdog Model by\"\n",
    "          + f\" minimizing the error between the predicted best fitness histories \"\n",
    "          + f\"and the observed best fitness histories in the simulations of the slave GA, \"\n",
    "          + f\"which solves the problem of matching a target sequence of 1s.\")\n",
    "    print(f\"number_of_generations {number_of_generationsjk10}\")\n",
    "    print(f\"digit_alphabet = {digit_alphabet}\")\n",
    "    print(f\"length_of_chromosomesjk10 = {length_of_chromosomesjk10}\")\n",
    "    print(f\"population_sizejk10 = {population_sizejk10}\")\n",
    "    print(f\"include_clone_populationjk10 = {include_clone_populationjk10}\")\n",
    "    print(f\"include_mutate_alljk10 = {include_mutate_alljk10}\")\n",
    "    print(f\"include_crossover_alljk10 = {include_crossover_alljk10}\")\n",
    "    if include_mutate_alljk10:\n",
    "        print(f\"mutation_ratejk10 = {mutation_ratejk10}\")\n",
    "    if include_crossover_alljk10:\n",
    "        print(f\"recombination_ratejk10 = {recombination_ratejk10}\")\n",
    "\n",
    "\n",
    "# *****************************\n",
    "# SLAVE GENETIC ALGORITHM THAT SOLVES THE PROBLEM OF\n",
    "# MATCHING A TARGET SEQUENCE OF 1s.\n",
    "# *****************************\n",
    "\n",
    "# The following functions are used to implement the genetic algorithm,\n",
    "# which is a search heuristic that mimics the process of natural selection.\n",
    "\n",
    "\n",
    "def create_populationk10(population_size, length_of_target_sequence, alphabet):\n",
    "    \"\"\"Creates an initial population of random chromosomes.\"\"\"\n",
    "    # print(\"create_populationk10**\")\n",
    "    # print(f\"alphabet: {alphabet}, length of target sequence: {length_of_target_sequence}\")\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        chromosome = ''.join(random.choice(alphabet) for _ in range(length_of_target_sequence))\n",
    "        population.append(chromosome)\n",
    "    return population\n",
    "\n",
    "\n",
    "def create_perfect_populationk10(population_size, target_sequence):\n",
    "    \"\"\"Creates an initial population of perfect chromosomes.\"\"\"\n",
    "    population = []\n",
    "    for _ in range(population_size):\n",
    "        chromosome = target_sequence\n",
    "        population.append(chromosome)\n",
    "    return population\n",
    "\n",
    "\n",
    "def calculate_fitnessk10(chromosome, target_sequence):\n",
    "    \"\"\"Calculates the fitness of a chromosome.\"\"\"\n",
    "    # print(f\"target sequence: {target_sequence}, Chromosome: {chromosome}, \")\n",
    "    fitness = sum(1 for c, t in zip(chromosome, target_sequence) if c == t)\n",
    "    return fitness\n",
    "\n",
    "\n",
    "def calculate_all_fitnessk10(population, target_sequence):\n",
    "    \"\"\"Calculates the fitness of all chromosomes in the population.\"\"\"\n",
    "    fitness_scores = []\n",
    "    for chromosome in population:\n",
    "        fitness = calculate_fitnessk10(chromosome, target_sequence)\n",
    "        fitness_scores.append(fitness)\n",
    "    return fitness_scores\n",
    "\n",
    "\n",
    "def select_the_bestk10(population, target_sequence):\n",
    "    \"\"\"Selects the best chromosome from the population.\"\"\"\n",
    "    fitness_scores = calculate_all_fitnessk10(population, target_sequence)\n",
    "    best_fitness = max(fitness_scores)\n",
    "    best_chromosome = population[fitness_scores.index(best_fitness)]\n",
    "    return best_chromosome, best_fitness\n",
    "\n",
    "\n",
    "def clone_populationk10(chromosome, population_size):\n",
    "    \"\"\"Clones a chromosome to create a new population.\"\"\"\n",
    "    new_population = [chromosome] * population_size\n",
    "    return new_population\n",
    "\n",
    "\n",
    "def mutatek10(chromosome):\n",
    "    \"\"\"Mutates a chromosome with a given mutation rate.\"\"\"\n",
    "    # print(\"mutatek10**\")\n",
    "    # print(f\"mutation rate: {mutation_rate}, Chromosome: {chromosome}, \")\n",
    "    counter = 0\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "            mutated_chromosome_list[i] = random.choice(binary_alphabet)\n",
    "            counter += 1\n",
    "    return \"\".join(mutated_chromosome_list), counter\n",
    "\n",
    "\n",
    "def mutate_allk10(population):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome, counter = mutatek10(chromosome)\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "    return mutated_population\n",
    "\n",
    "\n",
    "def crossoverk10(parent1, parent2, length_of_target_sequence):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, length_of_target_sequence)\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "\n",
    "def crossover_allk10(population, length_of_target_sequence):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 = population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverk10(parent1, parent2, length_of_target_sequence)\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "def run_one_generationk10(population_size, population, target_sequence, length_of_target_sequence):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    best_chromosome, best_fitness = select_the_bestk10(population, target_sequence)\n",
    "    new_population = clone_populationk10(best_chromosome, population_size)\n",
    "    if include_mutate_allk10:\n",
    "        new_population = mutate_allk10(new_population)\n",
    "    if include_crossover_allk10:\n",
    "        new_population = crossover_allk10(new_population, length_of_target_sequence)\n",
    "    best_chromosome, best_fitness = select_the_bestk10(new_population, target_sequence)\n",
    "    return best_chromosome, best_fitness, new_population\n",
    "\n",
    "\n",
    "def plot_Best_Fitnessk10(best_fitness_history, population_size, length_of_target_sequence):\n",
    "    \"\"\"Plots the best fitness history over generations.\"\"\"\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(best_fitness_history,\n",
    "             label=f'Population size {population_size}, Target length {length_of_target_sequence}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title('Best Fitness History over Generations')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def oneEvolucionaryProcessk10(population_size, numberOfGenerations, length_of_target_sequence, target_sequence):\n",
    "    \"\"\"Runs one la Evoluciónary process and returns the normalized best fitness history.\"\"\"\n",
    "    # print(\"onela EvoluciónaryProcessk10**\")\n",
    "    population = create_populationk10(population_size, length_of_target_sequence, binary_alphabet)\n",
    "    generation = 0\n",
    "    # print(f\"Initial population: {population}, target sequence: {target_sequence}\")\n",
    "    best_chromosome, best_fitness = select_the_bestk10(population, target_sequence)\n",
    "    # print(f\"Initial best fitness: {best_fitness}\")\n",
    "    best_fitness_history = [best_fitness]\n",
    "\n",
    "    # print(f\"population_size: {population_size}, numberOfGenerations: {numberOfGenerations}, length_of_target_sequence: {length_of_target_sequence}\")\n",
    "    while generation < numberOfGenerations:\n",
    "        best_chromosome, best_fitness, population = run_one_generationk10(population_size, population, target_sequence,\n",
    "                                                                         length_of_target_sequence)\n",
    "        generation += 1\n",
    "        # print_best_fitnessk10 = True\n",
    "        if print_best_fitnessk10:\n",
    "            print(f\"Generation {generation}: Best Fitness = {best_fitness}\")\n",
    "        best_fitness_history.append(best_fitness)\n",
    "    normalized_best_fitness_history = [fitness / length_of_target_sequence for fitness in best_fitness_history]\n",
    "    if show_graphk10:\n",
    "        plot_Best_Fitnessk10(best_fitness_history, population_size, length_of_target_sequence)\n",
    "    return normalized_best_fitness_history\n",
    "\n",
    "\n",
    "def draw_all_historiesk10(normalized_best_fitness_histories):\n",
    "    \"\"\"Draws all the normalized best fitness histories together.\"\"\"\n",
    "    plt.figure(figsize=(10,12))\n",
    "    for i in range(len(population_sizesk10)):\n",
    "        for j in range(len(lengths_of_target_sequencesk10)):\n",
    "            plt.plot(normalized_best_fitness_histories[i][j],\n",
    "                     label=f'Population size {population_sizesk10[i]}, Target length {lengths_of_target_sequencesk10[j]}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Normalized Best Fitness')\n",
    "    plt.title(\"Normalized Best Fitness History over Generations\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ************************\n",
    "# THE UNDERDOG MODEL PREDICTION OF\n",
    "# THE BEST FITNESS ALONG AN la EvoluciónARY PATH\n",
    "# FOR EACH LENGTH OF TARGET SEQUENCE AND POPULATION SIZE.\n",
    "# ************************\n",
    "\n",
    "# Mathematical definitions of the Underdog Model,\n",
    "# which predicts the best fitness along an la Evoluciónary path\n",
    "# for a given length of target sequence and population size,\n",
    "# based on the mutation rate and four parameters\n",
    "# that are related to the selection processes of the algorithm.\n",
    "# $\\mu = $ mutation rate, constant\n",
    "#\n",
    "# $H = $ length of the target string, total number of sites to be solved by la Evolución.\n",
    "#\n",
    "# $m = $ population size, number of binomial distributions, or of monkeys, or of chromosomes that propose each one a possible solution.\n",
    "#\n",
    "# $M_t = $ number of solved sites at generation $t$\n",
    "#\n",
    "# $I = M_o = $ initial number of matched sites\n",
    "# >>\n",
    "# >> $ = \\frac{H}{2} +  \\sqrt{\\frac{H}{k}\\ln m  }  $.\n",
    "# >>\n",
    "# >> $ = \\frac{H}{2} +  \\lambda_k\\sqrt{H \\ln m  }  $.\n",
    "# >>\n",
    "# > $T= $ number of touched sites $ = \\mu H +  \\lambda_T \\sqrt{H \\mu (1 - \\mu) } \\sqrt{ \\ln (m)} $\n",
    "# >\n",
    "# $ s = $ number of solved sites among touched. It is given by\n",
    "# >\n",
    "# $ s =  \\frac{T}{2} + \\lambda_s \\sqrt{T\\ln m  }  $. It is a constant and a population effect.\n",
    "# >\n",
    "# > $H-T = $  number of untouched sites\n",
    "# >\n",
    "# > $p_t = \\frac{M_t}{H}$ proportion of matched sites in the winner chromosome of present generation.\n",
    "# >\n",
    "# > $ r = $ number of untouched sites that were right in the winning chromosome of generation $t$.\n",
    "# >\n",
    "# > $ r =  (H-T)p_t +  \\lambda_r \\sqrt{(H-T)p_t(1-p_t)} \\sqrt{ \\ln (m)} $.\n",
    "# >\n",
    "# > The prediction for the number of solved sites in the winner chromosome for the next generation is given by\n",
    "# >\n",
    "# > $M_{t+1} =  s + r = $ prediction for the winner of the next generation\n",
    "# >\n",
    "# > The parameters that la Evolución must specify are 4: $\\lambda_k$, $\\lambda_T$, $\\lambda_s$, $\\lambda_r$.\n",
    "\n",
    "# Implementation of the Underdog Model prediction for\n",
    "# the best fitness along an la Evoluciónary path\n",
    "\n",
    "# Definition of parameters for the underdog model\n",
    "\n",
    "# n_generations = generation # number of generations to make predictions by the model\n",
    "\n",
    "\n",
    "###\n",
    "# $T= $ number of touched sites $ =  \\mu H +  \\sqrt{H \\mu (1 - \\mu) } \\sqrt{c \\ln (m)} $\n",
    "def T( H, m, lambda_T):\n",
    "    T_value = mu * H + lambda_T * ((H * mu * (1 - mu)) ** 0.5) * ((np.log(m)) ** 0.5)\n",
    "    # print (f\"def_T: mu {mu}, H {H}, m {m}, lambda_T {lambda_T}, t_value {T_value}\")\n",
    "    return T_value\n",
    "\n",
    "\n",
    "#  $ s = $ number of solved sites among touched $ =  \\frac{T}{2} +  \\sqrt{\\frac{T}{k}\\ln m  }  $\n",
    "def s(m,  H, lambda_T, lambda_s):\n",
    "    T_value = T( H, m, lambda_T)\n",
    "    s_value = T_value / 2 + lambda_s * (T_value * np.log(m)) ** 0.5\n",
    "    return s_value\n",
    "\n",
    "\n",
    "def r(H, p_n, m,   lambda_T, lambda_r):\n",
    "    T_value = T(H, m, lambda_T) + 0\n",
    "    if p_n > 1.0:\n",
    "        p_n = 1.0\n",
    "    crucial_term = ((H - T_value) * p_n * (1 - p_n))\n",
    "    if crucial_term < 0:  # p_n > 1, and (1 - p_n) < 0\n",
    "        print(f\"Warning: The term under the square root is negative. \")\n",
    "        print(\n",
    "            f\"def_r : H {H}, T_value {T_value}, p_n {p_n},  m {m}, mu {mu},   lambda_T {lambda_T}, lambda_r {lambda_r}, crucial_term {crucial_term}\")\n",
    "        # return 1000000\n",
    "    return (H - T_value) * p_n + lambda_r * (crucial_term ** 0.5) * (np.log(m)) ** 0.5\n",
    "\n",
    "\n",
    "def prediction_of_M_n_k10(H, p_n, m,   lambda_T, lambda_s, lambda_r):\n",
    "    r_value = r(H, p_n, m,   lambda_T, lambda_r)\n",
    "    s_value = s(m,  H, lambda_T, lambda_s)\n",
    "    M_n = s_value + r_value\n",
    "    # To avoid overflowing\n",
    "    if M_n > H:\n",
    "        M_n = H\n",
    "        # print (f\"2 prediction_of_M_n_k10 : p_n  {p_n}, M_n {M_n}\")\n",
    "    return M_n\n",
    "\n",
    "\n",
    "def prediction_of_p_n_for_all_generationsk10(numberOfGenerations, population_size, length_of_target_sequence,\n",
    "                                              lambda_k, lambda_T, lambda_s, lambda_r):\n",
    "    H = length_of_target_sequence\n",
    "    m = population_size\n",
    "    # $M_t = $ number of solved sites at generation $t$\n",
    "    # $M_o = I = $ initial number of matched sites\n",
    "    # $ = \\frac{H}{2} +  \\sqrt{\\frac{H}{k}\\ln m  }  $\n",
    "    # $ I = \\frac{H}{2} +  \\lambda_k\\sqrt{H \\ln m  }  $.\n",
    "    # I = H / 2 + ((H / k) * np.log(m)) ** 0.5\n",
    "    I = H / 2 + lambda_k * (H * np.log(m)) ** 0.5\n",
    "    # Initial number of matched sites, which is the initial best fitness.\n",
    "    # It is given by the formula of I, which is the sum of a constant term and\n",
    "    # a variability term that depends on the population size and\n",
    "    # the length of the target sequence.\n",
    "    M_n_value = I\n",
    "    p_n = M_n_value / H\n",
    "    p_n_values = [p_n]\n",
    "    for i in range(numberOfGenerations):\n",
    "        M_n_value = prediction_of_M_n_k10(H, p_n, m, lambda_T, lambda_s, lambda_r)\n",
    "        p_n = M_n_value / H\n",
    "        if p_n > 1:\n",
    "            p_n = 1\n",
    "        p_n_values.append(p_n)\n",
    "    return p_n_values\n",
    "\n",
    "\n",
    "# The Underdog  Model does not include considerations about recombination\n",
    "# because we have not been able to detect its effect in the battle of la Evolución against complexity.\n",
    "def PredictionOfTheUnderdogModelk10(    lambda_k, lambda_T, lambda_s, lambda_r):\n",
    "    # print(\"PredictionOfTheUnderdogModelk10**\")\n",
    "    # print_parameters_for_underdog_modelk10()\n",
    "    # An la Evoluciónary process is triggered for each population size\n",
    "    # and each length of target sequence taken from this grid.\n",
    "    predicted_normalized_best_fitness_histories = []\n",
    "\n",
    "    for i in range(len(population_sizesk10)):\n",
    "        predicted_normalized_best_fitness_histories_popSize = []\n",
    "        for j in range(len(lengths_of_target_sequencesk10)):\n",
    "            population_size = population_sizesk10[i]\n",
    "            numberOfGenerations = numbersOfGenerationsk10[i]\n",
    "            length_of_target_sequence = lengths_of_target_sequencesk10[j]\n",
    "            predicted_normalized_best_fitness_history = prediction_of_p_n_for_all_generationsk10(numberOfGenerations,\n",
    "                                                                                                population_size,\n",
    "                                                                                                length_of_target_sequence,\n",
    "                                                                                                lambda_k,\n",
    "                                                                                                lambda_T, lambda_s,\n",
    "                                                                                                lambda_r)\n",
    "            predicted_normalized_best_fitness_histories_popSize.append(predicted_normalized_best_fitness_history)\n",
    "\n",
    "        predicted_normalized_best_fitness_histories.append(predicted_normalized_best_fitness_histories_popSize)\n",
    "\n",
    "    # print(f\"Predicted normalized plateau heights: { predicted_normalized_plateau_heights}\")\n",
    "    # title = 'Predicted normalized best fitness histories'\n",
    "    # draw_all_historiesk10(predicted_normalized_best_fitness_histories, title, population_sizesk10, lengths_of_target_sequencesk10)\n",
    "    return predicted_normalized_best_fitness_histories\n",
    "\n",
    "\n",
    "# ******************\n",
    "# COMPARISON OF THE PREDICTION OF THE UNDERDOG MODEL WITH THE OBSERVED HISTORIES\n",
    "# ******************\n",
    "\n",
    "\n",
    "def draw_M_values_and_errorsk10(M_values, Errors, best_fitness_history):\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(M_values, label='Predicted M(n)')\n",
    "    plt.plot(best_fitness_history[:len(M_values)], label='Observed Best Fitness History')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('M(n) / Best Fitness')\n",
    "    plt.title('Predicted M(n) and Observed Best Fitness History')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(Errors, label='Absolute Error between Predicted M(n) and Observed Best Fitness')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Absolute Error')\n",
    "    plt.title('Absolute Error between Predicted M(n) and Observed Best Fitness over Generations')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def draw_predicted_and_observed_historiesk10(predicted_p_n_values, observed_best_fitness_history, population_size,\n",
    "                                            length_of_target_sequence):\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(predicted_p_n_values,\n",
    "             label=f'Predicted normalized best fitness for Population size {population_size}, Target length {length_of_target_sequence}')\n",
    "    plt.plot(observed_best_fitness_history,\n",
    "             label=f'Observed best fitness history for Population size {population_size}, Target length {length_of_target_sequence}')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('Best Fitness')\n",
    "    plt.title(\n",
    "        f'Predicted and Observed Best Fitness Histories for Population Size {population_size} and Target Length {length_of_target_sequence}')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def infographic_comparison_of_prediction_and_observed_historiesk10(initial_or_final, predicted_normalized_best_fitness_histories,\n",
    "                                                                  observed_normalized_best_fitness_histories,\n",
    "                                                                  errors, values):\n",
    "    # Work by the AI of Visual Studio Code, with the help of the Author,\n",
    "    # to create an infographic that compares the predicted and observed histories\n",
    "    # for each combination of population size and length of target sequence.\n",
    "    # Create a tall canvas with a background color\n",
    "    fig = plt.figure(figsize=(15, 18), facecolor=\"#d5c1da\")\n",
    "    # Create a grid of subplots with 4 rows and 6 columns\n",
    "    gs = GridSpec(len(population_sizesk10), len(lengths_of_target_sequencesk10), figure=fig)\n",
    "    sum_errors = sum(errors)\n",
    "    # Add a header title\n",
    "    fig.suptitle(\n",
    "        f'{initial_or_final} Observed vs Predicted Best Fitness Histories, total error: {sum_errors:.1f} \\n values of parameters {values}',\n",
    "        fontsize=24, fontweight='bold', y=0.95)\n",
    "\n",
    "    # Loop through each combination of population size and target length\n",
    "    for i in range(len(population_sizesk10)):\n",
    "        for j in range(len(lengths_of_target_sequencesk10)):\n",
    "            predicted_normalized_best_fitness_history = predicted_normalized_best_fitness_histories[i][j]\n",
    "            observed_normalized_best_fitness_history = observed_normalized_best_fitness_histories[i][j]\n",
    "            population_size = population_sizesk10[i]\n",
    "            length_of_target_sequence = lengths_of_target_sequencesk10[j]\n",
    "\n",
    "            # Create at [i,j] a subplot for the current combination of population size and target length\n",
    "            ax = fig.add_subplot(gs[i, j])\n",
    "            ax.plot(predicted_normalized_best_fitness_history, label='Predicted', color='blue')\n",
    "            ax.plot(observed_normalized_best_fitness_history, label='Observed', color='orange')\n",
    "            ax.set_title(\n",
    "                f'Pop: {population_size}, Length: {length_of_target_sequence}, Error: {errors[i * len(lengths_of_target_sequencesk10) + j]:.1f}',\n",
    "                fontsize=8)\n",
    "            ax.set_xlabel('Generation')\n",
    "            ax.set_ylabel('Normalized Best Fitness')\n",
    "            ax.legend()\n",
    "            ax.grid(True)\n",
    "\n",
    "\n",
    "\n",
    "def totalErrork10(x, y):\n",
    "    sum = 0\n",
    "    for i in range(len(x)):\n",
    "        sum += abs(x[i] - y[i])\n",
    "    return sum\n",
    "\n",
    "\n",
    "def comparison_of_prediction_and_observed_historiesk10(predicted_normalized_best_fitness_histories,\n",
    "                                                      normalized_best_fitness_histories):\n",
    "    # print(\"comparison_of_prediction_and_observed_historiesk10**\")\n",
    "    errors = []\n",
    "    for i in range(len(population_sizesk10)):\n",
    "        for j in range(len(lengths_of_target_sequencesk10)):\n",
    "            predicted_normalized_best_fitness_history = predicted_normalized_best_fitness_histories[i][j]\n",
    "            observed_normalized_best_fitness_history = normalized_best_fitness_histories[i][j]\n",
    "            # print(f\"length of predicted_normalized_best_fitness_history: {len(predicted_normalized_best_fitness_history)}, length of observed_normalized_best_fitness_history: {len(observed_normalized_best_fitness_history)}\")\n",
    "            error = totalErrork10(predicted_normalized_best_fitness_history, observed_normalized_best_fitness_history)\n",
    "            errors.append(error)\n",
    "            # print(f\"Population size {population_size}, Target length {length_of_target_sequence}: Error between predicted and observed histories = {error:.4f}\")\n",
    "    # print(f\"\\nTotal error between predicted and observed histories: {sum(errors):.4f}\")\n",
    "    return errors\n",
    "\n",
    "\n",
    "def runOneAssignationk10( lambda_k, lambda_T, lambda_s, lambda_r):\n",
    "    ''' An la Evoluciónary process is triggered for each couple\n",
    "    of population size and target length taken from a grid.\n",
    "    The normalized best fitness history is stored in a list,\n",
    "    and then all the histories are plotted together.\n",
    "    A graphic is also plotted to show the tendency of the heights of the plateaus,\n",
    "    by plotting the last entry of the normalized best fitness history\n",
    "    for each length of target sequence.\n",
    "    '''\n",
    "    # print_parametersk10()\n",
    "    # print(\"runOneAssignationk10**\")\n",
    "    # List to store the normalized best fitness history for each length of target sequence\n",
    "    normalized_best_fitness_histories = []\n",
    "    normalized_plateau_heights = []\n",
    "    # An la Evoluciónary process is triggered for each population size\n",
    "    # and each length of target sequence taken from this grid.\n",
    "    for i in range(len(population_sizesk10)):\n",
    "        normalized_plateau_heights_popSize = []\n",
    "        normalized_best_fitness_histories_popSize = []\n",
    "        for j in range(len(lengths_of_target_sequencesk10)):\n",
    "            population_size = population_sizesk10[i]\n",
    "            numberOfGenerations = numbersOfGenerationsk10[i]\n",
    "            length_of_target_sequence = lengths_of_target_sequencesk10[j]\n",
    "            # print(f\"population_size: {population_size}, numberOfGenerations: {numberOfGenerations}, length_of_target_sequence: {length_of_target_sequence}\")\n",
    "            target_sequence = '1' * length_of_target_sequence\n",
    "            normalized_best_fitness_history = oneEvolucionaryProcessk10(population_size, numberOfGenerations,\n",
    "                                                                       length_of_target_sequence, target_sequence)\n",
    "            normalized_best_fitness_histories_popSize.append(normalized_best_fitness_history)\n",
    "            # To see the tendency of the heights of the plateaus,\n",
    "            # we can print and draw the last entry of\n",
    "            # the normalized best fitness history for each length of target sequence.\n",
    "            # print(f\"Population size {population_size}, Target length {length_of_target_sequence}: Normalized best fitness = {normalized_best_fitness_history[-1]}\")\n",
    "\n",
    "            normalized_plateau_heights_popSize.append(normalized_best_fitness_history[-1])\n",
    "        normalized_plateau_heights.append(normalized_plateau_heights_popSize)\n",
    "        normalized_best_fitness_histories.append(normalized_best_fitness_histories_popSize)\n",
    "\n",
    "    # print(f\"Normalized plateau heights: { normalized_plateau_heights}\")\n",
    "    if include_draw_all_historiesk10:\n",
    "        draw_all_historiesk10(normalized_best_fitness_histories )\n",
    "\n",
    "    return normalized_best_fitness_histories\n",
    "\n",
    "\n",
    "# Main function to test one assignation of parameters of the Underdog Model,\n",
    "# by comparing the predicted and observed histories for all combinations of population sizes and lengths of target sequences\n",
    "def testOneAssignationjk10( lambda_k, lambda_T, lambda_s, lambda_r):\n",
    "    print(\"\\ntestOneAssignationjk10**\")\n",
    "    print(f\"lambda_k, lambda_T, lambda_s, lambda_r: {lambda_k}, {lambda_T}, {lambda_s}, {lambda_r}\")\n",
    "    observed_normalized_best_fitness_histories = runOneAssignationk10(lambda_k, lambda_T, lambda_s, lambda_r)\n",
    "    predicted_normalized_best_fitness_histories = PredictionOfTheUnderdogModelk10( lambda_k, lambda_T, lambda_s, lambda_r)\n",
    "    total_errors = comparison_of_prediction_and_observed_historiesk10(predicted_normalized_best_fitness_histories,\n",
    "                                                                     observed_normalized_best_fitness_histories)\n",
    "    sum_errors = sum(total_errors)\n",
    "    if sum_errors == \"nan\" or sum_errors == \"inf\":\n",
    "        print(\n",
    "            f\"WWWWWW Warning: Total error is {sum_errors}, which is not a valid number. This may indicate an issue with the calculations. Please check the predicted and observed histories for any anomalies.\")\n",
    "\n",
    "    print(f\"Total error between predicted and observed histories for all combinations: {sum_errors:.4f}\")\n",
    "\n",
    "    return observed_normalized_best_fitness_histories, predicted_normalized_best_fitness_histories, total_errors\n",
    "\n",
    "\n",
    "# Main function to test one assignation of parameters of the Underdog Model,\n",
    "# by comparing the predicted and memorized observed histories\n",
    "# for all combinations of population sizes and lengths of target sequences\n",
    "def shortTestOneAssignationjk10(  lambda_k, lambda_T, lambda_s, lambda_r, observed_normalized_best_fitness_histories):\n",
    "    # print(\"\\nshortTestOneAssignationjk10**\")\n",
    "    # print(f\"lambda_k, lambda_T, lambda_s, lambda_r: {lambda_k}, {lambda_T}, {lambda_s}, {lambda_r}\")\n",
    "    predicted_normalized_best_fitness_histories = PredictionOfTheUnderdogModelk10(  lambda_k, lambda_T, lambda_s, lambda_r)\n",
    "    total_errors = comparison_of_prediction_and_observed_historiesk10(predicted_normalized_best_fitness_histories,\n",
    "                                                                     observed_normalized_best_fitness_histories)\n",
    "    sum_errors = sum(total_errors)\n",
    "    if math.isnan(sum_errors):\n",
    "        sum_errors = 1000000\n",
    "        # print(f\"WWWWWW Warning: Total error is {sum_errors}, which is not a valid number. This may indicate an issue with the calculations. Please check the predicted and observed histories for any anomalies.\")\n",
    "\n",
    "    # print(f\"Total error between predicted and observed histories for all combinations: {sum_errors:.4f}\")\n",
    "\n",
    "    return observed_normalized_best_fitness_histories, predicted_normalized_best_fitness_histories, total_errors\n",
    "\n",
    "\n",
    "# *******************\n",
    "# MASTER GENETIC ALGORITHM THAT OPTIMIZES THE ASSIGNATION OF PARAMETERS TO\n",
    "# THE UNDERDOG MODEL TO FIT THE PREDICTION TO THE OBSERVED HISTORIES\n",
    "# *******************\n",
    "\n",
    "\n",
    "def decoderjk10(s):\n",
    "    ''' The input is a number as string, outputs the number as a float.\n",
    "         Valid for numbers of the form 1.23456789 only, with a length of chromosome part of 10. '''\n",
    "    # A point is intercalated after the first char\n",
    "    ss = s[:1] + '.' + s[1:]\n",
    "    n = float(ss)  # Convert to float\n",
    "    return n\n",
    "\n",
    "\n",
    "def decodeFullChromosomejk10(chromosome ):\n",
    "    \"\"\"Decodes the full chromosome into the coefficients of the polynomial.\"\"\"\n",
    "    \"\"\"The chromosome is divided into 4 parts, \n",
    "        each one encoding for a parameter.\"\"\"\n",
    "    chro0 = chromosome[0:length_of_chromosomesjk10]\n",
    "    chro1 = chromosome[length_of_chromosomesjk10:2 * length_of_chromosomesjk10]\n",
    "    chro2 = chromosome[2 * length_of_chromosomesjk10:3 * length_of_chromosomesjk10]\n",
    "    chro3 = chromosome[3 * length_of_chromosomesjk10:4 * length_of_chromosomesjk10]\n",
    "\n",
    "    a_0 = decoderjk10(chro0)\n",
    "    a_1 = decoderjk10(chro1)\n",
    "    a_2 = decoderjk10(chro2)\n",
    "    a_3 = decoderjk10(chro3)\n",
    "\n",
    "    return a_0, a_1, a_2, a_3\n",
    "\n",
    "\n",
    "def encodeFullChromosomejk10(a_0, a_1, a_2, a_3 ):\n",
    "    \"\"\"Encodes the coefficients of the polynomial into a chromosome.\n",
    "        The chromosome is divided into 4 parts,\n",
    "        each one encoding for a parameter.\n",
    "        The encoding of a coefficient is as follows:\n",
    "        The first char encodes for the position of the decimal point,\n",
    "        The remaining chars encode for the number itself,\n",
    "        without the decimal point nor sign.\n",
    "        There is no sign because all numbers are positive,\n",
    "        and the position of the decimal point is encoded in a way that\n",
    "        allows to represent both very small and very large numbers.\n",
    "        Developed with the help of the AI of Visual Studio Code.\"\"\"\n",
    "\n",
    "    def encoderjk10(float_number, length_of_chromosomes):\n",
    "        ''' The input is a parameter as a float, of the form 1.23456789,\n",
    "            outputs a string of digits. '''\n",
    "        # number to string\n",
    "        n_str = str(float_number)\n",
    "\n",
    "        if float_number == 0:\n",
    "            return '0' * length_of_chromosomes\n",
    "        # Remove the decimal point\n",
    "        n_str_noPoint = n_str.replace(\".\", \"\")\n",
    "        # print (f\"Float number: {float_number}, Number as string: {n_str}, string without point: {n_str_noPoint}\")\n",
    "        # If the string is too long, it is trimmed to fit the length of the chromosome part.\n",
    "        n_str_adjusted = n_str_noPoint\n",
    "        if len(n_str_adjusted) > length_of_chromosomes - 1:\n",
    "            n_str_adjusted = str(n_str_noPoint)[:length_of_chromosomes]\n",
    "        # If the string is too short, it is completed with zeros to fit the length of the chromosome part.\n",
    "        while len(n_str_adjusted) < length_of_chromosomes:\n",
    "            n_str_adjusted = n_str_adjusted + '0'\n",
    "        return n_str_adjusted\n",
    "\n",
    "    chro0 = encoderjk10(a_0, length_of_chromosomesjk10)\n",
    "    chro1 = encoderjk10(a_1, length_of_chromosomesjk10)\n",
    "    chro2 = encoderjk10(a_2, length_of_chromosomesjk10)\n",
    "    chro3 = encoderjk10(a_3, length_of_chromosomesjk10)\n",
    "    return chro0 + chro1 + chro2 + chro3\n",
    "\n",
    "\n",
    "def createPerfectPopulationjk10(lambda_k, lambda_T, lambda_s, lambda_r ):\n",
    "    \"\"\"Creates a new population for the master GA with the perfect chromosome.\n",
    "\n",
    "    By the AI of Visual Studio Code, based on the encodeFullCromosome function.\"\"\"\n",
    "    perfect_chromosome = encodeFullChromosomejk10(lambda_k, lambda_T, lambda_s, lambda_r )\n",
    "    # print(f\"Perfect chromosome: {perfect_chromosome}\")\n",
    "    a0, a1, a2, a3 = decodeFullChromosomejk10(perfect_chromosome )\n",
    "    # print(f\"Decoded perfect chromosome: lambda_k = {a0}, lambda_T = {a1}, lambda_s = {a2}, lambda_r = {a3}\")\n",
    "    population = [perfect_chromosome] * population_sizejk10\n",
    "    return population\n",
    "\n",
    "\n",
    "def calculate_errorjk10(chromosome,   lambda_k, lambda_T, lambda_s, lambda_r,\n",
    "                       observed_normalized_best_fitness_histories):\n",
    "    \"\"\"Calculates the error of a chromosome,\n",
    "        it must be decoded into four values of the four parameters.\n",
    "        This is an assignation for the four paramters of the Underdog Model.\n",
    "        Next, the assignation must be tested by running the corresponding la Evoluciónary Processes,\n",
    "        and calculating the error between the predicted and observed fitness histories.\"\"\"\n",
    "    lambda_k, lambda_T, lambda_s, lambda_r = decodeFullChromosomejk10(chromosome )  #\n",
    "    observed_normalized_best_fitness_histories, predicted_normalized_best_fitness_histories, total_errors = shortTestOneAssignationjk10( lambda_k, lambda_T,\n",
    "        lambda_s, lambda_r, observed_normalized_best_fitness_histories)\n",
    "\n",
    "    total_error = sum(total_errors)\n",
    "    # print(f\"\\nTotal error between predicted and observed histories for all combinations: {total_error:.4f}\")\n",
    "    return total_error\n",
    "\n",
    "\n",
    "def calculate_all_errorsjk10(population, lambda_k, lambda_T, lambda_s, lambda_r,\n",
    "                            observed_normalized_best_fitness_histories):\n",
    "    # print(\"\\n*calculate_all_errorsjk10(population, target_sequence)\")\n",
    "    \"\"\"Calculates the error of all chromosomes in the population.\"\"\"\n",
    "    # List with the errors of all chromosomes\n",
    "    errors = []\n",
    "    for chromosome in population:\n",
    "        total_error = calculate_errorjk10(chromosome,   lambda_k, lambda_T, lambda_s, lambda_r,\n",
    "                                         observed_normalized_best_fitness_histories)\n",
    "        errors.append(total_error)\n",
    "        # print(f\"{chromosome} error : {total_error}\")\n",
    "    # print(\"errors: \", errors)\n",
    "    return errors\n",
    "\n",
    "\n",
    "def select_the_least_errorjk10(population, lambda_k, lambda_T, lambda_s,\n",
    "                              lambda_r, observed_normalized_best_fitness_histories):\n",
    "    \"\"\"Selects the chromosome of the population of the master GA with minimal error for reproduction.\n",
    "     The error is calculated by decoding the chromosome into the four parameters,\n",
    "     running the corresponding la Evoluciónary Processes,\n",
    "     and calculating the error between the predicted and observed fitness histories.\"\"\"\n",
    "    print(\"select_the_least_errorjk10**\")\n",
    "    errors = calculate_all_errorsjk10(population,  lambda_k, lambda_T, lambda_s, lambda_r, observed_normalized_best_fitness_histories)\n",
    "    smallest_error = min(errors)\n",
    "    best_chromosome_index = errors.index(smallest_error)\n",
    "    best_chromosome = population[best_chromosome_index]\n",
    "    # print(\"\\n*select_the_least_errorjk10(population)\")\n",
    "    # print(\"smallest_error = \", smallest_error)\n",
    "    # print(\"The best chromosome = \", best_chromosome )\n",
    "    parameters = decodeFullChromosomejk10(best_chromosome )\n",
    "    # print(f\"Decoded best chromosome: lambda_k = {parameters[0]}, lambda_T = {parameters[1]}, lambda_s = {parameters[2]}, lambda_r = {parameters[3]}\")\n",
    "    return smallest_error, best_chromosome\n",
    "\n",
    "\n",
    "def clone_populationjk10(best_chromosome, population_size):\n",
    "    \"\"\"Creates a new population by cloning the best chromosome.\"\"\"\n",
    "    return [best_chromosome] * population_size\n",
    "\n",
    "\n",
    "def mutatejk10(chromosome):\n",
    "    \"\"\"Mutates a chromosome.\"\"\"\n",
    "    mutated_chromosome_list = list(chromosome)\n",
    "    for i in range(1, len(mutated_chromosome_list)):\n",
    "        if random.random() < mutation_rate:\n",
    "            mutated_chromosome_list[i] = random.choice(digit_alphabet)\n",
    "    mutated_chromosome_list[0] = random.choice(binary_alphabet)\n",
    "    mutated_chromosome_list[10] = random.choice(binary_alphabet)\n",
    "    mutated_chromosome_list[20] = random.choice(binary_alphabet)\n",
    "    mutated_chromosome_list[30] = random.choice(binary_alphabet)\n",
    "    mutated_chromosome = \"\".join(mutated_chromosome_list)  # list to string\n",
    "    # values = decodeFullChromosomejk10(mutated_chromosome)\n",
    "    # print(f\"values of mutated chro : {values}\")\n",
    "    return mutated_chromosome\n",
    "\n",
    "\n",
    "def mutate_alljk10(population ):\n",
    "    \"\"\"Mutates all chromosomes in the population.\"\"\"\n",
    "    mutated_population = []\n",
    "    for chromosome in population:\n",
    "        mutated_chromosome = mutatejk10(chromosome )\n",
    "        mutated_population.append(mutated_chromosome)\n",
    "    return mutated_population\n",
    "\n",
    "\n",
    "def crossoverjk10(parent1, parent2  ):\n",
    "    \"\"\"Performs crossover between two parent chromosomes.\"\"\"\n",
    "    if random.random() < recombination_rate:\n",
    "        crossover_point = random.randint(0, length_of_chromosomesjk10)\n",
    "        return parent1[:crossover_point] + parent2[crossover_point:]\n",
    "    else:\n",
    "        return parent1\n",
    "\n",
    "\n",
    "def crossover_alljk10(population ):\n",
    "    \"\"\"Performs crossover on all chromosomes in the population.\"\"\"\n",
    "    recombinant_population = []\n",
    "    for i in range(len(population)):\n",
    "        parent1 = population[i]\n",
    "        parent2 = random.choice(population)\n",
    "        child = crossoverjk10(parent1, parent2 )\n",
    "        recombinant_population.append(child)\n",
    "    return recombinant_population\n",
    "\n",
    "\n",
    "def run_one_generationjk10(population,  lambda_k, lambda_T,\n",
    "                          lambda_s, lambda_r, observed_normalized_best_fitness_histories):\n",
    "    \"\"\"Runs one generation of the genetic algorithm.\"\"\"\n",
    "    # Calculate fitness scores for the current population\n",
    "    smallest_error, best_chromosome = select_the_least_errorjk10(population,\n",
    "                                                                lambda_k, lambda_T, lambda_s, lambda_r,\n",
    "                                                                observed_normalized_best_fitness_histories)\n",
    "    length_of_chromosomes = len(population[0])\n",
    "    # print(f\" best_chromosome {best_chromosome}, best_fitness {best_fitness}\")\n",
    "    # Clone the best to build the new population\n",
    "    new_population = []\n",
    "    if include_clone_populationjk10:\n",
    "        new_population = clone_populationjk10(best_chromosome, population_sizejk10)\n",
    "    if include_mutate_alljk10:\n",
    "        new_population = mutate_alljk10(new_population )\n",
    "    if include_crossover_alljk10:\n",
    "        new_population = crossover_alljk10(new_population )\n",
    "    return smallest_error, best_chromosome, new_population\n",
    "\n",
    "\n",
    "# Optimize the assignation of the parameters to the Underdog Model\n",
    "def masterGAjk10( observed_normalized_best_fitness_histories):\n",
    "    print(\"masterGAjk10**\")\n",
    "    # Create a population with the chromosome that encodes for the initial values of the four parameters.\n",
    "    population = createPerfectPopulationjk10(lambda_k_o, lambda_T_o, lambda_s_o, lambda_r_o  )\n",
    "    # print(population)\n",
    "\n",
    "    # la Evolución must optimize the values of parameters beginning with a seed\n",
    "    lambda_k = lambda_k_o,\n",
    "    lambda_T = lambda_T_o                                                                        , \n",
    "    lambda_s = lambda_s_o\n",
    "    lambda_r = lambda_r_o \n",
    "    \n",
    "    generation = 0\n",
    "    best_chromosome = ''\n",
    "    best_fitness = 0\n",
    "    smallest_error_history = []  # List to store best fitness of each generation\n",
    "    if print_best_fitness:\n",
    "        print(\"Generation, best_chromosome and best_fitness\")\n",
    "    while generation < number_of_generationsjk10:\n",
    "        generation += 1\n",
    "        smallest_error, best_chromosome, population = run_one_generationjk10(population, lambda_k, lambda_T,\n",
    "                                                                            lambda_s, lambda_r,\n",
    "                                                                            observed_normalized_best_fitness_histories)\n",
    "        print(f\" GENERATION: {generation},   best chromosome: {best_chromosome}, smallest error: {smallest_error}\")\n",
    "        smallest_error_history.append(smallest_error)\n",
    "\n",
    "    # print(f\"\\nTarget sequence matched in generation {generation}\")\n",
    "    # print(f\"Final chromosome: {best_chromosome}\")\n",
    "    totalCost = population_sizejk10 * generation\n",
    "\n",
    "    return best_chromosome, totalCost, smallest_error_history\n",
    "\n",
    "\n",
    "# *******************\n",
    "# OPTIMIZING ALGORITHM TO MINIMIZE THE ERROR BETWEEN\n",
    "# THE PREDICTION OF THE UNDERDOG MODEL AND THE OBSERVED HISTORIES\n",
    "# *******************\n",
    "\n",
    "\n",
    "def plot_functionjk10(smallest_error_history):\n",
    "    g_values = list(range(1, len(smallest_error_history) + 1))\n",
    "    # Plot the fitness history\n",
    "    plt.figure(figsize=(10, 8), facecolor=\"#d5c1da\")\n",
    "    plt.plot(g_values, smallest_error_history, label='Smallest Error Over Generations')\n",
    "    plt.xlabel('Generation')\n",
    "    plt.ylabel('SmallestError')\n",
    "    plt.legend()\n",
    "    plt.title('Smallest Error Over Generations')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Main function to optimize the assignation of parameters of the Underdog Model.\n",
    "# This is a task for la Evolución, which can be implemented by a genetic algorithm that searches for\n",
    "# the best assignation of parameters that minimizes the total error between the predicted and observed histories.\n",
    "def mainjk10(  ):\n",
    "    ### INITIAL SETTING OF THE PARAMETERS OF THE UNDERDOG MODEL AND COMPARISON OF THE PREDICTION WITH THE OBSERVED HISTORIES\n",
    "    # Initial assignation of parameters of the Underdog Model.\n",
    "    # It is the one defined at the beginning of the program,\n",
    "    # which is based on the theoretical considerations of the model and on some preliminary tests.\n",
    "    print_parameters_for_slave_GAk10()\n",
    "    print_parameters_for_underdog_modelk10()\n",
    "    print_parameters_for_master_GAk10()\n",
    "\n",
    "    # Test to the initial assignation of parameters of the Underdog Model\n",
    "    # by running the corresponding la Evoluciónary Processes.\n",
    "    # So, they are run only once and best fitness histories are memorized and reused whenever needed.\n",
    "    observed_normalized_best_fitness_histories, predicted_normalized_best_fitness_histories, errors = testOneAssignationjk10(\n",
    "                                                                                                        lambda_k_o, lambda_T_o, lambda_s_o, lambda_r_o)\n",
    "    values = [lambda_k_o, lambda_T_o, lambda_s_o, lambda_r_o]\n",
    "    infographic_comparison_of_prediction_and_observed_historiesk10(\"Initial\",\n",
    "                                                                  predicted_normalized_best_fitness_histories,\n",
    "                                                                  observed_normalized_best_fitness_histories,\n",
    "                                                                  errors, values)\n",
    "\n",
    "    ### la Evolución fine tunes the assignation of parameters of the Underdog Model to minimize the error between the predicted and observed histories.\n",
    "    best_chromosome, totalCost, smallest_error_history = masterGAjk10(observed_normalized_best_fitness_histories)\n",
    "    final_error = smallest_error_history[-1]\n",
    "    xlabel = 'Generation'\n",
    "    ylabel = 'SmallestError'\n",
    "    title = 'Smallest Error Over Generations'\n",
    "    print(f\"smallest_error_history {smallest_error_history}\")\n",
    "    plot_functionjk10(smallest_error_history)\n",
    "\n",
    "    print(f\" The final best chromosome is {best_chromosome}\")\n",
    "    lambda_k, lambda_T, lambda_s, lambda_r = decodeFullChromosomejk10(best_chromosome )\n",
    "    print(f\" Encoded values: lambda_k {lambda_k}, lambda_T {lambda_T}, lambda_s {lambda_s}, lambda_r {lambda_r}\")\n",
    "    print(f\" Total cost =  {totalCost} chromosomes\")\n",
    "    print(f\" Final error =  {final_error} \")\n",
    "\n",
    "    predicted_normalized_best_fitness_histories = PredictionOfTheUnderdogModelk10( lambda_k, lambda_T, lambda_s, lambda_r)\n",
    "    errors = comparison_of_prediction_and_observed_historiesk10(predicted_normalized_best_fitness_histories,\n",
    "                                                               observed_normalized_best_fitness_histories)\n",
    "    values = [lambda_k, lambda_T, lambda_s, lambda_r]\n",
    "    infographic_comparison_of_prediction_and_observed_historiesk10(\"Optimized\",\n",
    "                                                                  predicted_normalized_best_fitness_histories,\n",
    "                                                                  observed_normalized_best_fitness_histories,\n",
    "                                                                  errors, values)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    mainjk10( )\n",
    "\n",
    "    end_time = time.time()\n",
    "    Execution_time = end_time - start_time\n",
    "    # print(f\"Execution time: {Execution_time:.2f} seconds\")\n",
    "    # Execution time in hours, minutes and seconds:\n",
    "    hours = int(Execution_time // 3600)\n",
    "    minutes = int((Execution_time % 3600) // 60)\n",
    "    seconds = int(Execution_time % 60)\n",
    "    print(f\"Execution time: {hours} hours, {minutes} minutes, {seconds} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9878b205",
   "metadata": {},
   "source": [
    "*Nuestras observaciones.* \n",
    "\n",
    "Para 100 generaciones del  AG Maestro, nuestras observaciones son las siguientes:    \n",
    "\n",
    "1. Los errores iniciales entre lo predicho y lo observado estaban en el orden de los decimales. Estos se obtuvieron por una Evolución rudimentaria, hecha por el Autor haciendo cambios a mano.  \n",
    "2. Para el pequeño experimento tenemos:\n",
    "> - Los valores optimizados de los cuatro parámetros fueron:   \n",
    ">>> lambda_k 0.67422946, lambda_T 0.187559769, lambda_s 0.640827007, lambda_r 0.023058801. El ajuste fue muy bueno.      \n",
    "> -  Para otra prueba, el ajuste no fue malo. Los valores optimizados de los cuatro parámetros fueron    \n",
    ">>> lambda_k 0.201150048, lambda_T 1.072661032, lambda_s 0.782895876, lambda_r 0.016005215.    \n",
    "> - Tercera corrida con un buen ajuste:     \n",
    ">>> lambda_k 0,005948459, lambda_T 0,580856665, lambda_s 0,71, lambda_r 0,014.     \n",
    "> - Vemos que el parámetro lambda_T observa una alta variabilidad. Este parámetro cuantifica la participación de los sitios tocados sobre el rendimiento general. Además, el parámetro lambda_k no está fuertemente restringido porque determina la condición inicial. Por lo tanto, puede cambiar mucho si solo se mantiene pequeño. Los otros dos parámetros son importantes para el proceso recursivo, por lo que su variabilidad es muy pequeña.       \n",
    "3. Para el gran experimento, \n",
    "> - Los valores de los parámetros fueron:    \n",
    ">>> k 0.17135506, lambda_T 0.735381264, lambda_s 0.787101097, lambda_r 0.014983946. El ajuste en una corrida fue bueno. \n",
    "> - En otra prueba, el ajuste también fue bueno y los valores de los parámetros fueron    \n",
    ">>> lambda_k 0,62533042, lambda_T 0,111140669, lambda_s 0,705993696, lambda_r 0,015598979.    \n",
    ">   - En una tercera ronda, el ajuste también fue bueno y los valores de los parámetros fueron    \n",
    ">>> lambda_k 0,305317511, lambda_T 0,535010903, lambda_s 0,780780999, lambda_r 0,015380011    \n",
    "> - Confirmamos la generalización anterior: los parámetros lambda_k y Lambda_T son más flexibles y admiten más cambios que los otros dos, porque el primero trata con la condición inicial y nada más, el segundo con sitios tocados por mutación, pero lambda_s se ocupa del efecto de los sitios resueltos entre tocados, mientras que lambda_r representa el efecto de la herencia de los sitios resueltos de la generación anterior. Su importancia crítica se denota por su poca variabilidad.\n",
    "4. La Evolución funciona: la Evolución mejora la calidad de las predicciones desde un error inicial del orden de los decimales hasta otro en el orden de milésimas. De hecho, para este problema, la Evolución eleva la calidad casi hasta la perfección. Es sorprendente que un modelo con parámetros constantes pudiera producir un ajuste tan bueno.       \n",
    "\n",
    "**Ejercicio.** De respuesta a la siguiente objeción a los resultados reportados en la observación anterior de que la Evolución da a algunos parámetros valores muy diversos. \n",
    ">\n",
    "> *Objeción.* Esta conclusión es prematura porque el Proceso Evolutivo con solo 100 generaciones estaba muy crudo. \n",
    ">\n",
    "> *Respuesta.* La objeción es correcta. No tenemos derecho a afirmar que 100 generaciones sean suficientes para lograr una estimación final de los parámetros. Para remediar esto, el programa se ejecutó durante 1000 generaciones para el Proceso Evolutivo Maestro del experimento largo. \n",
    ">>>\n",
    ">> - Los valores de los parámetros para una ejecución fueron:\n",
    ">>>\n",
    ">>>  lambda_k 0,407185299, lambda_T 0,295248246, lambda_s 0,760910943, lambda_r 0,012388185\n",
    ">>>\n",
    ">> - Para otra ejecución, los valores fueron:\n",
    ">>>\n",
    ">>> lambda_k 0,426610715, lambda_T 0,401010011, lambda_s 0,780849601, lambda_r 0,010760077\n",
    ">>>\n",
    ">>-  Para una tercera prueba, los valores fueron:\n",
    ">>>\n",
    ">>> lambda_k 0.388449127, lambda_T 0.380907956, lambda_s 0.79101312, lambda_r 0.010200201\n",
    "\n",
    ">> - Vemos que para 1000 generaciones, la variabilidad en los valores de los dos primeros parámetros parece ser menor que con 100 generaciones solamente. \n",
    "\n",
    "**Ejercicio.** Comparar los valores de los cuatro parámetros con  los cuatro tipos de fluctuaciones que están presentes en un proceso evolutivo.\n",
    ">\n",
    "> *Respuesta.* Adoptemos los siguientes valores para los cuatro parámetros y el correspondiente micro-observable:\n",
    ">>>\n",
    ">> $ \\lambda_k \\in [0.38, 0.43]$: condición inicial.\n",
    ">>> \n",
    ">> $ \\lambda_T \\in [0.29, 0.40]$: número de sitios tocados\n",
    ">>>\n",
    ">> $ \\lambda_s = 0,7$: número de sitios resueltos entre los afectados por la mutación.\n",
    ">>>\n",
    ">> $ \\lambda_r = 0,01$: número de sitios mutados no tocados que estaban resueltos al comienzo de esta generación.\n",
    "\n",
    "Por otro lado, los cuatro tipos de microfluctuaciones son:\n",
    "\n",
    " 1. La fluctuación en el número de tocados por sitios de mutación es muy negativa. \n",
    " 2. Las fluctuaciones en el número de sitios tocados y resueltos se equilibran, las fluctuaciones positivas y negativas juegan un papel similar en la meseta. En la rampa son positivas. \n",
    " 3. La fluctuación en el número de sitios no tocados por mutaciones es positiva, excepto al principio.\n",
    " 4. Las fluctuaciones en el número de sitios sin tocar y resueltos son muy positivas. Tenemos fluctuaciones positivas súper potentes. \n",
    "\n",
    " De esto, inferimos que \n",
    " 1. La creación de una población siempre produce fluctuaciones positivas que son un punto de inflexión importante para la Evolución con un peso alrededor de 0,4 que se da por $ \\lambda_k en [0,38, 0,43]$.\n",
    " 2. La Evolución da a las fluctuaciones muy positivas en el número de sitios no tocados y resueltos un bajo peso que se da por $ \\lambda_r = 0,01$.  Consideramos que esto significa que las fluctuaciones muy positivas en este observable son muy importantes pero rara vez ocurren. \n",
    " 3. Las fluctuaciones en el número de sitios tocados tienen un peso variable dado por $ \\lambda_T \\in [0,29, 0,40]$. Consideramos que su peso es de alrededor de 0,35, lo cual es relativamente alto. \n",
    " 4. Las fluctuaciones en el número de sitios resueltos entre los afectados por la mutación se utilizan comúnmente, con un peso de $ \\lambda_s = 0,7$. \n",
    "\n",
    "**Desafío.** El programa anterior necesita demasiada memoria. Incluso cuando todas las demás aplicaciones están desactivadas, se necesitan unos 25 GB en un coputador de mesa. Hacer algo para mantener las necesidades de memoria dentro de los límites habituales, digamos, menos de 16 GB para el sistema en general. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4ed96d",
   "metadata": {},
   "source": [
    "# GRADUACIÓN\n",
    "\n",
    "Elija uno de los siguientes proyectos:\n",
    "\n",
    "1. Utilice el modelo Underdog para hacer extrapolaciones sobre el tiempo necesario para resolver una tarea con una complejidad del orden del microorganismo más simple, medio millón de bases de ADN. \n",
    "2. Nuestro modelo Underdog se basa en la teoría matemática de MAX. Este modelo necesita la especificación externa de cuatro parámetros. Un trabajo preliminar fue hecho a mano y su resultado se dio como una pista para un AG, lo que mejoró con éxito la alta calidad. Es por eso que afirmamos que el modelo de Underdog es la manera natural de entender la Evolución. Demuestre que la Evolución también puede especificar con éxito esos parámetros si no se da ninguna pista, es decir, si la población inicial se sintetiza únicamente por azar. \n",
    "3. Haz una incursión en el mundo de los parámetros variables para el modelo Underdog. Por lo tanto, presta atención a la dependencia de los parámetros del tamaño de la población porque para algunos de sus valores, el ajuste es mucho mejor que para otros.\n",
    "4. Realizar un análisis estadístico riguroso para desentrañar el papel de la recombinación. Hasta este momento, consideramos que con respecto a la adquisición de función por pasos simples, el papel de la recombinación es insignificante. Más exactamente, su papel está claramente oscurecido por la incertidumbre en la estimación de los valores óptimos de los parámetros del modelo Underdog. Sabemos esto porque las simulaciones en la presente contribución incluyen recombinación mientras que el modelo de Underdog se basa en mutación sin recombinación y las predicciones coinciden muy bien con las observaciones. La recombinación de genes completos es, sin embargo, un fenómeno muy importante en los seres vivos reales que comienza con las bacterias y todo tipo de infección por virus. \n",
    "5. Hemos demostrado que la perfección tiene un costo exponencial en términos del tamaño de la población. Esto es un rechazo claro de la Teoría Evolutiva. Sin embargo, esta afirmación es muy abstracta. Trabaje en el desarrollo de simulaciones que permitan a los laicos comprender qué es una imperfección y por qué éstas deben encontrarse necesariamente en todas partes del registro fósil, así como en nuestra anatomía y fisiología. \n",
    "6. La perfección tiene un costo exponencial que se paga en el tamaño de la población. Pero la Evolución es un fenómeno dinámico que acumula pequeños avances a lo largo de las generaciones, a lo largo del tiempo. No hemos especificado la relación entre el procesamiento paralelo, dado por el tamaño de la población, y el procesamiento en serie, dado por las generaciones manejadas por Evolución. Hemos utilizado abusivamente un postulado ergótico que dice que el procesamiento paralelo y en serie se pueden intercambiar entre sí sin afectar la complejidad de la Evolución, que pertenece a la clase exponencial. Impugnar esto, es decir, encontrar la relación correcta y hacer las modificaciones correspondientes, si es que hay alguna (pensamos que ninguna).\n",
    "7. Observamos que el buen ajuste de las simulaciones por el modelo Underdog se podía lograr con una asignación diversa a los valores de sus parámetros. Pensamos que esto se debía a un tamaño de muestra pequeño para las pruebas. Así, tomamos en los últimos programas una muestra grande con diversos valores de tamaño y complejidad de la población. Ahora, tendemos a creer que los valores óptimos se dan por una sola asignación. Desafíe esta creencia. Por ejemplo, ponga $ \\lambda_T = 0$ y trata de demostrar que los otros tres parámetros se pueden cuadrar para obtener un buen ajuste.  ¿Consideras que tus resultados atacan al modelo Underdog porque está limitado de manera poco estricta? \n",
    "8. Tendemos a creer firmemente que la relación entre recursos y complejidad determina la silueta del Proceso Evolutivo y de todas sus observables. Es decir, debe existir una relación escalable entre los recursos y la complejidad. Convertir esta apreciación difusa en una afirmación científica, cuantitativa y sistemáticamente reproducible.\n",
    "9. Hemos dicho que las altas fluctuaciones en el número de sitios que fueron tocados y resueltos por la mutación tienen una baja participación en la aptitud general del mejor de cada generación. Esto es contra-intuitivo porque la Teoría Evolutiva dice precisamente que la selección del mejor es el combustible de la Evolución, por lo tanto, todos esperamos que las fluctuaciones correspondientes tengan una participación poderosa sobre el fitness. Para reencontrar esta aserción, ignora la variabilidad poblacional de los sitios tocados y calcula nuevamente la participación del número de sitios resueltos en el fitness.\n",
    "10. La ideología del Underdog dice que el éxito evolutivo no proviene de fluctuaciones altas. La razón es que una alta fluctuación se produce como resultado de dos altas fluctuaciones en tándem. La primera es en el número de sitios que son tocados por la mutación. La segunda, es en el número de sitios que han sido tocados por una mutación y son resueltos por ella. Tal coincidencia es, por supuesto, baja. Esta afirmación dice que por debajo de nuestro modelo que presta atención a los valores medios y la desviación esperada, un procedimiento de conteo podría dar resultados exactos cuyos valores promedio podrían coincidir con los de nuestro modelo. Desarrollar tales procedimientos  de conteo, y tratar de reproducir y corregir el modelo Underdog. \n",
    "11. Utilice el modelo de Underdog para encontrar el valor óptimo de la mutación para aumentar la tasa evolutiva, que es un parámetro constante que mide la velocidad de la Evolución para lograr una tarea. Pon a prueba tu predicción. \n",
    "12. Cuantifique la variación de las fluctuaciones alrededor de la meseta. Demostrar que cuando la complejidad crece, la magnitud de las fluctuaciones disminuye y, por lo tanto, se vuelve más difícil para ellas alcanzar la perfección, la cual siempre se puede hallar. Modelar las fluctuaciones y hacer predicciones sobre el costo de hallar la perfección. Does your result challenge our rejection of the Evolutionary Theory? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46f9053",
   "metadata": {},
   "source": [
    "# CONCLUSIÓN\n",
    "\n",
    "Hemos estudiado la batalla entre la Evolución y la complejidad.\n",
    "\n",
    "**Ejercicio.** Redacta tus conclusiones de este estudio.\n",
    ">\n",
    "> *Respuesta.* Nuestras conclusiones del presente trabajo son:\n",
    "\n",
    "1. La Teoría Evolutiva dice que cada especie en la Tierra es un producto de la Evolución. Hemos encontrado que esta teoría es claramente científica porque la Evolución ha sido abstraída como un Algoritmo Genético (AG), cuyas propiedades se pueden estudiar y sus conclusiones obligatorias extraerse. Un AG se compone de iteraciones de evaluación de la aptitud, selección del más apto seguida por su clonación, mutación y recombinación. \n",
    "2. Los AGs son efectivos. De hecho, la Evolución Rudimentaria, hecha a mano, puede producir aproximaciones no malas, y su implementación automática en una computadora puede mejorar sensiblemente la alta calidad en ejemplos apropiados.Esta es la prueba, desde el punto de vista de la Ciencia Moderna, que la explicación natural de nuestra existencia es la Evolución. Pero, la Ciencia está equivocada porque la Evolución también tiene predicciones obligatorias que contradicen letalmente la teoría antes mencionada. \n",
    "3. La predicción obligatoria más importante de la Teoría Evolutiva es que para fabricar soluciones aproximadas a los problemas planteados, la Evolución debe pagar un costo. Para cuantificar esto, hemos probado la Evolución con la tarea de emparejar una cadena binaria de longitud predefinida. La complejidad está representada por la longitud de la cadena de entrada o objetivo. La Evolución utiliza la población como un recurso que permite ensayos recursivos paralelos para ganar calidad paso a paso, serialmente, hasta alcanzar un nivel, generalmente por debajo de la perfección. Si el tamaño de la población es lo suficientemente alto, se puede lograr la perfección. El costo correspondiente es exponencial en la longitud de la entrada o la longitud de la cadena binaria objetivo. En la práctica, esto significa que la Evolución es real y no mágica y que debe producir toneladas de organismos imperfectos antes de alcanzar cualquier rastro de perfección. Así, los museos deben estar llenos de todo tipo de esqueletos retorcidos, y además, las disfunciones y malformaciones deben ser contadas en nuestros cuerpos por millones. Por lo tanto, cada persona en la Tierra puede poner a prueba a  la Teoría Evolutiva simplemente mirando su cuerpo y fisiología. \n",
    "4. La Evolución se puede entender casi perfectamente gracias al modelo del Perdedor o Underdog. Este modelo supone que la selección del Fittest es correctamente abstraída por MAX, que obtiene el mejor resultado de las pruebas aleatorias. El mejor está por encima de la media, así que éste es una fluctuación positiva. Nuestra teoría matemática de MAX requiere la especificación externa de cuatro parámetros correspondientes a cuatro procesos, en los cuales MAX captura las fluctuaciones correspondientes. Hemos utilizado la Evolución, tanto rudimentaria como automática y completa, para encontrar buenos valores para esos parámetros. Es sorprendente lo bien que se ajusta el resultado. Nuestra estimación de los parámetros confirma nuestro análisis anterior que dice que el Underdog, por debajo del valor medio predicho por MAX, es un combustible importante para la Evolución.  Pero como el ajuste no es perfecto para poblaciones pequeñas, se podrían considerar parámetros que dependen del tamaño  de la población y de la complejidad. \n",
    "\n",
    "\n",
    "## A Parable\n",
    "\n",
    "The la Evoluciónary Process is similar to an ancient couple, in which the husband works hard and sends the money to the wife, who saves it, so their capital grows. Here, the husband is MAX that selects the best of the fruits of mutation. Because this is a probability, there is variability in the population in the number of touched and solved sites.  Thanks to this variability, more than the half of sites get solved. Half by mere probability and the rest by positive fluctuations, which are an effect of the variability of the population from which MAX takes the best. Because the mutation rate is small, most solved sites are passed intact to the next generation, and so constitute the richness of the la Evoluciónary Process, as if they were gathered by a wife. Who is the wife?"
   ]
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",
      "text/plain": [
       "<Figure size 800x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#infographic.py\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "# Create a tall canvas with a background color\n",
    "fig = plt.figure(figsize=(8, 12), facecolor=\"#d5c1da\")\n",
    "gs = GridSpec(3, 2, figure=fig)\n",
    "\n",
    "# Add a header title\n",
    "fig.suptitle('Annual Data Summary', fontsize=24, fontweight='bold', y=0.95)\n",
    "\n",
    "# Subplot 1: Main Trend (spanning two columns)\n",
    "ax1 = fig.add_subplot(gs[0, :])\n",
    "ax1.plot([1, 2, 3, 4], [10, 20, 25, 30], color='#ff7f0e', lw=3)\n",
    "ax1.set_title('Growth Trend')\n",
    "\n",
    "# Subplot 2: Categorical Breakdown\n",
    "ax2 = fig.add_subplot(gs[1, 0])\n",
    "ax2.bar(['A', 'B', 'C'], [5, 10, 15], color='#1f77b4')\n",
    "\n",
    "# Subplot 3: Proportions\n",
    "ax3 = fig.add_subplot(gs[1, 1])\n",
    "ax3.pie([40, 60], labels=['Yes', 'No'], colors=['#2ca02c', '#d62728'])\n",
    "\n",
    "# Final adjustments\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.92])\n",
    "#plt.savefig('infographic.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d901c466",
   "metadata": {},
   "outputs": [],
   "source": [
    "from manim import *\n",
    "\n",
    "class Q1Q2Infographic(Scene):\n",
    "    def construct(self):\n",
    "        # Set dark-mode infographic background\n",
    "        self.camera.background_color = \"#1F1F2E\"\n",
    "\n",
    "        # Define Colors\n",
    "        TITLE_COLOR = \"#00E6FF\"\n",
    "        Q1_COLOR = \"#FF0055\"\n",
    "        Q2_COLOR = \"#00FF66\"\n",
    "        TEXT_COLOR = \"#FFFFFF\"\n",
    "\n",
    "        # 1. Title Header\n",
    "        header = Text(\"Q1 vs Q2 Performance Dashboard\", font_size=42, color=TITLE_COLOR)\n",
    "        header.to_edge(UP)\n",
    "        self.play(Write(header))\n",
    "\n",
    "        # 2. Add some visual grid dividing lines\n",
    "        line_h = Line(start=LEFT*6, end=RIGHT*6, color=\"#3F3F59\").shift(UP*1.5)\n",
    "        line_v = Line(start=UP*1.5, end=DOWN*3.5, color=\"#3F3F59\")\n",
    "        self.play(Create(line_h), Create(line_v))\n",
    "\n",
    "        # 3. Data Side: Q1\n",
    "        q1_label = Text(\"Q1 Target\", font_size=32, color=Q1_COLOR).shift(LEFT*3 + UP*0.5)\n",
    "        q1_number = DecimalNumber(0, number_to_evaluate=85, num_decimal_places=0, font_size=64, color=Q1_COLOR).next_to(q1_label, DOWN)\n",
    "        q1_unit = Text(\"%\", font_size=32, color=Q1_COLOR).next_to(q1_number, RIGHT)\n",
    "        \n",
    "        self.play(Write(q1_label), FadeIn(q1_number), FadeIn(q1_unit))\n",
    "        self.play(ChangeDecimalToValue(q1_number, 85), run_time=2)\n",
    "\n",
    "        # 4. Data Side: Q2\n",
    "        q2_label = Text(\"Q2 Target\", font_size=32, color=Q2_COLOR).shift(RIGHT*3 + UP*0.5)\n",
    "        q2_number = DecimalNumber(0, number_to_evaluate=98, num_decimal_places=0, font_size=64, color=Q2_COLOR).next_to(q2_label, DOWN)\n",
    "        q2_unit = Text(\"%\", font_size=32, color=Q2_COLOR).next_to(q2_number, RIGHT)\n",
    "        \n",
    "        self.play(Write(q2_label), FadeIn(q2_number), FadeIn(q2_unit))\n",
    "        self.play(ChangeDecimalToValue(q2_number, 98), run_time=2)\n",
    "\n",
    "        # 5. Call to action banner at the bottom\n",
    "        cta = Text(\"Metrics improved by 15% - View Full Report\", font_size=28, color=TEXT_COLOR).shift(DOWN*2.5)\n",
    "        box = SurroundingRectangle(cta, color=TITLE_COLOR, corner_radius=0.1)\n",
    "        self.play(Create(box), Write(cta))\n",
    "\n",
    "        self.wait(3)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}