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    ">> THE COST OF EVOLUTION IS EXPONENTIAL\n",
    ">>>>\n",
    ">>> José Rodríguez\n",
    ">>>>\n",
    ">>> jose@ejristos.com\n",
    ">>>>\n",
    ">>> 27 May 2026.\n",
    ">>>>\n",
    ">>> Bogotá, Colombia.\n",
    "\n",
    "**Abstract**\n",
    "\n",
    "Evolution is an informational machine that uses resources to produce complexity and function. The resources are parallel computing, given by population size, and serial computing, given by generations of the corresponding Evolutionary Process. Evolution is not magic. Evolution is real and can be studied deeply. In particular, nothing comes for free, everything has a cost. We have proposed the Underdog model to understand Evolution. Here we test this model for different predictions related to the cost of Evolution in terms of resources used to solve a model task. \n",
    "\n",
    "# INTRODUCTION\n",
    "\n",
    "Evolution is not magic. Evolution is real and can be deeply studied. It uses population size and generations to produce complexity and function. Thus, it is a tool to propose approximate solutions to the most varied type of problems. In consequence, Evolution has a cost. To quantify the cost, we give a task to Evolution and measure the needed number of generations for a given population size. But there is a problem. Evolution cannot solve complex tasks in bounded time. Thus, the task cannot be to find perfection. It also cannot be to reach, say, 95% of perfection. This also may take more time than available. The reason is that each task generates an Evolutionary Process that proposes better and better approximations but may end at a plateau below perfection, which is a stable state. This sub-process can be described deterministically. But, beneath it, a world of fluctuations agitates itself attempting to reach perfection. Since the plateau is stable, fluctuations, both positive and negative, are absorbed and the system returns to the stable state, the plateau. \n",
    "\n",
    "Our purposes for this contribution are: \n",
    "\n",
    "1. Show a plateau.\n",
    "2. Compare the plateaus for different settings of complexity that is given by the length of the target string that Evolution must match.\n",
    "3. Determine the ramp over which Evolution rises to the plateau.\n",
    "4. Compare different ramps for different settings of complexity.\n",
    "5. Test the power of the Underdog Model to fit the silhouette of the Evolutionary Process as described by the ascent towards perfection. As a byproduct, we expect to predict the form and height of plateau as a function of the complexity of the task and of the population size.\n",
    "\n",
    "Our experiments in this contribution include recombination, but the Underdog Model lacks it. So, the role of recombination could be immediately assessed if the model does not fit observations in the simulations. \n",
    "\n",
    "# EVOLUTION BATTLES COMPLEXITY\n",
    "\n",
    "We give to Evolution the task that consists in matching a binary target string. This is the *Binary Shakespeare Challenge*. Evolution battles with complexity by solving an instance of this problem and then facing a more complex one, in which a longer target string is given. \n",
    "\n",
    "Every task generates an Evolutionary Process that tends to a plateau. In some cases, the plateau coincides with perfection, in others the plateau ends below perfection.\n",
    "\n",
    "## The plateau\n",
    "\n",
    "Let us see in detail how Evolution always tend to a plateau.\n",
    "\n",
    "**Example.** The next program shows us what happens when a population size of 30 chromosomes battles against a task that consists in matching a complex binary string with 1000 bits. Evolution rapidly reaches a plateau that is below perfection, which is defined by matching all bits, and indicated by a red horizontal line. The drawing shows that fluctuations always work. They appear as a broken line that dances around an average constant value. \n",
    "\n"
   ]
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    "# Program NumberOfMatchedSites_VS_Generations.py\n",
    "# The task given to Evolution 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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    "**Exercise.** Run the previous program and play with it to see how the plateau behaves when the complexity of the task varies, which is given by the length of the target sequence. \n",
    ">\n",
    "> *Answer.* Evolution can find perfection for low values of complexity, in this case, around a length of the target string of 410 or less. When the complexity grows, the plateau falls down and perfection seems inaccessible in spite of fluctuations.\n",
    "\n",
    "**Exercise.** Compare the arisen plateaus from different settings of complexity. Tun on and off recombination to see the effect on your output variables.\n",
    ">\n",
    "> *Answer.* The following program draws the plots of the Evolutionary Processes for different values of complexity or length of the target sequence. To establish comparisons, each fitness history is normalized by the length of its target sequence, so that it runs in within 0 and 1. At last, we are comparing the relative proportions of realization of the posited task. Recombination is also turned on and off."
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lab: Simple Genetic Algorithm\n",
      "Purpose: compare diverse plateaus for different degrees of complexity\n",
      "Date of Experiment  = 2026-06-15 11:33:31.516162\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: 1111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111101111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111011111111111111111111111111111111111111111111111111111111111111111111\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",
      "Target sequence matched in generation 700\n",
      "Final chromosome: 11111111111111111111111111111111111111111111111111111101111111111011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111011111111111111111111111111111111111111111111111111111111111111111111111111111111011111111111111111111111111111111111111111111111111111011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111\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: 1111101111111111111111111111011111111011111111110111111111111101111101101111111111111111111111111111101110111111111101111011011111111011111110011111111111111011111111111111110010111111110110111111101011011111110111011111111110111111111001111111111111110111111111111111111111111111111101111111111110111111111110111111111111111101111111111111111110111111111111111101111101111111111101110111111111111101111111111111101111111011110111011111111100101011111111111110111101111110111111111111011110111110111111111111111111110111111111111111111111101111111111110111111111111110111111111111111111111111101111111111111111111111101111011101111110111111011111101111111111111011111111100111111111011111111111011111111111010111111111101111111111111111111011111111111111111101111111111110110110111111111111111111111111111111111111111111111111111111101101111111110101111110111111111111111111000101010111110101101011111111111101111111111111111111101111111101011111111111111101111111111111111101011111110111111111111011\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: 11011101011011111111001011011101110111101011110110111111111111111011101111101111111111111001101111111101011111111111111111111111111111111111001111111000111111001111011100011111111110101111111111111100111111111111000011101111101111111111111011110111111111111111111111011111110111100110111111011111110111111101110111101110111001111100011101111111100011111111111111011110100111111111011111010011111110111111111101111010111111101101110111110111111111110111111111011011101111011111000111111111111001111111111111111111111111100111110101111111111111111111001111111111110101100101101101011010111111111101111011111001011101111111111111111110111110100011111111101101000111111111011101111101101110110110110011111011111101111111011111001010111110001011111111101101110001101111111111110010111101111011011111101101011101111001110111111111111111110111011110001011100111011110101011110011010011111111110111111111111110111111111111111110111110111111111111111011111001111101110100111110011110111001111111111111100101011001111111101011111111110101111011011111111111011101011111111111011111111111011100111110101101011111110111011111111111001101111111111001111111011101111011101111110111111111111011011011111111111101111111111111110101111101011011110101111111111110111110111111001111011111011011111111110111101010001011000011011111011011110011111110011011111101100011111111101111110111011111111111010111101010111100001111111111111111011101111101111111111111010111110110011101111111111111111101110101111011111011111111011111101011111101111111111101110101111101010011111101111110110111111111111111111111111101111111111111011101111110110000111111111100011000111101111111000110011111001101111111111101010011110111101111111011110110111111110111111101011111111110110111011111111100111100110011111111111110101100111110111000011111110011111111101011111111111101110011111101110111000111101101111111011010111111110100111111111110010111111111111100011111111110111111001111111111111110111110111101101111111111011110111111100111100111\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: 01011111011001111110101101111111111011111101100110010011111100011100011101110111011111010001111111010111111010101111111111101101001010110011111110110101011111100101111101001111101111011110111011101101111011111110001101111101111100101111011010001110110110101010111111011001111111100001111101111011111111110111111101011111011010110101010011101111100110110111101111110111100110111111101011111111111111111101101111111111111111001010100101110111111111011111010110110101101111110100111110011111111110011111110011101101011011011101111011111111111110110011111111111101111011100001101010101111111101010111011001101110111111110100001110011001011101111011111101010110010100100101110101111111111110111001111011111000011011100111111110111111111010111111011011101110010110110011100011111110011000111101110011011101100111011111111111111011011001101011111010110110110010111111111111110110111100011110111111111110001011101110011111111101101011110011011100110011100111111111100111100001001111111111101110111011100011111111100111001111101111101101110011111111101111010010111011011011110111111011111111111110010011011011011100011000111101110111110010111111011111100111011011110111101001100010111111111111101111110111111001110111111110111111001111111111111001110111101110111111111111101100011010111111001111101111101111111001111100011111111111111010001011011001101011100011111111101011101010110101110011011010011011011011110011100101111011101111011110011010101101111011111011101111011101111101011001001111111111111101111010111111111110111011111111011111111111001111101101111111111111101001101101111111011001111010111111011011011011110011101111110111011110111110101011010111111001011111010101001110111111000100111100011010111110011111101110001001111010111111011001111111111001111001111111011101110111101111111011101110100011101001100111111111011010100011111111010110111111111111111111000100111111111010111011110111001110110111101101100101110110011001011010111010111011101110111010111100011001111110110000111100101111111111111101101100111110101111111101110101011011111111010100011001101111010111010101101100111111111011111110111011010100101110001010110111010100111101111111111111110010001110111110110010010111011110111100001111111101111111111011110111001001111000111111111100010111110001111101011100111100111001011111000101011111101111111111101101111111001101111011111111011001110011100111111111110010110011101011011111101111010111011001110111110110010111010011110111111101110100011101111111101011001011100000010111111101011010010010110010011111010111101111011101111111001100101111110001111010111111110110111111100111111101001011111011101011010110111101111111111111111100111111100111111111101111111111110101011110111111100111110101111111011111111011111001110011100100001111111110011111010111111101101010011101110111101010011011111101110101101101110111110110111011101000111111111110111110101001110110110011111011101111010011111100111010111011111010100110110011011101011111111001111111000101101011101010111111111011101101101110111111111111111111111111011111001101101110101101000101101011111011110110010110111111011111111011001001110101111011111110010101101111111111111001001111111110000111111101110111011000111101111111001111110011011011110000011010100110111011111011010101110001001100111011101101110011111011111111101000011110011101111101111111001001111111001111110101100111111100110110110101110110001110001101010111101111111101110111111011011010111111101010110111011111111110011111111111111010101110111111111111011111111111010110111100110011111011101001111011110001001101111100100011001011111011011111011111001101011101010101111110011010110011111111010110100001111001001110101101100100100110011111010010101010011101101111011011011110011110011100111101000011111110010111011001100101001011001110111111101111111010100111111111101111010100110100111010111011100000111110011111011100111010111111111101101100101101101011111111111001111010011101011011101010010010111011100111011100111100101111011111101110111111111111111001010110100100100110101011111101111111110100111111111111111111011010010111001100111000011111110011111001110111101111001011111111111011011101001111101111101111010111100111110111010001111011010111101110001110110101111111101100101100010100010001011010100111110111111101101011111110011010101111110111111010110101100110110111000111011010111011111100110111111110000111111110111010011111101111110011110111011100111011110001010111111010111111111010111101101101111001100110111111100011111001101100011110111101001111000011111111111101001011011101111011111101110101011110010111101111111010101110101101111011111111111110011101001111011000111010111011100101010010100101011101100111111111111111100111111000000101101000101111011001011111101101111111001110111111111001100111011110110111111001011001110111111001111101111011011110001001110110001011111100110110110111111001100011011111111010111010100011101101101111110110111111000110111100111110101101111111110100011100011111001101111111111111011111001011111111011110111110111110\n",
      "Total cost = population size * number of generations: 27000\n"
     ]
    },
    {
     "data": {
      "image/png": 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yuy2VGxhacjx48GD3/rx589xrfaZzi0o3Qs+dKqHTdKE5geltW02nz15//fXge5q3zkXKxY6Wcq21ndu0aROW3iqJ0fxVEhF5vtQ+nBnpnYdSO66Ue6p9MpSfK6oSjFBPPPGEe1/7VujxVqNGjbAcU13jdD3WvEOvd/p9lTi1bt06quVNj45rfU/fT0tmru+6HmrfCz2fjBs3zv1GVks6svt+Rf91Hj7llFMOyFX3v5eZbZ+WtK59aZUEpkelYioR1D6jHPNmzZq57/bp0+eA+4bIewP57LPPwvZFf39ZsWLFAdPqOFeNiMzOMzP841IlaqFUWqn3dd4Uv3ZJ165dw6a788473fsTJ0484HgLLdnQPbL2xwYNGgTfU+mcSq00rfbX7t27u+0aWVMktZKOSKpFovmEXl+iPf9mVPoYuo+olF0l0qFUqhN5zhGth75z1113edGKumKyoiZRDk4kRUSKsPyHGm/L33//7SI8RZh+DrQeiqqUi6GoSjl4oZRrFdqlq3KwFG3//vvv7rVyRVQaoRwdf356KDJTjm5qOTgZUa6Rci1V4hAZjUtGXcwqx1q5r1omn798yqlMj6Ji5ZQpZ8qnKD8yF0s5VYpw1XhPOfc+5aYpqlaj+Uip9TaRFZHzUZooDf19wteyZcuw0ha/REy5UcphiXx/2bJllt2U86l9JPSh3L2sUM5maKmPtrVKE/zl1rleuV/ab/Q8dH/U/q2cXL/XDeVu/fHHHy53NC2aRjkrKnWLlo4N5fgpJ04lGT61t1IumnK1/XRSrszu3btd7rhP39XxpM8yu04+5fJEU3c8rXPIsGHDws4foTkmqtur3BWVAIYui59rFHm8Z2eapbd+yi30c+OUBjoetF7KXc5qTys6hnUeU4lYKOW4aVmVg5WZdc3qPhWtrGxL5USHloT5JRT+MqtNj7alzoehbc9UGqIc5cxQeoSWqul3lTOXmfOOcil37drlcgxDc1+1fNrWanAZS6H7nbantq1K07QOeh1KJW5+aWXo9UW59iopCW3bFnq9EV1bdD3WOUPb309HlZTovP7NN98cdKcHWhelgRrcK4c5NdFe3/3roY7NUqVKBb+va6FKPrIiVvcrmpdyjyPbFPrfy45tH3nNS+sRuX+kRrUVVGJ+/vnnuxIalZbre08++aS7hvlU0yK1UhN/PfV56P+0pvU/z8w8syLynkqlVOLfO/n/e/XqFTad36A+8lhXSU1oyZfOB506dXK5/uoYRVS6pfTVeUglYYMHD3bX6rJly7raD9FS6bnSQmmizleyev5Nje4ntW+oFFWlxyq9jCxhyc50ibp6lX/TGLkwomoFOrBU1BN6klfLfW0M9USTVm80Kl7VSdGnrtRC+Rca/ySlg1PSKqpSwmelRx3dEGW1X3c1IFfRk276tM5+YugGXFWs1BtDWnRy0g2iijJDRfZ+4p/EdFMTSTdlCm78xuKhF6HskF6ahG7vyOn8i0HFihVTfT+tC4/oQq+LQCjdkGbUWElFr7oZyw6R6+Ovu7/c2m90gVSxb1rdY/qNxVStQzcwuulR2rZp08ZdZFSU71NVE11Etb1UzK5OCXQSCw0mImkZVP0grf1CF6uVK1e6k596mtPNu4r+Vfwqeq6TjH88ZWadMrufhZ5DQm8UFJT6x55O8KFd5up4V9F2WtU3IpclO9MsvfXzeylStUH11he6zH7Rd2bpGNeFLDRAF79I2z8HRLuuWd2nopWVbZnR+d1fx8jznwKQzI5poqobkRlG+r3MdOue1nlXN8/ahpFpkt1UfUvVbb7//nt3nIfSTUXocZTafqrlU2AauR0it69/XU2tWm/o72U28AulmxZVl9IxrpsuVQtR9Vvtj37GULTXd3+7p9abUVYD/1jcr6jzHUnv3iI7tn12XfNSo31H1at0j6GA0b/HUxCp6qaRVHXY/zz0f1rThgbW0c4zKyL3FR0XykhQMODvU3odeWxo31TmTeSxrukijyu/hy/N09+n9Z6qDOoasXDhQtdMQedlBaw6ZjNKO92bqlqj9jtVBfN/Myvn39ToXOYvg45HBbq6L1EVer3O7nSJOujQyU03x/Pnzz/gMz/n2k88nx+dq/5uWhF2ZAKndVPpt0nw56lETC0HO949M2m5VL9VN/yp5bAo0XWTlVoJUaxlV88lGaVJRtNF+/1Q6sFC/XOH0s1dPAdTi3Zf1Ek4rQuGX69bN46qc60TjnIflUOhG1aVzPjd5imHTTllqjuqEojHH3/cXaRVMpGZOujpUYnGgw8+6HJDdHOrXC3lKvrHTWbWKbP7md9Dic4hocGWboj9wFQX1tD6yVoeBZLKZUtNZECbnWmW3vqpnZRuTJT7pDYffrsh5YjHqxvcaI6rWO5TWdmWWTkXZFU8fysWdMOqGwAdN9r/ta/rBkE5sqrbHbmfHcz53p+X9o+0uh/OjmuYjg/lzKpmgW5idQypzrxKGBo0aBCT63taNRUixwOKxf1KvLa9n7MezX1cVvYT/zwbmhGo+0GVOEXy31MGij+d/37k+VrvhbZXiXae2SGt/SLawZMzS/tKnTp13EM9ROn+RpnSGQUdqt2ikmq1+w3N5M3K+TcaahendNCy+UGHXquUUft16PbJSrpk6ghWjr0aEWvlM2rYJH5umqoPZVck7lcnUBSW0Tyj3XmUi6rETC2gyoiKHlXkqGKpyEY2yu1QNKsTbFqNZ9XQSImpXKzQ0g6/xCR0OkmtO1GNkaLc6tBSjsyI1UF2MJQrryK/UFmtJhWr9dZ+oxt3Xbyi2b+VPrrp10MlOcq9UACgxoV+MaUObjUU1UMBqxrVaZq0bhC1DNpv0tovdCMceqLXbyvIUdCj3EblooT2sZ3ZdcoMncDUkFgns9CgI6PjXX226+YrO/bT7Fo/NUrWReOVV14Je185T37jWsnMMvuNDlVqHFraoXT0P8+KzO5T0YrFvuKvo85/oZkOauSoTK3Qi2g8zluh593Q0iEdv8oEyY71Tms9VN1BuYvKGAjNUc9MFWItv3JXI28WIq8v/nVV18Hsuq6mRb+l0g49lMuvG211x6kaA9Fe3/108UsJQkWeC/0SgsjBDCNzrmN5v6J7i7TGbsnMtk9L5PAFaVFjZ93EZpZfJTG0xFnppkbuuvkNrXqo6py6Jvm5/n4gpaqTofeNupHWvZPfgU1m5pkV2ldCSwN1DOh3/IxM7VN6relC7+VUg0f7TuT51y8ZCz0e1NBdMsocbfhfNf7UAqxQul7q/lGZRH6mXTyu1SrBCK2+qXTRvb9qHYRmrvsdgGRmnKRMdTaven5KeOXuKSEyiu514lB7D1W/Sm3jpjbqYUaUA+H376z66enN078Jz2jkVO3cqmenk7wOjMzkWvhVq9TDjHo8CH2o3qyK9HSTld76aD1C6/dpx/fbxYSeVJSw6lkhdH10MlMOpqpNZJWWP9lGl9WFQgdS6CO7x9mIdv9IL+dCVYN0A59awBq6L6qubijlWOrg1b6l9NeJI7KOto4f5SCkVqwZugyqqqUez0JLGnV8qpcatY8IzR3RyVQ5LapWpYf2K42OmpV1yiwFGqpzraJgLW9qIo815dSrHnVq9V9Vj1QljJmRXeun+UQuq9qfRNb5zsw+pmNY+4HfD7pPudq6sGU2SMjqPhWtWOwruhireprSO3S0Yp1DI6tjHuzxGw2dd3Ssqqeb0PRWsKltm17V2WiltR5+Lnro7+o3deMYLV1ftE8qcAm9oYg8nlT1Tje///d//5dqFeqsXFcjKWPNr47h02/qxsnfH6O9vodeD0P3cWVUKcgKpZtFbUu1jQilkuZY36/o3Kz1U2lO5Lr76ZqZbR/rNh0qyYgsAVI66OZXx0FoRoDucXSdCW0jqFJqnQdVmuW3AVDVXt0w67wfOm/1SKbzWui4WtHOMysi76nU86f451X/Hkq9RoXyS9kjj3UFTaE9WikDT9WftF/6GaQKoFLbj8f+134ktWrRPmVAqf2GBuVLbeDmgz3/6toZWWVTND+da0PbN6stiYLx0GNG+6/aY6raV0a9tGa5pEM30LqRUXUMbSx/RHL9uHJ99Jlu4EO7rFNC68ZHNzq6CVdugnYq1VFVlKtczMzQCUk7qwYZU46dcmkV8anLPTX00Y2Nf9HWwSxqmKmDTYmU1siJOsnp5l2N9PyuOXXi0c6uxripDfikE6USSDdSad0QqwGf6n4rh1EntUjamRT9K9dHkbMOTl0g/GLM0Chaxa86QFQ0pzr5fpe5KjJNbdT1aGk7aQfXwaUbEuUGZKZb5JzK3z90UGu/0EGlE1tmSox0MlbOo7aX9m8FEko71SnWNvXTURcfnYi0f6qEQTkG2k91ItNFSRdwHTc66eqYUnG6vq+G58oFTI8GodQFRceZcrNVBUEXTu2fqjsaSaUdqtalfVb7UWT3hNGuU1YoSFc3ptrvtS/rpk4Bpj8iuW4MQm+udZyry0p1ZqBl0vbThUu5/3pfVTRS6/whPdmxfiq1UemmGkbrhKuOJHRjHNlWQjcTOnfo5Kx01r6l302t/r32PV3UtT8qgNR+oHOSAjRVS4nsyjojKjGJZp9SPW39rtoOZPY8kt37im5stAxq5Kl6/Qo6tS3U7Whk24TMbNus0rVFJZEqHdR+q/O5ctJ18dWovJnp/jmz5yGdM7Q99Fzd4OqGVMGCriMZ5ZD69D2dZ3TNVpenfrUJ/3rlb0+dA5STqWNPN4nar3UzoYBF6avrrt/ldVbPm8oFVoml0lT7ic5TumnT/YB/Xc7M9V038jp/6rynjFDta7oeavlDb951fVRXpvpM66v9RtVcU6vvHov7FWUaqCtW7S9qx6fzneajGz4FTZnZ9mnJrpxu3XvoeqJzho4jbVPd1+mmVvdIobUNNI3a5Wh5Fej5o4fr/Bw60rZ/76JjR/u00lTzUzpqu4SWKmRmniqx0faLttq1ptMy6DhWeupapPTQuVH0X9WUFBzpeqx7QdXq0W/oehVZ3VulLrp+6nyqa/qrr77q9pXQTAFVZdWo36rV4JfSzpo1ywUnqpKbXpe2Oma17+u+W8saSvec+s2DOf+qREf7je4HdN+p/VCZ7votbc/QLpJ1HdGyKh0VRGlfVgmMgiqdT6IeGFC8LFA3jTfddJNXvXp1N3iTugRTF3wa9Ct0cCyfuvJUl67qrlBddmpgoHPOOcf74IMPDuiCLrLL2rS6u9NrdTGnbsG0DBo4TIMOaWAT3549e7zbbrvNDYikLidDVzey60d/gD8tp6ZX15fqIkzdTaY1OKDf3W16A7FMmjTJTaOuIdMaEExdLF555ZXBwQG1HuoKVN8bOXJk2LRfffWVd+qpp7ptrkGE1L1dWoMDptZ1Y2pd5i5atMh1rep37RY5OGDkfPy0Cu0yUa8ju+b0uySM7CIxmoH8Yj04oE8DE2l/VLeskd3rptbVqNIusnvhtWvXumkrVqzo9m/t5+p68cUXXwzrPljbWN2Bat/S/qoB7tTNnmgf02sNNOQP8KfnqQ0EltbggDoeNPiUBlHSgGIajC81GozN7x4vrcGnolmnrKajuo5UF9FNmjRx+3CBAgXc/HVO0IBZOm5D7dq1y3Xh6A8CpkG7NGDXgAEDgtsvFmmW3vqpS00NDqjuCnXc6JjUYIypdXuogcU0YJPWM7TbytTOBeoyVN1uaiA7LZe60kxvcMD01jXafUqDQmp+6oY4I6kdQwezLdMaoE0DaWpdlN4nn3yyOx8qzTUAYzTb1h8cMFJq2zwa6iJX1zitnwYv0/UvchDMrHaZm9556NNPP/Xq1q3rrnEaZEzHgbrpjTz/+oOVpWbZsmXuM+2nurZpv/WvXZED/c6ePdu76KKLgucpzffSSy91g9JGs7zpUbee2k+0HbUv6lqnrmTVFWikaK7vovVQN8xaVu0HqQ0OKEoTdZWsc6POHzfccIPrzjS1fS8W9ytKR3W761+3tU9r0NGsbPtY0vbVPYU/eKOuJxqkMbU0EnW/rm5btczatjruUht2wB/IUd3Gat3ULes999zjzu1ZnafSU9szrcFoI49L3Sep+3GdC7UP3HrrrakODqjriroqVtrrnJbR4IA6PrVO2q8jz286b2mf1yCy2pcLFizoVapUye3LoV3cS+S1I72uj0P3r2jOv6nRMaFBF/3jUemt640GD03tHKYuwx966KHgQLM6v7755pteZqX8t3JIMooi1R2bSlmirf8OAJmlarPqDEMlrQdTfSGWVOVUuX7KMcxMV5NInaqQqEci5d6H9saUGygHXKV3kR3bIHdRTr96PlPuezSDe6q6UWh7u4OhkgD1SKYSM2ROptp0IDYi+zhWUaKKglWsmh2jBgNAWlQ8r16EkiXgUN33yLwwVUdQVYHQUXKRteuLtq+qX6raRm4LOJA3aOR47dfqih45S3z7l0WqVH9ZB5DaaqgevhpRqctY1aHMrm5vASA16Q1YmQjTpk1zufCqh69G5aqfrIbbylnUe9lFOZ+RjWZDqT1F6ECsmaV2Bak1DM7s2EMHS6VD6v1KDVzV6Fp1ttUmKr0OTjJL881ogLDs7n0QeZfavkQOToycgaAjCajBpBp2qqhOuVDqWk8lHbfeemuiFw0A4kpVF9TNs3qMUumGbvxVjcLvQSe7qDFkeoP7qSGpqulklXojimz8GikeYw+pExU1VFaQoSBLjU1HjhzpGpBmFzU6VYPb9FCTGwBtOgAAeY5G+04vd169DPk9NWWFxjbwxzdIi3pKyu6uwBNBPQ2pC9H0xHLUbAA5A0EHAAAAgJiiITkAAACAmKJNB2LWxaWK2zVoV+iAXgAAIHmp/Y0GF9VgwZGDxwIHg6ADMaGAQ41BAQBAzrNy5Uo3GjWQXQg6EBMq4fBPWhpvJLvs3r3bxo0bZ23atLGCBQtm23yRdaRJciE9kgvpkVxIj4ypO1plGvrXcSC7EHQgJvwqVQo4sjvoKFasmJsnQUdyIE2SC+mRXEiP5EJ6RI+q0chuVNYDAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAAAgpgg6AAAAAMQUQQcAAACAmCLoAAAAABBTBB0AAAAAYoqgIwe5//77LSUlJexRo0aN4Oc7duywW265xUqXLm0lSpSwDh062Nq1a8PmsWLFCmvfvr0VK1bMypQpY71797Y9e/aETTNp0iQ78cQTrXDhwla9enUbMWJE3NYRAAAAuQ9BRw5zwgkn2OrVq4OPKVOmBD/r2bOnjR492t5//32bPHmyrVq1yi666KLg53v37nUBx65du2zq1Kn22muvuYDi3nvvDU6zfPlyN82ZZ55pc+bMsR49eljXrl3tyy+/jPu6AgAAIHcokOgFQOYUKFDAypUrd8D7GzdutFdeecXefvtta9GihXtv+PDhVrNmTZs2bZo1btzYxo0bZwsXLrSvvvrKypYta/Xr17dBgwZZ3759XSlKoUKFbNiwYValShV74okn3Dz0fQU2Tz31lLVt25bkAgAAQKYRdOQwS5YssQoVKliRIkWsSZMm9vDDD1ulSpVs5syZtnv3bmvVqlVwWlW90mfff/+9Czr0v06dOi7g8CmQuOmmm2zBggXWoEEDN03oPPxpVOKRnp07d7qHb9OmTe6/V6OGefnSL1Dz6te3vR99FPZe/gsvtJQ5cw6YNr/nWZudOy1/4cLmpaTYvu7dbV/osm3ebAXq1rVo7PnwQ7MTTwy+TvnsM8t/660Zf7F4cdszf37YW/nuusvyvftuhl/d166d7Rs6NHydGje2lIhqcKnZ+9BD5l1xxf43Fi+2AmedFd26Tp1qVr588HXKyy9b/gcfzPB73rHH2t5x48KXt1MnS/n22zTTxLfvuutsX//+Yd8tUKVKVMu7d8QI8844Y//yTp5s+a+9Nqrv7lm+POx1vkGDLN+rr2b4Pa9ZM9v7+uth7+Vv08ZSlizJeHnvvtu8rl33v7F6tRVo2jS65f3iC7Pjjw++TnnnHcv/v/9lvLxly9readPC3st3882Wf+zYVNMj1L7LLrN9jzwS9l6B2rXNtm7N8Hf3DhliXvv2+9+YNcsKdOhg0djz009mhxyyf3mfftryDR6c4fcyc46IlOhzhM7LTu/e5n3wQZ48R6QlEecIna9s6ND96ZIHzxH5Pv88/e/t2xfVcgGZRdCRg5xyyimuOtTxxx/vqlYNGDDAmjVrZvPnz7c1a9a4kopDDz007DsKMPSZ6H9owOF/7n+W3jQKIrZv325FixZNddkU/Gh5IqWsXm2p3/bs90+JEvbt2LFh7zX79Vc7/M8/D5yfmYUuwZKZM21xyHcLbNtm7VP5Xmq+nzTJ/vlvvaXC1KnWKIrv7i5a1MZGLG/9n36yylF8d83ChfZjxHfb/P67Ff3rrwy/O2/6dFtRqlTw9SErVliLKNd14vjxtuOII4Kvq82YYbWj+O7mfPns64jlbbJokZUJ+W5kmviWz51rCyK+e36Uyzt9yhRbH3IDfOTs2dY0yu9Gps0Jc+da9Si+u37RIvs+4rtnLl1qJaP47qIZM2xphQrB10U2bLC2US7vt19/bZuXLg2+rjR9ujWI4rtqwzUuYnkbLlxoR61alWp6hPrjp59sTsR3z1650gpu357h786eOtVWhQQzhy1aZKdHua7jvvzS9hQrFnx9/MyZViOK72bmHBEpWc4RqxYsyLPniLQk4hzh77njx4/Pu+eIDL6b0TUbyCqCjhykXbt2wed169Z1QUjlypXtvffeSzMYiJd+/fpZr169gq8VpFSsWNG88uUzLOk4tHp1O/vss8Pey//SS+Zt2XLAtJ7nuRIVNXJXQ/pjTzrJqoV+d/Nm8446KqplbtK8eXgupudF9d0CxYsfsLz5vvnGvJ9/zvC75WrVOnBdK1c2r0iRDL9b5+STrXbodxcvjnpdW7RuHZ6LuWqVeRG5k6kpUa3agcs7cqR569enmSa+KvXqWeWI70a7vCefdlp4Lmbx4lF/94C0mTnTvJkzM/zeETVqHLiuTz8dVa5fjUaN7PjQ765eHfXyNjvzzPBczI0bzfv44wy/V7hs2QPXdcwY2/fbb6mmR6ij69a1ChHfLaDjNYqSjgZNm1r90O+WKxf1urZRFc3Qko5ffjEvpF1adpwjIiX6HKEcdd3gVjjhhDx7jkhLIs4ROl9J69atrWDBgnnyHOH9/nu633Prs3p1VMsGZEaK5x+ByJEaNWrkqkPpBNqyZUv7559/wko7FJSoapQamavB+KeffuoaiIc2HK9atarNmjXLVa86/fTTXc9VTz/9dHAatQ3RPNRuJFoKOkqVKuW+U7JkyWxbX13AlYOoE6l/wUBikSbJhfRILqRHciE9Enf9Bui9KgfbsmWLLV261MqXL28nnXSSuwmfMGFC8PPFixe7LnLV9kP0f968ebZu3brgNMqB00mlVq1awWlC5+FP488DAAAAyCyCjhzkzjvvdF3h/vbbb67L2wsvvNDy589vV1xxhcuV6NKli6vi9PXXX7uG5Z07d3bBghqRS5s2bVxw0bFjR5s7d67rBveee+5xY3uoKobceOONtmzZMuvTp48tWrTIhg4d6qpvqaQEAAAAyAradOQgf/zxhwsw/vrrLzvyyCPttNNOc93h6rmoW9t8+fK5QQFVp1u9Tilo8ClAGTNmjOutSsFI8eLF7ZprrrGBAwcGp1F3uZ999pkLMgYPHmxHH320vfzyy3SXCwAAgCwj6MhBRo4cme7n6kb3ueeec4+0qI1HZK8qkZo3b26zZ8/O8nICAAAAoaheBQAAACCmCDoAAAAAxBRBBwAAAICYIugAAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAAAgpgg6AAAAAMQUQQcAAACAmCLoAAAAABBTBB0AAAAAYoqgAwAAAEBMEXQAAAAAiCmCDgAAAAAxRdABAAAAIKYIOgAAAADEFEEHAAAAgJgi6AAAAAAQUwQdAAAAAGKKoAMAAABATBF0AAAAAIgpgg4AAAAAMUXQAQAAACCmCDoAAAAAxBRBBwAAAICYIugAAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAAAgpgg6AAAAAMQUQQcAAACAmCLoAAAAABBTBB0AAAAAYoqgAwAAAEBMEXQAAAAAiCmCDgAAAAAxRdABAAAAIKYIOgAAAADEFEEHAAAAgJgi6AAAAAAQUwQdAAAAAGKKoAMAAABATBF0AAAAAIgpgg4AAAAAMUXQAQAAACCmCDoAAAAAxBRBBwAAAICYIugAAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAACAoAMAAABAzkVJRw72yCOPWEpKivXo0SP4XvPmzd17oY8bb7wx7HsrVqyw9u3bW7FixaxMmTLWu3dv27NnT9g0kyZNshNPPNEKFy5s1atXtxEjRsRtvQAAAJC7FEj0AiBrZsyYYS+88ILVrVv3gM+uv/56GzhwYPC1ggvf3r17XcBRrlw5mzp1qq1evdo6depkBQsWtIceeshNs3z5cjeNgpW33nrLJkyYYF27drXy5ctb27ZtSTIAAABkCiUdOdCWLVvsqquuspdeeskOO+ywAz5XkKGgwn+ULFky+Nm4ceNs4cKF9uabb1r9+vWtXbt2NmjQIHvuueds165dbpphw4ZZlSpV7IknnrCaNWvarbfeahdffLE99dRTcV1PAAAA5A6UdORAt9xyiyuJaNWqlT3wwAMHfK7SCQUVCjjOPfdc69+/f7C04/vvv7c6depY2bJlg9Or9OKmm26yBQsWWIMGDdw0mncoTRNajSvSzp073cO3adMm93/37t3ukV38eWXnPHFwSJPkQnokF9IjuZAe0W8jILsRdOQwI0eOtFmzZrnqVam58sorrXLlylahQgX76aefrG/fvrZ48WIbNWqU+3zNmjVhAYf4r/VZetMokNi+fbsVLVr0gN99+OGHbcCAAQe8r5KV0Opd2WX8+PHZPk8cHNIkuZAeyYX0SC6kR9q2bdsWx5RAXkLQkYOsXLnSunfv7k6WRYoUSXWabt26BZ+rREPtMFq2bGlLly61atWqxWzZ+vXrZ7169Qq+VoBSsWJFa9OmTVj1ruzIgdH6t27d2rVDQeKRJsmF9EgupEdyIT0y5tdUALIbQUcOMnPmTFu3bp3rVSq0Yfg333xjQ4YMcdWb8ufPH/adU045xf3/9ddfXdChKlfTp08Pm2bt2rXuvz7z//vvhU6j4CG1Ug5RL1d6RFJgEIvgIFbzRdaRJsmF9EgupEdyIT3S3zZALNCQPAdRicW8efNszpw5wUfDhg1do3I9jww4RO+LSjykSZMmbh4KXnwqOVBAUatWreA06rEqlKbR+wAAAEBmUdKRgxxyyCFWu3btsPeKFy9upUuXdu+rCtXbb79tZ599tntPbTp69uxpp59+erBrXVV3UnDRsWNHe+yxx1z7jXvuucc1TvdLKtRVrkpO+vTpY9ddd51NnDjR3nvvPfvss88Sst4AAADI2SjpyEUKFSpkX331lQssatSoYXfccYd16NDBRo8eHZxGpSFjxoxx/1VycfXVV7txOkLH9VB3uQowVLpRr14913Xuyy+/zBgdAAAAyBJKOnI4jRzuU8PtyZMnZ/gd9W41duzYdKfRyOazZ8/OlmUEAABA3kZJBwAAAICYIugAAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAAAgpgg6AAAAAMQUQQcAAACAmCLoAAAAABBTBB0AAAAAYoqgAwAAAEBMEXQAAAAAiCmCDgAAAAAxRdABAAAAIKYIOgAAAADEFEEHAAAAgJgi6AAAAAAQUwQdAAAAAGKKoAMAAABATBF0AAAAAIgpgg4AAAAAMUXQAQAAACCmCDoAAAAAxBRBBwAAAICYIugAAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAAAgpgg6AAAAAMQUQQcAAACAmCLoAAAAABBTBB0AAAAAYoqgAwAAAEBMEXQAAAAAiCmCDgAAAAAxRdABAAAAIKYIOgAAAADEFEEHAAAAgJgi6AAAAAAQUwQdAAAAAGKKoAMAAABATBF0AAAAAIgpgg4AAAAAMUXQAQAAACCmCDoAAAAAxBRBBwAAAICYIugAAAAAEFMEHQAAAABiiqADAAAAQEwRdCTApk2b7OOPP7aff/45ET8PAAAAxBVBRxxceumlNmTIEPd8+/bt1rBhQ/de3bp17cMPP4zHIgAAAAAJQ9ARB9988401a9bMPf/oo4/M8zz7999/7ZlnnrEHHnggHosAAAAAJAxBRxxs3LjRDj/8cPf8iy++sA4dOlixYsWsffv2tmTJkngsAgAAAJAwBB1xULFiRfv+++9t69atLuho06aNe/+ff/6xIkWKxGMRAAAAgIQpkLifzjt69OhhV111lZUoUcIqV65szZs3D1a7qlOnTqIXDwAAAIgpSjri4Oabb3YlHa+++qpNmTLF8uULbPaqVaseVJuORx55xFJSUlxQ49uxY4fdcsstVrp0aRfkqCrX2rVrw763YsUKV7VLVbzKlCljvXv3tj179oRNM2nSJDvxxBOtcOHCVr16dRsxYkSWlxMAAAB5G0FHnKjHqgsvvNAFAnv37rU5c+ZY06ZN7dRTT83S/GbMmGEvvPCC6wErVM+ePW306NH2/vvv2+TJk23VqlV20UUXBT/Xbyvg2LVrl02dOtVee+01F1Dce++9wWmWL1/upjnzzDPdciqo6dq1q3355ZcHsQUAAACQVxF0xIFu2l955ZXgTf8ZZ5zhShHU1kMlCpm1ZcsWV13rpZdessMOOyyswbp+58knn7QWLVrYSSedZMOHD3fBxbRp09w048aNs4ULF9qbb75p9evXt3bt2tmgQYPsueeec4GIDBs2zKpUqWJPPPGE1axZ02699Va7+OKL7amnnsq2bQIAAIC8gzYdcfDBBx/Y1Vdf7Z6rFEIlCYsWLbI33njD7r77bvvuu+8yNT9Vn1JJRKtWrcKqZ82cOdN2797t3vfVqFHDKlWq5Kp3NW7c2P1XO5KyZcsGp2nbtq3ddNNNtmDBAmvQoIGbJnQe/jSh1bgi7dy50z1CB0AULY8e2cWfV3bOEweHNEkupEdyIT2SC+kR/TYCshtBRxxs2LDBypUr556PHTvWLrnkEjvuuOPsuuuus8GDB2dqXiNHjrRZs2a56lWR1qxZY4UKFbJDDz007H0FGPrMnyY04PA/9z9LbxoFEhrcsGjRogf89sMPP2wDBgw44H2VrKjtSHYbP358ts8TB4c0SS6kR3IhPZIL6ZG2bdu2xTElkJcQdMSBbthVpal8+fKuy9znn38+eGDnz58/6vmsXLnSunfv7k6WydbVbr9+/axXr17B1wpQVH1M3QOXLFkyW3NgtP6tW7e2ggULZtt8kXWkSXIhPZIL6ZFcSI+M+TUVgOxG0BEHnTt3tksvvdQFHeptyq+69MMPP7jqT9FS9al169a59iA+tRFR17tDhgxxDb3VLkOjnYeWdqj3Kr+kRf+nT58eNl+/d6vQaSJ7vNJrBQ+plXKIernSI5ICg1gEB7GaL7KONEkupEdyIT2SC+mR/rYBYoGgIw7uv/9+q127tiupUNUq/+ZcpRx33XVX1PNp2bKlzZs374CARoFL3759XcmCThYTJkxwXeXK4sWLXRe5TZo0ca/1/8EHH3TBi7rLFZUcKKCoVatWcBpVAwulafx5AAAAAJlB0BEn6v3JH0fDd80112RqHocccogLXkIVL17cjcnhv9+lSxdXzenwww93gcRtt93mggU1IhdVd1Jw0bFjR3vsscdc+4177rnHNU73g6Ebb7zRlZz06dPHtTuZOHGivffee/bZZ58d9HYAAABA3kOXuXGgKlDqlvaoo45y43QsW7bMvd+/f/9gV7rZRd3annPOOa6k4/TTT3dVpUaNGhX8XKUrY8aMcf8VjKhXrU6dOtnAgQOD06i7XAUYKt2oV6+e6zr35Zdfdj1YAQAAAJlFSUccqDqTBuFTycL1118ffF+lE08//bQrnciqyHE+1MBcY27okZbKlSsfUH0qUvPmzW327NlZXi4AAADAR0lHHLz++uv24osvugH9QnurUimCxusAAAAAcjOCjjj4888/rXr16ge8v2/fPgbhAQAAQK5H0BEHarj97bffpjpSuUYABwAAAHIz2nTEwb333ut6qlKJh0o31LBbXdmq2pUadQMAAAC5GSUdcXD++efb6NGj7auvvnJd3CoI+fnnn917GlkbAAAAyM0o6YiTZs2auS5oAQAAgLyGoCOOdu3a5UYCVxWrUJUqVYrnYgAAAABxRdARB0uWLHEje0+dOjXsfc/zLCUlxQ0eCAAAAORWBB1xcO2111qBAgVco/Hy5cu7QAMAAADIKwg64mDOnDk2c+ZMq1GjRjx+DgAAAEgq9F4Vp3E6NmzYEI+fAgAAAJIOQUccPProo9anTx+bNGmS/fXXX7Zp06awBwAAAJCbUb0qDlq1auX+t2zZMux9GpIDAAAgLyDoiIOvv/46Hj8DAAAAJCWCjjioUqWKVaxY8YBeq1TSsXLlyngsAgAAAJAwtOmIU9Cxfv36A97/+++/3WcAAABAbkbQEQd+241IW7ZssSJFisRjEQAAAICEoXpVDPXq1cv9V8DRv39/K1asWPAzjUL+ww8/WP369WO5CAAAAEDCEXTE0OzZs4MlHfPmzbNChQoFP9PzevXq2Z133hnLRQAAAAASjqAjDr1Wde7c2QYPHmwlS5aM5c8BAAAASYmgIw6GDx8ej58BAAAAkhJBR4xcdNFFNmLECFe6oefpGTVqVKwWAwAAAEg4go4YKVWqVLDHKj0HAAAA8iqCjhhWqZo4caKdfvrpVK8CAABAnsY4HTHUunVrNwCgr3Hjxvbnn3/G8icBAACApEPQEUPqKjfUggULbOfOnbH8SQAAACDpEHQAAAAAiCmCjhhSQ3K/MXlqrwEAAIC8gIbkMa5e1bJlSytQILCZt23bZueee27YyOQya9asWC4GAAAAkFAEHTF03333hb0+//zzY/lzAAAAQFIi6Ihj0AEAAADkRbTpAAAAABBTBB0AAAAAYoqgAwAAAEBMEXQAAAAAiCmCjjh4/fXXUx2JfNeuXe4zAAAAIDcj6IiDzp0728aNGw94f/Pmze4zAAAAIDcj6IjTIIGpjUT+xx9/WKlSpeKxCAAAAEDCME5HDDVo0MAFG3qEjkwue/futeXLl9tZZ50Vy0UAAAAAEo6gI4YuuOAC93/OnDnWtm1bK1GiRPCzQoUK2THHHGMdOnSI5SIAAAAACUfQEYcRyRVcXH755Va4cOFY/hwAAACQlGjTEQctWrSw9evXB19Pnz7devToYS+++GI8fh4AAABIKIKOOLjyyivt66+/ds/XrFljrVq1coHH3XffbQMHDozHIgAAAAAJQ9ARB/Pnz7eTTz7ZPX/vvfesTp06NnXqVHvrrbdsxIgR8VgEAAAAIGEIOuJg9+7dwfYcX331lZ133nnueY0aNWz16tXxWAQAAAAgYQg64uCEE06wYcOG2bfffmvjx48PdpO7atUqK126dDwWAQAAAEgYgo44ePTRR+2FF16w5s2b2xVXXGH16tVz73/66afBalcAAABAbkWXuXGgYGPDhg22adMmO+yww4Lvd+vWzYoVKxaPRQAAAAAShpKOOPE8z2bOnOlKPDZv3hwcIJCgAwAAALkdJR1x8Pvvv7t2HCtWrLCdO3da69at7ZBDDnHVrvRa7T0AAACA3IqSjjjo3r27NWzY0P755x8rWrRo8P0LL7zQJkyYEI9FAAAAABKGko44UK9VGpdD1alCHXPMMfbnn3/GYxEAAACAhKGkIw727dtne/fuPeD9P/74w1WzAgAAAHIzgo44aNOmjT399NPB1ykpKbZlyxa777777Oyzz47HIgAAAAAJQ/WqOHjiiSesbdu2VqtWLduxY4ddeeWVtmTJEjviiCPsnXfeicciAACAJLVundkPP5gVL2523HFmRx2lDMpELxWQvQg64uDoo4+2uXPn2rvvvuv+q5SjS5cudtVVV4U1LAcAAHnHvn1mTz5p1rt3+PslSpide646ojE75ZT97+/ebVawoLrhN/vpJ7Patc3y54/7YgNZQtARJwUKFHBBhh4AACD327bNTP3FHHus2UsvmX3wgVmlSma//Wb21VcHTl+hgtmqVWZbtpipIoRfGeLQQwMlHwo62rQxmzrVbM0as++/N2vcOO6rBWQJQUcc/PXXX1a6dGn3fOXKlfbSSy/Z9u3b7dxzz7XTTz89HosAAGF0U6OC1tBc0gULzB591CxfPrPOnc10eqKKB5LdnDlmd91l1qxZoMRAHUWq75YPPzTr08esaVOzFi3Mdu40u+662C2HAoCBAwP/VRpx+OGB4GLXrui+/+OPZiedpHsGszffNHvmGbNlywKf/fvv/ulGjQr8L1LEbOlSgg7kHDQkj6F58+a5bnHLlCljNWrUsDlz5lijRo3sqaeeshdffNFatGhhH3/8cdTze/75561u3bpWsmRJ92jSpIl9/vnnwc+bN2/uGqmHPm688caweWiAwvbt27uR0LVcvXv3tj179oRNM2nSJDvxxBOtcOHCVr16dRsxYkQ2bA0AB2P7drPXXjO7557ADYnfIZ7+p9I5Xtj3/v57/2vllCqgUMd5BQqYnXWW2Ysvmk2bZtaypdkbbwR+p3nzwHR//BE+vxkzzP73P7PZs0nPZLd5s9mvvwbaC/zyi9mmTfs/03Ole2q57bGinPnx480WLdof5N59t1mnTmb9+gVy9dPblyMpiND8zjzT7MsvA8dG4cJmVasGSgwuu0yD8wbme/31ZrfeqkA6v7377nH2+usp7jtTpuz/TV0KdXM/aVLgZl/HxMknBwJvtbF4/XWztWsDVZv0HR1XCjJ0GX/hhUBw88UXZhs3mm3YENjmqQUcqvBQs6ZZ+fJmtWqZvfuu2T//BAIOUR6lqlX9/LPZ4MFmn3wSKNl46y2N7xWYRqUdWlYqTyAnSfE8HT6IhXbt2rlqVXfddZe98cYbNmbMGNegXCUdctttt9nMmTNtms5sURg9erTlz5/fjj32WFOyvfbaa/b444/b7Nmz7YQTTnBBx3HHHWcDdRb8j4ILBSiibnvr169v5cqVc99bvXq1derUya6//np76KGH3DTLly+32rVru2Cla9eubvDCHj162GeffeaWPVqbNm2yUqVK2caNG4O/nx12795tY8eOdb1+FVRWEhKONIm9kSPNevUyW716/3uVK6u9WCB3VIGESiUaNTK78srdtnTp53bSSe1s2rSCdtttgRsg3YxdfHEgiJg8Oe3fOvJIs1atAr8ZenWoU8fskkvMHn88cDOrgOW++8wqVjRr0MCsbt2DX0/l8M6da3bGGbmnnnqijo9Zs8x0ylbah9J+oJv1UCeeaHbDDYGceaXtRx8FqgMpTcuWDUyj//7iz5wZuOnu0MGsVKnUf3/9+kB7Bf/7O3YEGkivXJn2cviqVQv8do0agRvwc84JBBUKBhQE6Eb+7bcDpQhpzcN3wgmB+ei72r/SosBFDblVHepgNGxodu21gaBCx6UCLa3DkiVm9esHll+NxQ/G1q2BUkqVSMZCrK7fAEFHDKl3qokTJ7rSCTUe18E7Y8YMO+m/7IxFixZZ48aN7d/QctNMOvzww10AoYbpCjoUVIR2zxtKpSLnnHOOrVq1ysr+dyUYNmyY9e3b19avX+8GL9RzBRjz588Pfu/yyy93y/iFsnCiRNCRd+TmoEM3DCopqFIlvr+rHFfl4KrqhG6uVF1DypQJ5Loqt/VgKTe4fftAjrcKTJUbrpsi5doqoBkzJtCQNVpNmpgdf7xZjx6BHF4FNsot7t8/cAOmGy3l1qqkRL+r9xTUqFRFN3u6KZswITAv3XDqJlg3aLrB0o1qsWKB0he9Vq64bjxVN75bt8DNrG5AVZ1G66WASDeauuHWtlRutn5bwYyCMG1DfV//tctqeuVOL1wYyP3Xb918cyDQ0o2qtr+q7KjhrgIvbZ/sOj603XVDrnXxKbj8+utAeitNFAwoN1zLp5zxSy9NOyjTdnzsscB21U1vrGl7aJm0TZTuCkaUQz92bCDoUE68cuu1H99++4HfVyNp7Rtan+++y9pNv3qe1/zVFkLBuY5b7T9XXBEeFClYeumlvfbGG/ts27a0z1UKEkIDFAXzWhellR80hVIApf1JQdirrwZKEXMygg7EjEo6EBspKSne2rVrg69LlCjhLV26NPh6zZo1Xr58+bI07z179njvvPOOV6hQIW/BggXuvTPOOMM74ogjvNKlS3snnHCCd9ddd3lbt24Nfqd///5evXr1wuazbNky5WV6s2bNcq+bNWvmde/ePWyaV1991StZsmSmlm/jxo1uvvqfnXbt2uV9/PHH7j+SQ25Mk717Pe/aa3VLHHjUquV5pUp5Xt26ntexo+cNG+Z5q1Zlbp47d+q4CPyfM8fzNm0KvL9nj+d98onnzZzped26eV6HDvt/N/Rx2mmet3174Dv67pAhnjdggOfNnatj1PMefdTz2rcP/06BAp535pmeN3685911l+dddJHn6RTQr5/n7du3f9m0DPPnB/6H0u9pvpr/HXd4XtWqntepk+f9+6/nvfKK59Wpk/qyZvTQae+hhzzvvvsy971DDjnwvcKFPe+EE7K2HOk98uf3vJo1D3w/JcXzbrvN8+6/P7AOM2Zk/vjQtp8yxfMeeSQwP8338MM97+STPe/WWwP7WnrL1rCh533+uee9+GIgfdu29bxff/W8Cy8Mn65yZc/76y/P27YtsN+98ILO8Z5Xv77nXX65rkGe9/77gf1av12tmucddlhgvbU8hQp5XtmynlesWOC5P9+KFQOPaLdl8eL7nw8eHFiO22/3vI8+Ct9Wy5d73pdfBvatSy7xvAYNAsuj75Up43mXXRZY9nLlAvPRvr97d+aOQ6XDRx997O3YsctbvdrzfvzR8664wvMuvtjzPv7Y8zZvDky3YoWmPfD7ev/ZZz1v3DjPW7kyMI22759/erlGrK7fACUdMZQvXz5bu3atHan6Cqbcj0Psp59+sir/ZZvqswoVKqQ6Wnl67UTUlkPjfZQoUcLefvvt4ACDaidSuXJlN0/9jkotTj75ZBv1X6uzbt262e+//25fqpz6P9u2bbPixYu7nDhVB1P1rM6dO1s/ZSX+R5+pHYimTauL3507d7pHaE5JxYoVbcOGDdlevWr8+PHWunXrXJernlPltjTRLU7//vnsscfSr99zxBGevf/+Xjv11EAdJB3Gyq1Vjufy5coVT7ExY/K53OZffklxr6VQIc927YquA/58+Txr186zk0/2rHv3fS4HPiPr1++2xx5bZO3a1bIWLWJfR0nVcSZPTrEVK1Lstdfy2ezZgXU76ijvv4au4etauLBnO3eGv3fccZ4dc4zntuE99+xz8xs3LsVmzEix3btT7PjjPZfzvGHD/u81aKD5KPc/fF7Finmu6olKcFRSoGXQdI0bezZpUkrYPCIVL+65HHul2ZIl4dMVKBBYPs878PsdO+6zxx/f60pMVDdfdCioXYF/fFSr1saGDi1k69enuJKM775Lv25M2bKem8cff+z/vVat9tm0aRpcNuP959RT99mTT+51Vd+yQqUvOhZCT/kqSVBJRPXqgVz/557L59oVKA30UA6/jofDDvPsqqv2uX2iT5/8tnx5YHkvvHCfvf323kxVnVNJlUqxVILlVyfScmW1g4Pcdr6KBV2/VVOD6lXIbgQdMQ46dCOvBtl+mww1HtdNvugmXVWWMhN07Nq1yzUG18nggw8+sJdfftkmT57sBh6MpKpdLVu2tF9//dWqVasW06Dj/vvvtwEDBhzwvoIitSsBko2qSyxcWNrmzz/Cli8vZSVL7rTjjvvHfvnlMBs//hg3zW23zbYqVf61ZcsOtW+/PcqqVNnobnwmTz7a/vqrqBUosM8aNVpjf/5ZwlasOPjgulChPda0aaDhxhlnrLSaNf+2IkUy0bI2wXQqmzy5oh1zzCarWnWjuzncvLmgu2FfurSU/f57KWvd+nf79NOqNnJkDdu7N5+dc85S69p1f3XOyDTaurWgHXLIbtu1K5/NmlXW8uXbZ8cf/4+VKrXLzX/58pL2xx+H2Lp1xeyss36zEiXSrlP077+FbNKkirZ5cyE7+eQ1tm1bAfvnnyJ21FFbrHz5rVa8+C53Q+zP95tvjnafX3zxEitTZqsVKrTPJk6sZD/+WNZWrSpuO3YUsLVri4cFifv2RX83fNRRm912atBgnRUrttvWrCluM2eWtcqVN9kll/zi1nHTpoL29ts1rVChvda58wJbuPBwe+yxRrZxY5ED5qflv/baBXbGGX+4ZU0GSrcFC0q7Y6V27b/oDS0H0LVegxgTdCC7EXTEkG7eozF8+PAs/0arVq1cQPGCus6IsHXrVlcaosBGjcDvvfde+/TTT10vWj41HK9atarNmjXLGjRo4LrwVc9Voe1CtHxqTK4TUFoo6ci7ckLOoW5eP/ssxRYvTrGTTvJsypQUe+WVfLZqVdo3iMq5VulCatSuoEuX/DZqVPQtOZXze955gdKKJk08u+aa/Pb11yn2+ut7rXVrz9VDV/1z1d/P7ekh6lVJJUBnneXFrEFsPEydmmI33JDf7VsZUSnXaad5rrG+9oGbb85aYOC31VCpg/btww4LdIGsAeWQO46PRKKkA7HCOB0xdDDBRLT27dsXVq0plB9clFe/fK6hZxN78MEHbd26da67XNHJV9Wf/JISTaOSjVCaRu+nR6U5folOKJ3UY3Fij9V8kbPTRI1o1dBTDY79G1lVz1Dj0qFDD5xehY7q4Ue996jqiHqC0uGkPv8vv1x1QFKvB6KBut5/3+z//i/Qm5MaGStOV89LGiFY3dCqeooOs/031Hqy/+5aDbV1w5gvX+A0nN2dxCRDeqRHDcn1yOnUOF0NlNVwXlWzFABMnx5okC6VK++1lSt/t8GDK1rVqgVdV+b7Za36m5+soYXI+l3knuMjkdguiBWCjhxEVZ5UBapSpUq2efNmV3VJY2qoutTSpUuD7Ts0EKHadPTs2dOVXKj3LGnTpo0LLjp27GiPPfaYrVmzxu655x675ZZbggGDusodMmSI9enTx6677jpXReu9995zPVoByUhVYb791uyOOwJBg6gnoAcfDPQ6deWVGp8m/Dv16gWmVy9AqcTKUVEwoYHH9PDrmKsnn8zOAzmfgtf/mtY5F1wQeMju3fts7Nh5VqlSRaoWAcjTCDpyEJVQaFwNja+hPrQVTCjgUDGxRjr/6quvXLUoVatSI+4OHTq4oMKnMT40VshNN93kSi7UluOaa64JG9dDjdwVYChgGTx4sB199NGu3UhmxugAYk01/VTKoK5W1bVsJAUZHTvuf60Gvsp5Vo1HNUhWrnB23vAzajcAAOkj6MhBXnnllTQ/U5ChBuUZUe9WkdWnImm8Dw04CCSyQbLGJFB1pQ8+CNRX10NVl/TZiBEHfkcD5Q0bFqha9eSTgZGCVbXq/PPNnnpq/1gb6oMfAADEF0EHgKSgthhdu5ppXEqVHPz554HTqGQjVNOmgVKLTp0CVZv8EgcNSHfjjYHuXNXVJgAASCyCDgAJoRILtcFQaYTGM1Dja41o7FOf/woa5OKLA1Wk1FBXI4TruapPabTmtKiHoP+GyAEAAAlG0AEgbjRg2yOPBNpXqG1FJDXqVomFmhBdeGGggS4AAMj5CDoAZKsNGwLdzn74YWA04+OO02jQgd6kBg1K+3s1aph98UWgNAMAAOQuBB0AssWuXerWOdBoW+NPhJ1oCgSqUckRR5jdeqvZ9dcH2mNooL3lywNVpeg2HwCA3ImgA0C2uOOOfPbCC+HvadA9NQhfuzbw+v77zdSLc/6QMdFUGqJABAAA5F4EHQAOyvTpKXb33afaggWBSGLkSDMNcK/2GapatXu32TffBEbnrlOHjQ0AQF5E0AEgSzQK9223mQ0dmt88L1BU0bKl2WWXhU+nKlN6HwAA5F0EHQAybc6cwNgY8+bpVYodccQ2a9OmiP3vf9k4zDcAAMg1CDoARG3HDrOHHzZ7/PHAeBnSqdM+u+ii8Xb22WdbwYIEHQAA4EDcIQCIypdfBrqzHTgwEHA0amT2wQdmQ4bsZQsCAIB0UdIBIF2jRplNmmSuZyp1i6vepl59NdB2IyUl0FAcAAAgPQQdAFK1d6/Z+PFmHTrsf69hQ7NPPzUrX56NBgAAokf1KgAH0AB/xYqZtWu3/70rrjAbPZqAAwAAZB4lHQBswwazhx4KDOSnxuIqzfA1aWI2caJZkSJsKAAAkDUEHUAeH2tD7TO6dj3ws6uvNrvmGrPTTzcrVCgRSwcAAHILgg4gD+vf3+zBBwPPS5Uy69XLbNOmQM9Ul14aaCgOAABwsAg6gDxGPVBpcL8XXzR75ZXAe4cdZvbJJ2bNmiV66QAAQG5E0AHkIUuWmLVta7Z8+f73brvN7OmnzfLRrQQAAIgRbjOAPNBu4/PPza680qxFi/0BR61aZh9/bPbMMwQcAAAgtijpAHIpjRo+eLDZgAGBHql8arsxb55ZxYqJXDoAAJCXEHQAudQtt5gNH77/ddOmZo0bm91wAwEHAACIL4IOIJdZtsxs4ECz114LvC5Z0mzoULOrrkr0kgEAgLyKoAPIBbZuDfREtXRpYNyNLVsC7yv4ULe4AAAAiUTQAeRgP/xgdu+9ZuPGhb/fsKHZI4+YtWyZqCUDAADYj6ADyIG9UU2ebHb//YH/voIFzW6+2axqVbMuXcyKF0/kUgIAAOxH0AHkMCrZeOCB/a9PO83slFPM+vQxK1MmkUsGAACQOoIOIIdQt7fnnGM2YULgdZEiZqNHm7VqleglAwAASB+DAwI5hKpT+QFH375m27YRcAAAgJyBkg4gyf39t9lzz5k9+mjg9Ycfml10UaKXCgAAIHoEHUAS27DBrH59sz//DLzu2ZOAAwAA5DxUrwKS1Lp1ZtdfHwg4ihUzGzzY7IknEr1UAAAAmUdJB5Bkli83e+sts4ceMtu+PfDep58y5gYAAMi5CDqAJPLTT2ZNmgQaiUvZsmZ33knAAQAAcjaCDiBJ/PVXoL2GAo4aNQI9VHXsaJY/f6KXDAAA4OAQdABJYOtWs6ZNzZYuNatUyWzKFLPSpRO9VAAAANmDhuRAEnjpJbNffjErVcrsk08IOAAAQO5C0AEk2KhRga5w5f/+L9BFLgAAQG5C0AEkiOeZPfyw2SWXBF43aGDWqRPJAQAAch+CDiBBg/5ddpnZ//5ntm+f2bXXmn33nVmhQiQHAADIfWhIDsTZ7t1m555rNm2aWUqK2dChZjfcEHgOAACQGxF0AHF2zz2BgKN4cbMPPjA76yySAAAA5G5UrwLi6LXXzB57LPB8xAgCDgAAkDcQdABx8vHHZp07B55r4L+LL2bTAwCAvIGgA4hTT1X33x/437Wr2UMPsdkBAEDeQdABxNjy5YFucefONSta1OzRR83yceQBAIA8hIbkQAzNn292yilm27YFXj/9tNnhh7PJAQBA3kJ+KxAju3aZXX55IOCoXNnshRfMunVjcwMAgLyHkg4gRhRkLFhgduSRZtOnm5Upw6YGAAB5EyUdQAyoO9zu3QPP77uPgAMAAORtBB1ANvr0U7Pzzw90jaueqtSA/Kab2MQAACBvo3oVkI3jcFx44f7X7dqZvfuuWUoKmxgAAORtBB1ANtiwwaxTp8DzZs3MWrc2u/VWAg4AAACCDiCbvPyy2ebNZvXqmU2YYFawIJsWAADAR5sO4CDNmWPWr1/gec+eBBwAAACRCDqAg7BokdnZZweeV6lidtllbE4AAIBIBB1AFm3aZHbBBWarV5sde6zZN9+YFSnC5gQAAIhEQ3IgC/buDYw2vnix2VFHmU2ZwlgcAAAAaaGkIwd5/vnnrW7dulayZEn3aNKkiX3++efBz3fs2GG33HKLlS5d2kqUKGEdOnSwtWvXhs1jxYoV1r59eytWrJiVKVPGevfubXv27AmbZtKkSXbiiSda4cKFrXr16jZCI90h6J9/zE46yUybXiUb6iqX0cYBAADSRtCRgxx99NH2yCOP2MyZM+3HH3+0Fi1a2Pnnn28LFixwn/fs2dNGjx5t77//vk2ePNlWrVplF110UfD7e/fudQHHrl27bOrUqfbaa6+5gOLee+8NTrN8+XI3zZlnnmlz5syxHj16WNeuXe3LL79MyDonGw34d+21ZnPnmuXPb/bmm2YNGyZ6qQAAAJIb1atykHPPPTfs9YMPPuhKP6ZNm+YCkldeecXefvttF4zI8OHDrWbNmu7zxo0b27hx42zhwoX21VdfWdmyZa1+/fo2aNAg69u3r91///1WqFAhGzZsmFWpUsWeeOIJNw99f8qUKfbUU09Z27ZtLa/7v/8LjDpeuLDZt9+aNWqU6CUCAABIfpR05FAqtRg5cqRt3brVVbNS6cfu3butVatWwWlq1KhhlSpVsu+//9691v86deq4gMOnQGLTpk3B0hJNEzoPfxp/HnnZc8+Z9e0beD54MAEHAABAtCjpyGHmzZvnggy131C7jY8++shq1arlqkKppOLQQw8Nm14Bxpo1a9xz/Q8NOPzP/c/Sm0aByfbt261o0aKpLtfOnTvdw6fpRYGQHtnFn1d2zjMar7ySYrfeGjhcbr55r3XuvM/ivAhJK1FpgtSRHsmF9EgupEf02wjIbgQdOczxxx/vAoyNGzfaBx98YNdcc41rv5FoDz/8sA0YMOCA91WlS43Ws9v48eMtXtatK2o9ewaqrHXo8Iu1bv2za0SOxKUJMkZ6JBfSI7mQHmnbtm1bHFMCeQlBRw6j0gz1KCUnnXSSzZgxwwYPHmyXXXaZayD+77//hpV2qPeqcuXKuef6P3369LD5+b1bhU4T2eOVXqu3rLRKOaRfv37Wq1evsJKOihUrWps2bdx3szMHRheL1q1bW8GCBS0eDccvuCC/7diRz047bZ+99VYVy5evSsx/NyeJd5qA9MhJOD6SC+mRMb+mApDdCDpyuH379rlqTQpAdMM3YcIE11WuLF682HWRq+pYov9qfL5u3TrXXa7oZlFBgapo+dOMHTs27Dc0jT+PtKh7XT0iaZlicSMaq/mm1nBcpRqFCpm99FI+K1yYZlCJThNEh/RILqRHciE90t82QCwQdOQgKk1o166daxy+efNm11OVxtRQd7alSpWyLl26uNKGww8/3AUSt912mwsW1HOVqNRBwUXHjh3tsccec+037rnnHje2hx8w3HjjjTZkyBDr06ePXXfddTZx4kR777337LPPPrO8RiONP/JI4Lk686pRI9FLBAAAkDMRdOQgKqHo1KmTrV692gUZGihQAYeqtYi6tc2XL58r6VDph3qdGjp0aPD7+fPntzFjxthNN93kgpHixYu7NiEDBw4MTqPuchVgaMwPVdtSV7wvv/xynusu9+efzc44w+yvv8yOO07BWKKXCAAAIOci6MhBNA5HeooUKWLPPfece6SlcuXKB1SfitS8eXObPXu25WU33WS2fr3GKQmMOF6AIwUAACDLuJUCIqgzMD3UjkMDsVesyCYCAAA4GLSKBSL4tc26dCHgAAAAyA4EHUCId94xmzhRvXeY3XUXmwYAACA7EHQA/1m+fH+D8X79zCpVYtMAAABkB4IOwMz27jW78koNimR26qlm/fuzWQAAALILQQdgZq+/bjZtmpkGT3/rLXqrAgAAyE4EHcjzNCbH7bcHNsPdd6tb4Ty/SQAAALIVQQfyvL59zbZs0fgkZj175vnNAQAAkO0IOpCnLVli9vnngecavF29VgEAACB7EXQgz/r3X7MWLcz27DFr1Sow+jgAAACyH0EH8qz/+z+zP/4IdI372muJXhoAAIDci6ADedKiRWYPPRR4/sgjZhUqJHqJAAAAci+CDuRJzz1n5nlm7dqZXX55opcGAAAgdyPoQJ6jKlUvvRR43quXWUpKopcIAAAgdyPoQJ7zwANmO3eanX66WcuWiV4aAACA3I+gA3nKsmVmr7yyP/iglAMAACD2CDqQpwwYEOgit21bs2bNEr00AAAAeQNBB/KMn382e/PNwPNBgxK9NAAAAHkHQQfyjEcfNdu3z+yCC8waNUr00gAAAOQdBB3IEzZuNPvgg8DzPn0SvTQAAAB5C0EH8oSHHzbbutWsZk2zxo0TvTQAAAB5C0EHcr1t28xefHF/8EGPVQAAAPFF0IFc7/HHzf75x6xKFbNzzkn00gAAAOQ9BB3I1TQI4NChgecPPWSWP3+ilwgAACDvIehArvbMM2br1plVqGDWoUOilwYAACBvIuhArrVrl9lTT+0ffbxgwUQvEQAAQN5E0IFc68MPzVavNitXzuyqqxK9NAAAAHkXQQdyrWefDfy/8UazQoUSvTQAAAB5F0EHcqWZM82+/z5QpeqGGxK9NAAAAHkbQQdypSefDPy/9NJA9SoAAAAkDkEHch2VcLzzTuB5r16JXhoAAAAQdCBX8Tyz224L/O/Y0ezEExO9RAAAACDoQK7y5ZeB9hzFipk98USilwYAAABC0IFcZciQwH81Hj/yyEQvDQAAAISgA7nG5s1m48cHnnftmuilAQAAgI+gA7nGF18ERiE/9lizmjUTvTQAAADwEXQg1/j008D/Cy80S0lJ9NIAAADAVyD4DMjhpkwJ/G/VKtFLAgDJx/M827Nnj+3du9fyqt27d1uBAgVsx44deXY75M+f322DFHLnEGcEHcgVfvst8MiXz+yUUxK9NACQXHbt2mWrV6+2bdu2WV4PvMqVK2crV67M0zfdxYoVs/Lly1uhQoUSvSjIQwg6kCs8+mjg/xlnmJUsmeilAYDksW/fPlu+fLnL4a5QoYK70cyrN9zaFlu2bLESJUpYPuVS5cGgSwHo+vXr3T5x7LHH5sntgMQg6ECO98cfZi+/HHg+YECilwYAkotuMnWzXbFiRZfDnZdpO2h7FClSJM/ebBctWtQKFixov//+e3BbAPGQN4845CpvvWW2Z4/ZaaeZNWuW6KUBgOSUV2+ycSD2BSQCZyDkaJ5n9tprgefXXJPopQEAAEBqCDqQoz35pNnPP5sVLmx28cWJXhoAQF5z7bXX2gUXXJDoxQCSHkEHciwNBOg3IL/3XrNDD030EgEAsvNmXg3e/Ufp0qXtrLPOsp9++inbfuP++++3+vXrRzVd6LL4j6+++soGDx5sI0aMCE7bvHlz69GjR7YtI5BbEHQgx3r1VbP1680qVDDr0yfRSwMAyG4KMtTVrx4TJkxw40ucc845CdnQJ5xwQnBZ/Mfpp59upUqVskPJ9QIyRNCBHNuW49lnA8/79jUrQD9sAJDrFC5c2I2roYdKJO666y43xoa6fPXp9aWXXupu/A8//HA7//zz7TcN3PSfSZMm2cknn2zFixd3n7dt29b13KTSiQEDBtjcuXODJRehJRaRFPD4y+I/1P1waPUqPZ88ebIr/fDnqWXRMui5AqeGDRu6XsSaNm1qixcvDvuNTz75xE488UTXo1TVqlXd8mlAR7+7W5W4VKpUyW0XdX98++23B787dOhQ1wWuvlu2bFm7mDrHSDLcqiFH+vHHFFu4UF3/6SSf6KUBgJyVaZOoMQLVY29WhwjR+BpvvvmmVa9e3VW18kcYVxDRpEkT+/bbb11g8MADDwSrYamXJgUE119/vb3zzjtuJPJvvvnGBQCXXXaZzZ8/37744gtXTUpUanEwFGz88ssvVrt2bRs4cKB778gjjwwGQXfffbc98cQT7r0bb7zRrrvuOvvuu+/cZ1r+Tp062TPPPGPNmjWzpUuXWrdu3dxn9913n3344Yf21FNP2ciRI12py5o1a1zAJD/++KMLQN544w0XzPz9999ufkAyIehAjvTaa4GrVocODAYIAJmhgKNEicRssy1bzIoXj376MWPGuIH8ZOvWrW4Ubb3nd/n67rvvurE3Xn755eCAh8OHD3elHipdUKnCxo0bXZWsatWquWmPOuooK1mypJuH5u2XYGRk3rx5wWWRWrVq2fTp08OmUdCi0g+VZKQ2zwcffNDO0Ci2Zq7Upn379i4QUumESjX03jX/dcWoko5BgwZZnz59XNCxYsUKN89WrVq5cTZU4qESHNFnKsnReh5yyCFWuXJla9CgQfQbGogDqlchx9m5M5+9+25g16WUAwByrzPPPNPmzJnjHrrBV6lGu3btXPUoUU7/r7/+6m60FRDooSpUupFXSYGeq8qTvnfuuee6UgSVEGTF8ccfH1wWPVTykFl169YNPlcAJevWrQuui0pH/PXQQyU0ajuybds2u+SSS2z79u0uGNH7H330UbDqVevWrV2goc86duxob731lvsOkEwo6UCOM316edu4McUqVdIFKdFLAwA5i6o4qcQhUb+dGcq9V3Uqn0o0VJrw0ksvuWpUqnJ10kknuZvsSKrC5Jd8qOqRqlG999571r9/f/vyyy9dNaTMUAlG6LJkhUoofH7JjEpfROui0o6LLrrogO+pJEQjyqsNiKqCjR8/3m6++WZ7/PHHXRsSBV2zZs1ypTvjxo2ze++917X/mDFjBo3ckTQIOpCjKEPoww+Pdc9VAs0AuwCQObrXzUwVp2SiG3VVi1KOv6jRtapYlSlTxlWZSouqGunRt29fa9y4sWvfoaBDgcTevXuzdRmzOk+ti4KK9AKbokWLuhIbPW655RarUaOGq/al76qamKpe6aHqWKpiNnHixFSDGCARCDqQo3Trlt9++y3Q0I+qVQCQu+3cuTNYHeqff/6xIUOGuBIB3XTLVVdd5XL71WOVqiYdffTRrurVqFGjXFsINTR/8cUX7bzzznO9Pf3888+u2pXfbuKYY46x5cuXu+pS+q5KDNQz1MHQPH/44QfXeNyv7hUNlU6oTYbaaqjnKQVXqnKlxu4q1VHPWgpmTjnlFNdmRI3qFYSoWpXauSxbtsx14XvYYYfZ2LFjXQmKqoQByYI2HchRVDRfqNBe69Jln1WtmuilAQDEkqpEqe2DHrrZVnWh999/3w3AJ7r5Vm9UulFXjn7NmjWtS5curk2HSj70+aJFi6xDhw523HHHuR6junbtajfccIP7vt5XT1dqO6LqWCoBOVh33nmn5c+f3zU01zzVyDsaanei4EHVoxo1auRKZNRblYIKUcmFqpWdeuqprm2IqlmNHj3a9eSlzxRotWjRwm2DYcOGuXVRL1dAskjx1PEzkM02bdrk6t2q15D0irwzS7lWysE5++yzw+rGInFIk+RCeiSXZEgP3YArN79KlSqubUBeptx/XZ/83qvyqvT2iVhdv4G8e8QBAAAAiAuCDgAAAAAxRdABAAAAIKYIOgAAAADEFEFHDvLwww+7Hi3UpZ/6JL/gggtcn96h1KOH+jEPfai3jlDqSaN9+/auVw/Np3fv3sFRTX0aYEj9fqvrQPUZrq76AAAAgKwg6MhBNOqoBgOaNm2aG41UvaK0adPGtm7dGjbd9ddfb6tXrw4+HnvsseBn6uNbAceuXbts6tSp9tprr7mAQv2D+9SjhaZRF4Lqu7xHjx6ui0GN4AoAyJnorBLsC0gkBgfMYf2Vh1KwoJKKmTNnugGBfCrBKFeuXKrzUP/fCxcudP17ly1b1urXr2+DBg1yo7Tef//9biRV9e+tbvSeeOIJ9x31+T1lyhTXX7j6EQcA5Bx+V73btm1zg8kB2hdC9w0gHgg6cjD1oS2Ro52+9dZbbqRSBR4atbV///4uEJHvv//e6tSp4wIOnwKJm266yRYsWGANGjRw07Rq1SpsnppGJR7pjRqrR2g/36LSGD2yiz+v7JwnDg5pklxIj+SSLOmharlr165141ToeqCqt3m1tEcl/du3b8+T20Drr4Bj/fr1bgwO7Q96hEr0vorci6Ajh9JJQkGARiatXbt28P0rr7zSjV5aoUIF++mnn1wJhtp9aKRSWbNmTVjAIf5rfZbeNAokdKJOLadM7U0GDBiQasmKH/BkJ1UvQ3IhTZIL6ZFckiE9FHioOm5eHhQPgfuHzZs325IlS9ItBQGyG0FHDqW2HfPnz3fVnkJ169Yt+FwlGuXLl7eWLVva0qVLrVq1ajFbnn79+lmvXr2CrxWgVKxY0bU5ye4RyXXxbt26NcXCSYI0SS6kR3JJtvRQuz51HJJX23do3dWesWnTplagQN67BVLpjtY7f/78aU7j11QAslveO+JygVtvvdXGjBlj33zzjR199NHpTnvKKae4/7/++qsLOlTlavr06WHTqMhd/HYg+u+/FzqNgoe06gOrlys9IukiG4sLbazmi6wjTZIL6ZFckiU9kmEZEh0EKvAoUaJEnt8Wacnr+whihzLWHEQ5Uwo4PvroI5s4caJr7J0R9T4lKvGQJk2a2Lx582zdunXBaZQLp4CiVq1awWkmTJgQNh9No/cBAACAzCLoyGFVqtRA/O2333Z1c9X2Qg+1sxBVoVJPVOrN6rfffrNPP/3UOnXq5Hq2qlu3rptG1Z0UXHTs2NHmzp3rusG955573Lz9kgqN67Fs2TLr06ePLVq0yIYOHWrvvfee9ezZM6HrDwAAgJyJoCMHef75512PVRoAUCUX/uPdd991n6u7W3WFq8CiRo0adscdd1iHDh1s9OjRwXmoHqeqZum/Si6uvvpqF5gMHDgwOI1KUD777DNXulGvXj3Xde7LL79Md7kAAADIEtp05CAZNfxTw20NIJgR9W41duzYdKdRYDN79mw72GXN7gZpqo+rnjU0X+qdJgfSJLmQHsmF9EgupEfG/Ot2Xu1sALFD0IGYUHd8fiAEAABy3nW8VKlSiV4M5CIpHqEsYtQP+KpVq1zbk+wcgMnvinflypXZ2hUvso40SS6kR3IhPZIL6ZEx3RYq4NB4X4zpguxESQdiQieqjLrzPRgKOAg6kgtpklxIj+RCeiQX0iN9lHAgFmhIDgAAACCmCDoAAAAAxBRBB3IUjSVy3333pTr6ORKDNEkupEdyIT2SC+kBJA4NyQEAAADEFCUdAAAAAGKKoAMAAAAAQQcAAACAnIuSDgAAAAAxRdCBHOW5556zY445xooUKWKnnHKKTZ8+PdGLlOs8/PDD1qhRIzeafJkyZeyCCy6wxYsXh02zY8cOu+WWW6x06dJWokQJ69Chg61duzZsmhUrVlj79u2tWLFibj69e/e2PXv2xHltcp9HHnnEUlJSrEePHsH3SI/4+/PPP+3qq692x0DRokWtTp069uOPP4aN6nzvvfda+fLl3eetWrWyJUuWhM3j77//tquuusoNVHfooYdaly5dbMuWLQlYm5xt79691r9/f6tSpYrb1tWqVbNBgwa5NPCRHkAS8IAcYuTIkV6hQoW8V1991VuwYIF3/fXXe4ceeqi3du3aRC9artK2bVtv+PDh3vz58705c+Z4Z599tlepUiVvy5YtwWluvPFGr2LFit6ECRO8H3/80WvcuLHXtGnT4Od79uzxateu7bVq1cqbPXu2N3bsWO+II47w+vXrl6C1yh2mT5/uHXPMMV7dunW97t27B98nPeLr77//9ipXruxde+213g8//OAtW7bM+/LLL71ff/01OM0jjzzilSpVyvv444+9uXPneuedd55XpUoVb/v27cFpzjrrLK9evXretGnTvG+//darXr26d8UVV8R5bXK+Bx980CtdurQ3ZswYb/ny5d7777/vlShRwhs8eHBwGtIDSDyCDuQYJ598snfLLbcEX+/du9erUKGC9/DDDyd0uXK7devWKbvQmzx5snv977//egULFnQXdt/PP//spvn+++/dawUZ+fLl89asWROc5vnnn/dKlizp7dy5MwFrkfNt3rzZO/bYY73x48d7Z5xxRjDoID3ir2/fvt5pp52W5uf79u3zypUr5z3++OPB95ROhQsX9t555x33euHChe6YmTFjRnCazz//3EtJSfH+/PPPGK9B7tK+fXvvuuuuC3vvoosu8q666ir3nPQAkgPVq5Aj7Nq1y2bOnOmqKPjy5cvnXn///fcJXbbcbuPGje7/4Ycf7v4rHXbv3h2WFjVq1LBKlSoF00L/Vd2kbNmywWnatm1rmzZtsgULFsR9HXIDVWdTdbXQ7S6kR/x9+umn1rBhQ7vkkktc1cEGDRrYSy+9FPx8+fLltmbNmrC0KlWqlKsSGnqMqEqV5uPT9Dqv/fDDD3Feo5ytadOmNmHCBPvll1/c67lz59qUKVOsXbt27jXpASSHAoleACAaGzZscPV2Q29iRa8XLVrERoyRffv2ubYDp556qtWuXdu9p5upQoUKuRumyLTQZ/40qaWV/xkyZ+TIkTZr1iybMWPGAZ+RHvG3bNkye/75561Xr172v//9z6XL7bff7o6La665JriPp3YMhB4jClhCFShQwAX3HCOZc9ddd7kMDWV+5M+f310rHnzwQddext/WpAeQeAQdANLNXZ8/f77LNURirFy50rp3727jx493HSggOYJxlVA89NBD7rVKOnScDBs2zAUdiK/33nvP3nrrLXv77bfthBNOsDlz5rjMkgoVKpAeQBKhehVyhCOOOMLlYEX2kKTX5cqVS9hy5Wa33nqrjRkzxr7++ms7+uijg+9re6u627///ptmWuh/amnlf4boqfrUunXr7MQTT3Q54XpMnjzZnnnmGfdcueekR3ypR6patWqFvVezZk3XY1voPp7e+Ur/la6h1LuberTiGMkc9Yyn0o7LL7/cVevs2LGj9ezZ0/XER3oAyYOgAzmCqi2cdNJJrt5uaG6jXjdp0iShy5bbqIMJBRwfffSRTZw40XVDGUrpULBgwbC0UJe6uuHy00L/582bF3ZTpZx6dQ0aebOG9LVs2dJtS+Xe+g/lsqvqiP+c9IgvVTeM7EZa7QkqV67snuuYUeAQeoyo+o/aaoQeIwrcFVT6dLzpvKa2H4jetm3bXFuYUMqk0rYkPYAkkuiW7EC01GWuen8ZMWKE6/mlW7dursvc0B6ScPBuuukm19XnpEmTvNWrVwcf27ZtC+uiVd3oTpw40XWZ26RJE/eI7DK3TZs2rtvdL774wjvyyCPpMjebhPZeRXokpuviAgUKuK5alyxZ4r311ltesWLFvDfffDOsi1adnz755BPvp59+8s4///xUu8xt0KCB63Z3ypQprncyuszNvGuuucY76qijgl3mjho1ynXR3adPH9IDSCIEHchRnn32WXezq/E61IWu+rdH9lJeRGoPjd3h043TzTff7B122GHuZuvCCy90gUmo3377zWvXrp1XtGhRdwNwxx13eLt37ya5YhB0kB7xN3r0aBdYKyOkRo0a3osvvhj2ubpp7d+/v1e2bFk3TcuWLb3FixeHTfPXX3+5IENjSqg76c6dO7uukZE5mzZtcseDrg1FihTxqlat6t19991h3XOTHkDipehPoktbAAAAAORetOkAAAAAEFMEHQAAAABiiqADAAAAQEwRdAAAAACIKYIOAAAAADFF0AEAAAAgpgg6AAAAAMQUQQcAIGFGjBhhhx56KCkAALkcQQcA5ABr1qyx7t27W/Xq1a1IkSJWtmxZO/XUU+3555+3bdu2WU5wzDHH2NNPPx323mWXXWa//PJLwpYJABAfBeL0OwCALFq2bJkLMFQi8NBDD1mdOnWscOHCNm/ePHvxxRftqKOOsvPOOy8h29fzPNu7d68VKJC1y0nRokXdAwCQu1HSAQBJ7uabb3Y39T/++KNdeumlVrNmTatataqdf/759tlnn9m5557rpvv333+ta9euduSRR1rJkiWtRYsWNnfu3OB87r//fqtfv7698cYbrtShVKlSdvnll9vmzZuD0+zbt88efvhhq1KligsG6tWrZx988EHw80mTJllKSop9/vnndtJJJ7ngZ8qUKbZ06VK3PCqBKVGihDVq1Mi++uqr4PeaN29uv//+u/Xs2dN9X4+0qlep9KZatWpWqFAhO/74493yhtJ3X375ZbvwwgutWLFiduyxx9qnn34agy0PAMguBB0AkMT++usvGzdunN1yyy1WvHjxVKfxb+AvueQSW7dunQsIZs6caSeeeKK1bNnS/v777+C0Cg4+/vhjGzNmjHtMnjzZHnnkkeDnCjhef/11GzZsmC1YsMAFCVdffbWbLtRdd93lvvfzzz9b3bp1bcuWLXb22WfbhAkTbPbs2XbWWWe5YGjFihVu+lGjRtnRRx9tAwcOtNWrV7tHaj766CNXjeyOO+6w+fPn2w033GCdO3e2r7/+Omy6AQMGuADsp59+cr971VVXha0nACDJeACApDVt2jRPp+pRo0aFvV+6dGmvePHi7tGnTx/v22+/9UqWLOnt2LEjbLpq1ap5L7zwgnt+3333ecWKFfM2bdoU/Lx3797eKaec4p7ru/p86tSpYfPo0qWLd8UVV7jnX3/9tVuejz/+OMNlP+GEE7xnn302+Lpy5creU089FTbN8OHDvVKlSgVfN23a1Lv++uvDprnkkku8s88+O/hav3/PPfcEX2/ZssW99/nnn2e4TACAxKBNBwDkQNOnT3dVoZTDv3PnTleNSqUNpUuXDptu+/btrnTDp2pVhxxySPB1+fLlXemI/Prrr65ReuvWrcPmsWvXLmvQoEHYew0bNgx7rd9W9S1V91Ipxp49e9xv+yUd0VLJSbdu3cLeU3uWwYMHh72n0hWfSoBUncxfDwBA8iHoAIAkpt6qVH1q8eLFYe+rTYf4jbB1068AQm0uIoW2mShYsGDYZ5q3ghd/HqLAQY3TQ6ntRqjIql533nmnjR8/3v7v//7PLbOW6+KLL3YBSyyktx4AgORD0AEASUwlFyp5GDJkiN12221ptutQ+w11q6sG5yrNyIpatWq54EKlE2eccUamvvvdd9/Ztdde6xp3+wHMb7/9FjaNGoarp6v0qJG85nXNNdeEzVvLBgDIuQg6ACDJDR061FUxUpUmVWFS1aJ8+fLZjBkzbNGiRa4XqVatWlmTJk3sggsusMcee8yOO+44W7VqlSu1UCAQWR0qNap2pRILNR5XqcFpp51mGzdudDf9qr4UGghEUg9SaiyuxuMqdejfv/8BJQ8Khr755hvXY5aCmyOOOOKA+fTu3ds1EFd1Lq3T6NGj3XxDe8ICAOQ8BB0AkOTUfax6hNIYHf369bM//vjD3bQr919BgrrU1Y3+2LFj7e6773a9Pa1fv97KlStnp59+uuvGNlqDBg1yXe6qFyuND6KqWSpF+d///pfu95588km77rrrrGnTpi6Y6Nu3r23atClsGvVcpd6otD5qhxJoEx5OQZPab6ialnqxUte9w4cPd13uAgByrhS1Jk/0QgAAAADIvRinAwAAAEBMEXQAAAAAiCmCDgAAAAAxRdABAAAAIKYIOgAAAADEFEEHAAAAgJgi6AAAAAAQUwQdAAAAAGKKoAMAAABATBF0AAAAAIgpgg4AAAAAMUXQAQAAAMBi6f8BmppxQZrEv2MAAAAASUVORK5CYII=",
      "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: 0.995\n",
      "Length 500: 0.99\n",
      "Length 1000: 0.898\n",
      "Length 2000: 0.7985\n",
      "Length 5000: 0.708\n",
      "Length 20000: 0.6012\n",
      "Length 50000: 0.5626\n",
      "Length 100000: 0.54478\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 = 8.2260 * x^-0.4468\n",
      "Exponential model: y = 0.3213 * exp(-0.0000 * x)\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: 0:11:57.360666\n"
     ]
    }
   ],
   "source": [
    "# Program comparingPlateaus.py\n",
    "# Evolution 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 oneEvolutionaryProcessk2( 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 Evolutionary 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 = oneEvolutionaryProcessk2( 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",
    "*Our Observations*\n",
    "\n",
    "Without recombination: \n",
    "> 1. The height of the plateau, $y$, is fitted by a power model that predicts \n",
    "   $ y = 8.2 * x ^{-0.4466} + 0.5$, where $x$ is the length of the target sequence. There is small variation from experiment to experiment.\n",
    "> 2. The limit of the proportion of solved sites, $y $, when the length of the target string, $x$, grows is $0.5$ because \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",
    " >>> Or\n",
    "\n",
    " >>> $ y \\rightarrow 0.5$\n",
    "\n",
    " With recombination rate = 0.01: \n",
    "> 1. The height of the plateau, $y$, is fitted by a power model that predicts \n",
    "   $ y = 8.2665 * x^{-0.44585} + 0.5$, where $x$ is the length of the target sequence.  \n",
    "> 2. The limit of the proportion of solved sites, $y $, when the length of the target string, $x$, grows is $0.5$.\n",
    "\n",
    " *Our interpretation.* \n",
    "\n",
    " 1. Evolution is moved by randomness. So, it is astonishing that the height of the plateau is accurately fitted by a simple mathematical model, a power law. \n",
    " 2. Resources, here population size, can be paired with complexity as follows:\n",
    "  >>> Resources could be sufficient to perfectly complete a simple task, here with a length of the target string equal to 400 or less.     \n",
    "  >>> As the complexity of the task grows, Evolution falls short of power and the maximum achievable proportion of solved sites diminishes according to a power law.    \n",
    "  >>> Very large complexity can kill Evolution, and its effect could be indistinguishable from that of randomness, whose signature is mediocrity, $0.5$. Half of the sites are solved by mere randomness. \n",
    "3. Fluctuations always operate. They are clearly visible when the complexity is low but diminish as the complexity grows. Thus, perfection always can be achieved for whatever target string, but maybe beyond a lapse of time longer than the age of the Universe.\n",
    "4. Present results were obtained with a population size of 30. This are the available resource. If the length of the target string is 5000 bits, the proportion of solved sites tends to 0.7. This means a deficit of 30\\%. This fact shows that Evolution always leaves an unerasable track of clearly imperfect products. \n",
    "5. The plateau represents a stable state. This means that if by whatever reason the plateau is perturbed, the inner dynamics of Evolution forces the process to come back to its usual state. We know this for certain because no fluctuation is amplified, be it negative or positive. All are damped and returned to the equilibrium state. To reinforce this conclusion, let us recall that in past works, we have programmed Evolution to start with a perfect chromosome that encodes for a perfect solution to the given task, and it evolves into the plateau. So, the graph of the best fitness shows a decreasing function when the initial condition is perfection. \n",
    "6. When Evolution rises to its final state that is given by the plateau, it does it in general very swift for low complexity values. But, the velocity diminishes as complexity grows.  \n",
    "7. The role of recombination is, in our opinion, insignificant. \n",
    "\n",
    "**Challenge.** Quantify the variation of the fluctuations around the plateau. Prove than when complexity grows, the magnitude of the fluctuations diminishes and hence, it becomes the more difficult to reach perfection.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e10e87e",
   "metadata": {},
   "source": [
    "## The ramp.\n",
    "\n",
    "The graph of the best-fitness in the population against generations is fairly described by a ramp that is followed by a plateau, both represented by straight segments. Let us pay attention to the ramp to rise to the plateau. We can measure its inclination and the place when it meets the plateau. Its $x$-coordinate would say how many generations are needed to meet the plateau.  \n",
    "\n",
    "**Example.** In relation to a graph of the proportion of solved sites against generations along an Evolutionary Process, a ramp is defined by the initial point that is the same initial condition of the Evolutionary Process, and a final point that is the intersection of the ramp with the plateau. The height of the plateau is given by the power law previously found:\n",
    "\n",
    "The height of the plateau, $y$, is accurately fitted by the power model\n",
    "   $ y = 8.2132 * x ^{-0.4466} + 0.5$, where $x$ is the length of the target sequence.\n",
    "\n",
    "The procedure to determine the ramp is the following: \n",
    ">\n",
    "> 1. The function of the best-fitness-history is locally smoothed for a better assessment. A smoothing window is determined by a width, say 10, and for each generation, the next 10 values are averaged. \n",
    "> 2. A generation $g$ is determined where the plateau begins. After many trials, we found that the following condition does it:\n",
    ">>\n",
    ">>> $smoothedFitness[g]- smoothedFitness[g-1] < 0.001$\n",
    ">>>\n",
    ">>> This $g$ is the *breakIndex*.  \n",
    "   \n",
    "> 3. The fitness function must be restricted to the interval $[0, breakIndex]$ and the regression line associated to it is found. \n",
    "> 4. The ramp is the regression line of the points of the fitness function but restricted to the interval $[0, crossIndex]$, where the $crossIndex$ is the generation at which the regression line intersects the plateau.\n",
    "\n",
    "We might replace the overall fitness function by the ramp-plateau ensemble. The ramp is defined in the interval $[0, crossIndex]$. The plateau is defined in the interval $[crossIndex, \\infty]$.\n",
    "\n",
    "The code to determine the ramp follows.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06e2d932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Program ramp.py\n",
    "# The task given to Evolution 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": [
    "*Our observations.* We have a procedure to replace the fitness function by the ensemble *ramp-plateau*:\n",
    ">\n",
    "> 1. The initial point of the ramp line is quite distant from the true initial point.\n",
    "> 2. The slope of the line is therefore biased towards low values.\n",
    "> 3. The cross point of the ramp line with the plateau seems to represent the ramp quite well. \n",
    "\n",
    "Now, we can easily compare different ramps for different values of the length of the target string. We can consider the slope of the ramp line and the x-coordinate of the cross point as indicators to be compared. \n",
    "\n",
    "**Exercise.** Find the best mathematical model of the ramp for a population size of 30.  Assess the role of recombination.  \n",
    ">\n",
    "> *Answer.* We pay attention to the ramp. For each case, the initial point of the ramp is that given by the GA, and the final point by a minimal error procedure, which also can be found by Evolution. To that aim, we reuse a program of the previous work. The code follows. "
   ]
  },
  {
   "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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S7o8//lDZRFx5MS4LMi9gu7878zczWz9kafSrFvgNsD9gvZDVs/28o7APox7DlQijt956Sy0rMreJWdekblOOSkpZIgNovAKhZ4b1ZUYbdpQl6kNjXwtkoZHJSwz8HsarGfheZJUSU+/gql5ERITKlBuvUmH5UNaLFi2S5GTc7lCeKFtcxcA64LkRrnToV4mMxxdkS5GhNvblMB5vAMcWHI9RZ6D89d8RGWrU6+vWrVPHycddF/wGaHOK7L89jh7f9eMh9s2AgADL53EsRMY5KZIrXsG8kK227UOjf84ZZW97zIvvYbt9PI4HDx7YzVbr64n3jX/jm1Z/P7Xn6QiHm2ToAY+9ITtwKRIbBS7PGSuof//9V1WkH3zwgXrYg0sy2KF1hQoVsnpfryT1HQwbFugHTFu2aXdHXLt2TR3MkzpuJzr74dINAhass/5jIHhEs4y2bdvG+1nsWAhuMCyKEZq+2E4HOCDbQkCBwFzv2GesQJ3B7DcxlrftdHpFZnu5Q389vkoTcJBCBWaEYAoVpxlcbkUg4Qy266Ovu77c2G5QuWP4qPiGkNI7AuFSMA6+OGDjt0WHBFSQuPynw+VpHABQXrg0hw6k6JxgDIRtYRlwyTK+7QIV7blz59TlwMqVK6vAE5cLcckc8H9cVtX3p8SsU2K3M2MdYjzI4YRK3/cQHBqHlcP+juYD8V3ytV0WZ/5mZuuHcp00aZJqaoRhNo3LrDeHSCzs42jKYTy51H9H/f3ErGtStylHJaUsE6rf9XW0rf8QPCd2zGpcmrdNduD7EjP0aXz1LgI/lKHtb+JsaPKBS/SbN29W+7kRAmbjfmRvO8Xy4aTKthxsy1c/rtprCmj8vsSetBghaEETC+zjefPmVU150GQP26Oe1HD0+K6XO5oc2krqSWtyxCsYKAHMYgtnlL2zjnmJPQEKDw+P8zqaG+rvG//GN63xpDA15+nUgBk7JgK7AwcOxHlPzxiiDY+RflaE9mrxndnY7rjxBUR6G1x9nmjnZC9zmNIjQGC50J4Lwaq9M1vsYAgQ7GXmk5uzekgn9JskNJ2jnzdCz9UmTZpYvYbAJCVv9ODotoiTxPgqO70dI4IetDFEPwBkfZCZQ7CFjDjaRwIyG8hQoB0kMr/o1YsDDDLCiWlzaQaZ5E8++URlMBCYoc05sjn6fpOYdUrsdoZgHVCHGE8UEMzpJ1U4KBjb42F5cBI0YcIEu/O0PRlz5m9mtn7oF4CDKtomoo2z3k4emcjHzcQ5ypH9Kjm3qaSUZVLqgqRKye9KDgi2kGHEfoPtH9s6AnVciUD7Ydvt7HHqe31e2D7QBtQeZxzDsH/gigSu6CLJg30I7Y+R2a1atWqyHN/ju0JsO957csQrKVX2ly9fdjiOc1ZcEBQUFCfjrmf/ASf/+nTG122n1adL7Xk6IlFbHzKl6PCFDmoJNUIHPYuBJgfOOgPSL0Gi01lC83T0jnbIXuHM1d7JQELQqQqX2tEBRc8E6XCWiUs2qBzi6+iEIehwmQnZA2OWWc9UG6cDe0NuYcB8ZAmN2eXEcLScUhKyobYDiye1aUVyrTe2GwSdqHgd2b7x+yBgxQMZdHSgQfCKjiD65SHs3OjUgwdOttAxC9PEF9xgGbDdxLddIIgzBpX4bgToCNiR5cGVFeONXRK7TomBbBI6feGqizFgTmh/37t3rwocnLGdOmv90IEMJ3Tff/+91evIuOodoSAxy4x9HFchcLXOmGXG76i/nxSJ3aYclRzbir6OqP+MJ8zo4IOEjDEAT4l6y1jvGrPy2H9xAu+M9Y5vPdDBD5kxnNQaM5mJaXaI5UcnVARwxu+xPb7ox1UcB511XI0PvgtZZjyQXUWQOH78eHWl1tHju/676NlZI9u6UM/M2t5oxfbqQHLGK4gtbIPtpJR9fGyH+I0POhKiWaczVKlSRW2LOIYYrzTrHXj14B/ZdZzooLkVTuCN+xCaoxhfS815OiJRg4miBz4OzsiqoPlFQmdV2OjRvhlNNuydCeCSXmLhzA8rjQwP2mOazVMPIBO6IxGCCrQBRQWFHyAxZ4t6cwz0ZMcICMYH2onhkhECBLP1wXpgFAXjGafeDty4Q+CH/d///me1PtgRkTnCpdakwvKntbs2oZJD5WF8OHscZUe3j/ggu4DmBAg+7Z1sGbdFtE0zQqYIVySwbeH3R9Bh2yYR+w/Ofu1dTjIuA5p3YGQV4xUe7J/oDY/2wMZKAid1yNiiKQYe2K4aNmyYpHVKLATJaGOIy/dYXnts9zVUfMgOGPcPHdqe4cpOYjhr/TAf22VFe2vbTEZitjHsw9gOvvrqK6vXkU1EkJLYADep25SjkmNbQR8SNGnB740gWYc61LYJ1+Puv45AvYN9FSOQGH9vnCihbM2a2zkqvvXQs5fG78V3IuhxFI4v2CYRdBsvRdvuT2iug8Dtiy++sNvsMinHVVtICumXwXX4Tpx06dujo8d34/HQuI0jyYITBNvgGmWJtsBGuMKX3PEK6masH7Lotuuu/66JKfu01Ib5mWeeUXWMsTkWfkdsn2h1oCdqkNXGfoRYCckAHa4iYH0x0lBamKfTM8wI/nAQxiVctBNCRwxkAvHD42wb7yH4NA7rgsAPB20cpBFA4iwOB3O0yUJmFtmjxMDOhCFmcAMEZEqQHUOmA8PSoAMGDsr6AQcbIqATDTYU7DTx3SYZOygCT3So0Ievwk6DgyA6TtkbjB6FjoMFgoD4gjl0tkBbR2R2sEPaQqCObD3OtnHWj8tvqNz09rvGs3lcssFBs27duqoNqj6sHDaex7kFOMoJmS1c9sPBFG3hEjN0oKvSt4/33ntPbRfILOByYWIy9ciY4uwV5YXtG0Ewfju0oUOZ6r8jKk5kyLF9IrOLdrnYTnHARYWKgw/2G+zc2KdwCQ6fRydBZF/M4AY5qAyxnyGLiDNvVPrYPtGG1RayzGgKgm0W25HtkHuOrlNSoILDUF/Y7rEto9LDyREuKWLeOKgZA0Ps5xjWCR1PsUwoP1R+yLridVzWtddR14wz1g/ZclxVQic2DGWGTr8I6mzbBuNAiLoDHRvxO2Pbwvfaa2+KbQ9ZVWyPOPnBdoA6CScXuJRtO9xjQnAgcWSbQicsfC/ayia2HnH2toLgFMuA4brQjhUnTCgLDM1l2xY3MWWbVDi24AoQrspgu0V9jgwmgq2aNWsmaojExNZDqDNQHvg/hopDIIBAF8cRewGdPfgc6hkcszGsHAJNbKf68UovT9QBuHqMfQ/9HbBdo60ugm38vjju6sNCJrXexHB0uFKE3xTbCeopNBVCPKAflxNzfEcQivoT9R6SeNjWcDzE8hsDTxwfEUDhPawvths0jbNtX59c8QpOeDF0K7YX9FtBfYf54AQCAX9iyj4+zrwaiDpYP7lAsI6khH4TtoYNG1oSLNjXUK7YP1CWyKBjfbC/2l55wxUt1JN6fIWyRB2EbRz7lS6155kgLQkwlBFui12iRAk1sDwG2cYwNbghgXHgfh2Gu8KwZxjqA8NaYdDyJ598Uvvzzz/jDNNiO6xbfEPC4DmGYcFQM1gG3NQAA6Lv2LHDMk1UVJT2+uuvq8HaMSyTcXXtDSyPm49gOTE9hofC0D0Ykim+G5foQ8LZu0mFbs2aNWoaDJ8U380KMAzRc889Z7lxCdYDw2Xhc7Nnz7aaduXKldoTTzyhyhwDnGNA+vhuXGJveCN7w8odOXJEDT+Gedq7cYntfPTfyjiskH4jBXvD9tgOI+TITUaS+8YlutGjR6vtEUOX2Q5BZ284Lvx2tkPwXblyRU1bsGBBtX1jO8fwRLhVunGIPZQxhszCtoXtFQOq64OpYxvD88qVK1tuPoL/27tJQXw3LsH+gEHbMYA+bnaAG4XYgxtF6MMMxTcwviPrlNTfEcMrYRhF3J4U2zAGoMf8USdgMH/st7a3kMYNJ/QbFOCGAriZwKhRo6wGo3f2b2a2fhh2CjcuwbBW2G+wT+JGMcaba+hw0wPc9ALraRzayV5dgGG1MDQVbrKB5cJwU2Y3LjFbV0e3KdywxtHhleztQ49TlvHdPAI3+cG64PeuVauWqg/xm9ve9ja+so3v1tj2ytwRGEYOxzisX968edXxz/YGPUkdVs6sHpo/f75WqVIldYzDjbSwH2AoO9v6V79xiT0nT55U72E7xbEN261+7LK9CRluN/z0009b6inMt0uXLuqGWY4srxkMPYjtBOWIbRHHOgy3hptd2XLk+A5YDwxViGXFdmDvxiWA3wTDCaJuRP3x0ksvqRuh2Nv2kiNewe+Ioen04za2adwQKSlln9ziuzmN2Nn3UZfjdtMoKywzhoO0HdpQh6FBUQb4PbEdYlswDjebVuZpxgP/JP4chJIb2j1jkHRktx1t70lElFhoaoeOy7jC5aybGjgbmqkh04h2//aa51Di4AYbuCkMsnLGUR/SA7TRxVUT20EIiB5XotowU/KwHQcQl5xx+QiXYpxxNy4iovjgsi9GK0grwTLaetrmcXAXPVxyRxtTerzjC8oXTbbQxDK9BctEySllx2Aju9BeD5Ua2iaj3SmGfMKwamhX7awhYIiI7DG7mU5q2LJli8p+ot0hOgCiPTTaGqJnvLEzz+NC+0zbocWM0H7YeJOoxEI7WnuduBI7tvzjQlYeo2ygkxw6yKEfAfoAmHVGTyzMN6EbQDh7lCOilMaAOQ1A5xY0VkdHBJz9o1E6Msy4Jz0RkTvBWOvouY6RKZBVRtCKm1ugg6HxLoGPC52wzG48gs5EuLSfVBj1QB9jPT4pMbY8OryjUxkCZJwgoMPd7NmzLXf3dAZ0KERHKjNs/Umujm2YiYjI7eAuemZZUYxmoI8IkRS4fXVCt+HGiAzOHi4zNWAot4Ruv54ad6MjciYGzEREREREJtjpj4iIiIjIBNswuyAMsYTLXxisPy3e1pqIiIjst+XGTYVwkzDbm0ZR2saA2QUhWE7M7RyJiIgo7Th37pzVXZEp7WPA7IKQWdZ3OIzV7EyRkZHqdry4vSRueUosE24r3H9Ypzgf61r3LJOwsDCV8NKP4+Q6GDC7IL0ZBoLl5AiYM2XKpOabXiusxGKZsFy4rXD/Yb3CutaZ2JzS9bABDRERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMRERERETpPWBet26dtGvXTvLly6duNzlv3jyr9zVNkxEjRkhQUJD4+flJs2bN5Pjx41bT3Lx5U7p3765uCZ0tWzbp27ev3L1712qaffv2SYMGDSRjxozqXvCff/55nGX5448/pEyZMmqaihUryuLFixO9LERERESUdqSLgPnevXtSuXJl+frrr+2+j8B28uTJMn36dNm6datkzpxZWrZsKQ8fPrRMg2D54MGDsmLFClm4cKEKwvv37295PywsTFq0aCGFCxeWnTt3yrhx4+TDDz+UGTNmWKbZtGmTdOvWTQXbu3fvlg4dOqjHgQMHErUsRERERJSGaOkMVmnu3LmW5zExMVpgYKA2btw4y2u3b9/WfH19td9++009P3TokPrc9u3bLdMsWbJE8/Dw0C5cuKCeT506VcuePbsWHh5umWbYsGFa6dKlLc+7dOmitW3b1mp5ateurb300ksOL4sjQkND1fLir7NFRERo8+bNU3+JZcJthfsP65TkwbrWPcskOY/flLy8JZ07deqUXL58WTV90AUEBEjt2rVl8+bN0rVrV/UXzTBq1KhhmQbTe3p6qixwx44d1TQNGzaUDBkyWKZBZvizzz6TW7duSfbs2dU0gwcPtvp+TKM3EXFkWewJDw9XD2O2G7QyZUTzNL9IoFWpItFz51q95tWxo3js2WN3ei9Nkxbh4eLl6yvRgwZJzKBBj968c0e8K1USR0T99ZdItWqW5x6LFonXa68l/MHMmSXKkJEHz3feEc85cxL8aEzr1hIzdar1+tSpIx5XriT42egxY0Tr1u3RC0ePinerVnHKRPPwiPPZqE2bRIKCLM89vvtOvD75JMHv1EqWlOjly62Xt0cP8Vi/PsHPxvTpIzEffGD1mnfRouKI6B9/FK1Ro0fLu3atePXq5dBno06dsvw/MjJSSs+eLV6vvmq3XIy0Bg0k+qefrF7zatFCPBxojhT93nuivfjioxcuXRLvevUcW96lS0VKl7Y89/jtN/F6990EP6flzSvRW7ZYveb56qviuWSJ6eewrZSrVUsimze3et27QgVcCkvwe6O/+kq0tm0fvbBrl3h36iSOiNq3TyRr1kfLO3GieE6alODnEltHGMUMHJhgHRHf/pOe6oiE2K0jPv7YtF5JD3UEeI4eLZ4//JDg51BHRH7/vaVuSa91hBYT49ByUdqT7gNmBKiQN29eq9fxXH8Pf/PkyWP1vre3t+TIkcNqmqI2lY0+T7yHgBl/E/qehJbFnrFjx8qoUaPivO5x6ZKYhykit7JkkfU27agb/Puv5Lhwwe70mJ/ff/8/snOnHDV81vv+fWkbz+dsbV6zRm4Z1infpk1S04HPRvr5xWn3XWXfPinswGcvHzokO2w+2+LMGfG7cSPBz+7ftk3OBgRYnmc9e1aa/vedxjKxZ9WKFfIwVy7L8+Lbt0sFB5b3jqenrLZZ3rpHjkgeBz57au9eOWjz2fYO/jbbNmyQa4bgLffu3VLPwc/a/jbl798Xz4sXE/zctSNHZLPNZ5ucOCH+Dnzvke3b5US+fJbnGa9fl5YOLu/61avlzokTlueFtm2Tqg58Fk2kltssb41DhyR/Ap/FtpLh7l3VtMuozblz4vPgQYLfu3vTJrloCJ6yHzkiDR1c1+XLlklUpkyW56V37pQyDnw2sXWE0XEH6oj49p/0VEckxG4dcfGiab2SbuqIvXulhAOfVXXEf/uNvv+k1zqCXFO6D5jTg+HDh1tlrpFhRqdDLSgowQxzthIlpE2bNlaveX37rWg2HRp1aNWCbLavr6+UrF5dihs/e+eOaPnzO7TMdRs3ts4eaZpDn/XOnDnO8nquWyfa4cMJfjawXLm461q4sGgZMyb42Yq1akkF42ePHrUsr7FM0KnUVlNkE43Zo4sXRbPJCtmTpXjxuMs7e7Zo164l+NmilStLYZvPOvrb1Kpf3zp7lDmzw581Li+yQGdnz5aY/zrbmslVpkzcdZ040aFsS5maNaW08bOXLjm8vA2aNLHOHoWGimbTKdge37x5426HCxeKduaM6eewrURkySLNmzcXHx8fy+ve2F8dyDBXrVdPqhi/NzDQ4XVt0bKldYb52DHRNmxI8HOJrSOMHKkj4tt/0lMdkRB7dUTMsmWm9Up6qCPAc+dO0XbuTPBzqCOw3yBY1vefdFlHYH0uXXJo2SiN0dJ5G+YTJ06o13bv3m01XcOGDbU33nhD/f/777/XsmXLZvV+ZGSk5uXlpf3999/q+QsvvKC1b9/eappVq1aped+8eVM9L1iwoPbll19aTTNixAitUqVKDi+LI9iGOWW5Q7u6pGC5sEy4nXD/YZ2SOGzD7LrSxSgZZtCMIjAwUEJCQqwytGibXLduXfUcf2/fvq1Gv9CtWrVKYmJiVPtifRqMnKG3rQKcCZcuXVo1x9CnMX6PPo3+PY4sCxERERGlLekiYMZ4yXv27FEPvXMd/n/27Fl1qWvQoEHy8ccfy/z582X//v3So0cPNWYzhnyDsmXLSqtWraRfv36ybds22bhxo7z22muqEx6mg+eee051+MOQcRh+bs6cOTJp0iSrphIDBw6UpUuXyvjx4+XIkSNq2LkdO3aoeYEjy0JEREREaUu6aMOMoLQJ2iH9Rw9ie/bsKT/++KMMHTpUjdWMcZWRSa5fv74KbHFzEd2sWbNUYBscHKxGx+jUqZMaL9k4msXy5ctlwIABUr16dcmVK5e6AYlxrOZ69erJr7/+Ku+//768++67UrJkSTVCRgX0kP+PI8tCRERERGlHugiYGzdurDqWxAeZ3Y8++kg94oMRMRDsmqlUqZKsT2A4n86dO6vH4ywLEREREaUd6aJJBhERERFRcmHATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERAyYiYiIiIiShhlmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMuFWAfOdO3dk0KBBUrhwYfHz85N69erJ9u3bLe9rmiYjRoyQoKAg9X6zZs3k+PHjVvO4efOmdO/eXfz9/SVbtmzSt29fuXv3rtU0+/btkwYNGkjGjBmlYMGC8vnnn8dZlj/++EPKlCmjpqlYsaIsXrw4GdeciIiIiJLKrQLmF198UVasWCE///yz7N+/X1q0aKGC4gsXLqj3EdhOnjxZpk+fLlu3bpXMmTNLy5Yt5eHDh5Z5IFg+ePCgms/ChQtl3bp10r9/f8v7YWFhar4Iynfu3Cnjxo2TDz/8UGbMmGGZZtOmTdKtWzcVbO/evVs6dOigHgcOHEjhEiEiIiKihLhNwPzgwQP566+/VFDcsGFDKVGihApk8XfatGkquzxx4kR5//33pX379lKpUiX56aef5OLFizJv3jw1j8OHD8vSpUvlu+++k9q1a0v9+vVlypQpMnv2bDUdzJo1SyIiIuSHH36Q8uXLS9euXeWNN96QCRMmWJZl0qRJ0qpVKxkyZIiULVtWRo8eLdWqVZOvvvoq1cqHiIiIiNw8YI6KipLo6GjVBMIITS82bNggp06dksuXL6uMsy4gIEAFxps3b1bP8RfNMGrUqGGZBtN7enqqjLQ+DQLyDBkyWKZBlvro0aNy69YtyzTG79Gn0b+HiIiIiNIOb3ETWbNmlbp166psLrK6efPmld9++00FqcgyI1gGvG6E5/p7+JsnTx6r9729vSVHjhxW0xQtWjTOPPT3smfPrv6afY+t8PBw9TA2+4DIyEj1cCZ9fs6erytjmbBcuK1w/2G9wrrWmccTcj1uEzAD2i736dNH8ufPL15eXqoZBNoSo61xWjZ27FgZNWpUnNeXL18umTJlSpbvRBttYplwW+H+wzolebGuda8yuX//fmovAiWRWwXMxYsXl7Vr18q9e/dUlhajYTz77LNSrFgxCQwMVNNcuXJFva7D8ypVqqj/Y5qrV6/GaeqBkTP0z+MvPmOkP09oGv19W8OHD5fBgwdbnmPZMfoGOhditA5nn/2ismrevLn4+Pg4dd6uimXCcuG2wv2H9QrrWmfQrxCT63GrgFmH0S/wQJviZcuWqY6AaEaBgDUkJMQSIGPDRtvkV155RT1Hk47bt2+rjHT16tXVa6tWrZKYmBjV1lmf5r333lNBlh5wIgAtXbq0ao6hT4PvwRB3OkyD1+3x9fVVD1uYf3IFtck5b1fFMmG5cFvh/sN6hXXt4x5HyDW5Tac/QHCMUS7QwQ8BapMmTdRYyL179xYPDw8VwH788ccyf/58Nexcjx49JF++fGrIN0DbZ4xu0a9fP9m2bZts3LhRXnvtNTUSBqaD5557TnX4w5BxGH5uzpw5alQMY4Z44MCBajnGjx8vR44cUaN17NixQ82LiIiIiNIWt8owh4aGquYN58+fVx31OnXqJJ988onljG/o0KGquQbGVUYmGcPGIbA1jqyBYeMQ2AYHB6vRMTAPjN1sHFkDbYsHDBigstC5cuVSN0MxjtWMG6b8+uuvagi7d999V0qWLKmGrqtQoUIKlwgRERERJcStAuYuXbqoR3yQZf7oo4/UIz4ItBHsmsEYzuvXrzedpnPnzupBRERERGmbWzXJICIiIiJKLAbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCQbMREREREQmGDATEREREZlgwExEREREZIIBMxERERGRCW+zN4mIKH2IiYmRiIiIFP/eyMhI8fb2locPH0p0dHSKf39axXJJv2WSIUMG8fRkPjK9YcBMRJTOIVA+deqUCppTmqZpEhgYKOfOnRMPD48U//60iuWSfssEwXLRokVV4EzpBwNmIqJ0HoRcunRJvLy8pGDBgime+UKQfvfuXcmSJQuzbiyXdL+tYB0uXryo9rlChQq5dOBP1hgwExGlY1FRUXL//n3Jly+fZMqUKdWagmTMmNFlg6DkwHJJv2WSO3duFTRj3/Px8UntxSEncd0tkoiIEqS3BeXlYaKUoe9rrtwOm9JwwBwWFibz5s2Tw4cPp/aiEBGlO7w0TMR9jVwwYO7SpYt89dVX6v8PHjyQGjVqqNcqVaokf/31V2otFhERERFR2giY161bJw0aNFD/nzt3ruqYcvv2bZk8ebJ8/PHHqbVYREREDuvVq5d06NCBJUaUzqVawBwaGio5cuRQ/1+6dKl06tRJdUhp27atHD9+3Onfh7ZEH3zwgRrqxc/PT4oXLy6jR49WgboO/x8xYoQEBQWpaZo1axZnWW7evCndu3cXf39/yZYtm/Tt21f16jXat2+fOhlAxwX0Sv/888/jLM8ff/whZcqUUdNUrFhRFi9e7PR1JiJy5UAUzUj0R86cOaVVq1aqfnWWDz/8UKpUqeLQdMZl0R8rV66USZMmyY8//miZtnHjxjJo0CCnLSMRuXnAjEBy8+bNcu/ePRUwt2jRQr1+69YtFUQ622effSbTpk1TzUDQThrPEchOmTLFMg2eI8M9ffp02bp1q2TOnFlatmypBlHXIVg+ePCgrFixQhYuXKgy5f3797dqi411KVy4sOzcuVPGjRunKtsZM2ZYptm0aZN069ZNBdu7d+9W2Qk8Dhw44PT1JiJyVQiQMTwXHiEhIeqmFk8++WSqLEv58uUty6I/GjZsKAEBASp5QkTpnJZKvv76a83b21vLli2bVrlyZS06Olq9PnnyZK1x48ZO/762bdtqffr0sXrt6aef1rp3767+HxMTowUGBmrjxo2zvH/79m3N19dX++2339TzQ4cOIR2tbd++3TLNkiVLNA8PD+3ChQvq+dSpU7Xs2bNr4eHhlmmGDRumlS5d2vK8S5cuanmMateurb300ksOrUtoaKhaDvx1toiICG3evHnqL7FMuK24/v7z4MEDVXfhb2pA3X7r1i1LHe+onj17au3bt7d6bf369aruu3r1quW1s2fPap07d9YCAgJU3fvUU09pp06dsry/evVqrWbNmlqmTJnUNPXq1dNOnz6tzZw5U83L+MBr9owcOVIdpxJaTvzfdp5YFiwD/r9y5UqtevXqmp+fn1a3bl1t27ZtVuWCbadq1arquFO0aFHtww8/1CIjIy3HKCxHwYIFtQwZMmhBQUHa66+/bnVMLVGihPpsnjx5tE6dOmmuJqnbSlpjts8l5/GbkleqjcP86quvSq1atdQdfZo3b24Zc7FYsWLJ0oa5Xr16Kst77NgxKVWqlOzdu1c2bNggEyZMUO/jLliXL19WzTB0yBzUrl1bZcK7du2q/iKTgA6KOkyPZUdGumPHjmoaZB2MQzghS42MNrLn2bNnV9MMHjzYavkwDUYJsSc8PFw9jFls/TaieDiTPj9nz9eVsUxYLq68rWBZ0NwMY9zigVZo9++n3Pfju+/dw93PNMmcOUYcvY8DPqcvN6Dp288//ywlSpRQ9Shex7qh7qxTp46sXbtWZaA/+eQTlZnes2ePqptx9e7FF1+UWbNmqTF+t23bpubbuXNn2b9/vyxbtkyWL19uqfPt3Q1Rb7oX33v6cn755ZfqGINs9KhRoyxj8p48eVL9/7333lNXHfHaK6+8Iq+99pq64ojPrl+/Xnr06CETJ05UTfpOnDghL7/8sqWp4J9//qnm/+uvv6r543iF4xg+u2PHDnnjjTfkf//7nzrWoekgjm+pcWfHx6GXs/F3d0Wx+5mmtk/cMMgoLdUN5EI3LkHgqQefaGOMygs7OypDZ3vnnXdUoIl2w9iA8X2oWNHEAlD5QN68ea0+h+f6e/ibJ08eq/dRQaMttnEatJO2nYf+HtYNf82+x9bYsWMtla8RKvnkuhEBmpwQy4TbiuvvP6ijcLthBJwIGBG8FiiQ0k0IYr/v/PnbkjmzY59AYLFo0SLVXwTQfA/rMXv2bEu/kTlz5qibQ4wfP94ybB4CziJFiqh+IVWrVlX9ZZo0aaKCVEBiQ58/biqBz+n1aHxJCCQscHzSlwVKly6tmolgeiwDji+YF4J0lLk+Tyw3bhwDw4cPV8sEr7/+ujz77LNy7do11Qxx5MiRMnDgQMvy5cqVSx230KQPbaLRnwbHHySasNxI3uB4hu89evSo+j4ka7JmzaqOM+inoydXXM2dO3fElWE/w+hfaLKJbcNI3xbI9aRawIwKAJ3d0I4XwWujRo3UmTZ2erQNRscJZ/r9999VhkE/O0f2AcuAu1/17NlT0jJUssaMNCpBtAFHW2ljBe4MqPxxsEfWn3coYplwW3H9/Qd9MHAlD7cbRmBmk/BKUaivHA2YUX44DkydOlU9xxU69EPB8KNbtmxR/UQQRCJ7i/rQdp3RxhjZZdTv6FSOq4F4ILOMjt3g6+urEigJ1aOYDgGy8SogXsPnsJwIkPV54P+4wmicpx48IxOuv46rqYDACoEw+sbgSqV+1RNwbMS6YJ7PP/+8fPPNN1KtWjWVVW/durW0a9dOvffUU0+pzLX+Hh4IvFPjzo6PA1lZBMsI+l153HD8Zhg4ACcwtn2yXPUkhlIxYMblJVQAsGDBAtUk4siRI+qSGy5bbdy40anfN2TIEHW2jqYVgGD9zJkzKnuLChWZC7hy5YqlMtWf672oMc3Vq1et5ouzR1z+0j+Pv/iMkf48oWn0922hYsbDFirq5DooJ+e8XRXLhOXiitsKgi4984lHlixo3pCyl6cRJCBQzJLF0+EmGVhmBPloQqfDFUk0m/j+++9V0z1kb6tXr66SIbaQUcb6YgQLZG7RuRyJE4yWhJMaBK96UJbQbZgxHYJg47IY39PL1/ia8bn+f9Tj+v/1vwgS8X9kzXEl8emnn47zHQh8URbIJGNkDiw/mnMgs46mKCiTXbt2yZo1a9SVR2SlP/roI9m+fbtLdUjUm2HYlp+rwbJjHezVA2mlXqDES7Ut8vr165YAEZfOcNaPyqhPnz7q0pez4TKI7Q6IzIK+g6IZBZYHl9h0qORxxl+3bl31HH8xVjRGv9CtWrVKzQNtnfVpcBnGeFkPlRuyE3pTE0xj/B59Gv17iIiSC2JEZHlT4/G4SUM9kEJWFpBR1ZsqoG2z8YEgUodmELhSh6uYFSpUUFcaAUGws29fnNR5Yl0QENuuBx76sQtZS2SVMZoTgmP0h9GPl8g0I4OO0Z4w9N7p06fV8YmIXDzDjDa7hw4dUtlcnPnjUpse2No2kncGVDJos1yoUCHVJAPDueHSFwJ0vSJGEw1kLUqWLKkCaGQi0GRDH5S+bNmyqjNJv3791NBzCIpxlo+sNaaD5557TmUJ0NRk2LBhaqg4jNOJzho6ZDvQBAXZAYw7jTZ56LRhHHqOiMjdoe2w3rcDTTIwLCgysajPAX1Q0BShffv2KqNaoEABdeXw77//lqFDh6o6GvUqmiygjkZAigAbnesAbZ1xdRNN9PBZNAWwdzUvMTBPJFoQsCIrrN9vICHo2Ich83CMeuaZZ1SQjE59OIbguIRMOQJxJGeQcf7ll19UAI2mKWjGiKYpaAKAxAySUEjkIFFDRE6SWsOQYHgcDPFTpkwZrVChQtrDhw/V699//71Wp04dp39fWFiYNnDgQPVdGTNm1IoVK6a99957VsO/YdieDz74QMubN68amic4OFg7evSo1Xxu3LihdevWTcuSJYvm7++v9e7dW7tz547VNHv37tXq16+v5pE/f37t008/jbM8v//+u1aqVCk1PFD58uW1RYsWObwuHFYuZaXFocLSApaLa5SJKw8rZxyeLWvWrGp4uD///NNqukuXLmk9evTQcuXKpepc1O39+vVT9eTly5e1Dh06qCHYUNcWLlxYGzFihGVZcNzB8GsY3tQZw8oBjhk4hmHoONth5VAOup07d6rXTpw4YXlt6dKlatg7fBbHl1q1amkzZsxQ782dO1cNP4rXM2fOrL4Dw9Tpw+01atRIDauHz1aqVEmbM2eO5mo4rBylZR74R1KxHTM6o6A5Bs7uAcPioM0VMgZkH5qK4HIjen8nR6c/ZCfatGnDtlYsE24r6WD/QQckZFFx1Sw5bgqVmDbMrtwu1dlYLum3TMz2ueQ8flM6HlYOl53AeCe9tD5iBRERERG5l1Q7hUNbrNGjR0v+/PlVOy99YHe0G0YPaCIiIiIitw6Y0QEPnRjQo9d4Vzz0YP7uu+9Sa7GIiIiIiNJGwPzTTz+p3svo5WwcFaNy5cpqPGYiIiIiIrcOmC9cuKDGl7TX6J/3WiciIiIicfeAuVy5crJ+/Xq7I2dgkHkiIiIiIrceJQODtGNEDGSakVXGQPMYVB5NNTAIOxERERGRW2eYMc7yggULZOXKlZI5c2YVQB8+fFi91rx589RaLCIiIiKitDMOc4MGDWTFihWpuQhERERERKZS/VY6ERERcv78eTl79qzVg4iIiCilYchb3HGYKE0EzMePH1cZZj8/PylcuLC6hSQeRYoUUX+JiMg9eXh4mD4+/PDDVF22efPmJTjd2rVrpWnTppIjRw7JlCmTlCxZUvXbQZKI0k5Qiphj4sSJqb0Y5AJSrUlGr169xNvbW3XwCwoKUpUQERHRpUuXLIUwZ84c1ccFncJ1uDtsYiBINd4gK7kdOnRIWrVqJa+//rpMnjxZJYaQJPrrr7/UXW6JyPWkWoZ5z5498s0330jr1q2lSpUq6oYlxgcREbmnwMBAyyMgIEAlVPTn9+7dUze8yps3rwqca9asqTqP22YNR48eLT169BB/f3/p37+/ev3bb7+VggULqoxvx44dZcKECXGynP/8849Uq1ZNMmbMKMWKFZNRo0ZJVFSUZb6Az2KZ9Oe2li9frpYVd7LF3WuLFy+uAmh8P4Jn3ebNm6VRo0bqNSzXG2+8odZPd/XqVWnXrp16H1deZ82aZZURPX36tFoOHE91t2/fVq+tWbPG8tqBAwfUsRblhXJ74YUX5Pr165b3GzdurL576NChKiOOZbfN4mO+L730kvo8ygbrZRzRasOGDZarxvbWJbHwfS+++KLkzp1b/YbI1u/du9fyPpYPscPPP/+sygTbSdeuXeXOnTuWafB/bCsYWACJuS+//FKt66BBgyzrfebMGXnzzTctVy+Mli1bJmXLllXlht/PeCJH7idVx2E27rBEREQJuXv3rrRp00ZCQkJk9+7dKpBBUGnb9+WLL75QyRdM88EHH8jGjRvl5ZdfloEDB6oAE6MxffLJJ1afwb0BEGRjGmSJkdRB0wF9uu3bt6u/M2fOVMGT/twWAk68v27dunjX48SJE9K5c2d5+umnZd++fSqTjqDztddes7oSe+7cOVm9erW6R8HUqVNVEJ3YwBPBJu5vsGPHDlm6dKlcuXJFunTpYjXd//73PxVYbt26VQX6H330kaVTPoZ+RcCNMvzll19U2Xz66aeWu/RiXfA7dOrUKd51SSyUDdZ1yZIlsnPnTnUSExwcLDdv3rQqQzSPQeCOB5rBYLl0gwcPVss8f/58tS74fXft2mV5H8PZFihQQK0rfi9jQHz//n21DSEgx++I7evtt99O8vpQOqClkpCQEK1u3bra6tWrtevXr2uhoaFWD4ofygc/XXKUU0REhDZv3jz1l1gm3FZcf/958OCBdujQIfXXyvjxmpY/f8KPdu3izhSvOfLZ8eO16Oho7datW+pvUsycOVMLCAgwnaZ8+fLalClTLM8LFy6sdejQwWqaZ599Vmvbtq3Va927d7ead3BwsDZmzBiraX7++WctKCjI8hx179y5c02XJyoqSuvVq5eaNjAwUC0Lls9YZ/fp00fr2bOnVbmsX79e8/T0VL/V0aNH1ee3bdtmef/w4cPqtS+//FI9P3XqlHq+e/duyzQoa7yGYyuMHj1aa9GihdXynTt3Tk2D74BGjRpp9evXt5qmZs2a2rBhw9T/ly1bppZLn95W3759tf79+1u9ZlwXR39XfVtZu3at5u/vrz18+NDq/eLFi2vffPON+v/IkSO1TJkyaWFhYZb3hwwZotWuXVv9H6/7+Phof/zxh+X927dvq88MHDjQalvRy9O4bCiff//91/La119/reXNm1d7rH0umY/flLxSrQ1zs2bN1F+cMRqhPsJlEbbzIiJKRmFhIhcuJDxdwYJxX7t2zbHP4juSIcOMy/GLFi1SGUE0l3jw4EGcDHONGjWsnqMNNJpSGNWqVcuqWQEu+SMjacw841j08OFDlXFEUw5HIPOKLPTHH38sq1atUlnbMWPGyGeffSbbtm1TzQOQicUDmWPj8Q/Z3FOnTsmxY8dUP5/q1atb3i9TpkyiO8phnZChttfuGxnaUqVKqf9XqlTJ6j0so57NRkYemVh9WnvfgXVBkxF764JmDYmBeeF3zpkzp9Xr+J2xzDo0xciaNavdZT558qRERkaq31iHZhulS5d2aBnwW6Mpjb15k3tKtYAZOzAREaUSf3+R/PkTni53bvuvOfJZfIeT4bI4Lq/jcnmJEiVUm9lnnnkmzugTaF6QWAjS0GYZzSRsod1uYuXPn1+1F8YDbaoRcE6fPl19B74LTS7eeust8fS0bh1ZqFAhFTAnRP9cbOI7FoJE23VCkxUE67YQBOp8fHys3kPiCgEvGNtd24PvQPtmtFu2hXVJLMwPy2Zsh60znjCYLfPjsjdvYzmT+0m1gBkdGNAxwLaRPTZItNkiIqJkNHhw7CMp5s93fFonBTA6ZIARaOrZYgRX6PyWEGQWbdsc2z5HO1lkohGImwVSSbkCmj17dhUE6h3h0KZY/y7bgFnPJiN7jva76NgImB5tknXoEAfItGN+YOwAqK8TRudANhYZ66RA9hn3S0AQby/LjO9Au2azcksMrMvly5fV8sbXsTIh6LCJ3wq/sR60h4aGqnVo2LChZTqMnsIr2pSmO/0hYL6Gy3o20KCf4zATEZE9GM8YnbUQGKIpwHPPPedQVhFDvC1evFiNjIEh3tChDx3KjEkbDF/3008/qQzwwYMH5fDhwzJ79mx5//33LdMggEOHQwR0t27dsvtdmPcrr7yiRstAEwLMa9iwYeovsr2AESnQPAPLhXXBMmGEDr2jHAJ8dKRD5hZNOhA4Y9QIY7YX/69Tp47q6IZlRac347LCgAED1HG1W7duKnjE8mD0h969ezscKGIkDwSZ6NSH7D6aWaDs0IEQsG6bNm1Sy25vXeKD78f0xgdOCtBks27dutKhQwdVhjghwvzfe+891XHREWiqgXGvhwwZoq5oo+z79u2rTk6Mvzl+T3Tqu3DhAgcioLQZMOttlW0hW5CUS19ERJT+IeBFtrZevXoq+GzZsqXKcCbkiSeeUM0h8HmMnoFgD8OJGY83mBfaNCNIQ1YXwSiGIsPNtXTjx49XQSOukOpZXVtoN4tjGUblKF++vAo4t2zZokZ0wP/1rC2+CxlPDMeGeSFgz5cvn2U+aAeN5/gMmolgeLw8efJYfdcPP/ygMtFo64zh0tBu2gifR1YewWmLFi2kYsWKajo0bbCX2Y4PstQoEwTeGOUKAb8ecGNdEKybrYs9KCNMqz+wDpg/YgOc3CBIR2CPrDaGjMMQcBjWzlH4rRF4P/nkkyoIxzaA9tTG3xwjZCAgR3tlPWNPZI8Hev5JCsIwLzBp0iTp16+fVScK7Hw4k0aHCezgZF9YWJjqvIDLSxif0pnQ/g0VFYZtsm3D5a5YJiwXV95W0GENGUFcuUuNZASyv6izUFclJkBLCTgGHTlyRA035irlgowoAl59LOH0JLm3FTSHQbtynPQg25wa+1xyHr8pnbVhxpiYgDh9//79Vndfwv9x5s+xDomIyNnQURDjL6NDIJoUYOxhjG1M6RPiDZwQIeOPABXZZGjfvn1qLxq5IO/UGh0Dl1mQZeYZFhERpQS0GcZNOXAHOHQKw22r0S6Y0vdJEtpFIyGHJh+4mpArV67UXixyQak2SgbaZhEREaWU33//3eUL25ERQSgW2kWjsySRywXM6LSA24wiq2xvnEsj9IImIiIiInKrgBkN3fWRMfB/IiIiIqK0zjulm2HgNqEYKoZNMoiIiIjIFaT4GD/ooYxB1HUY5xIDhhMRERERpUUpHjDbDvuMu++Eh4en9GIQERERETkkbY0iT0RERETk7gEzOv0Zb4lt+5yIiIiIyK3HYUaTjODgYPH2jv3q+/fvS7t27azu+Ae7du1K6UUjIqI0olevXupOfIDjRYECBaRz587qbm2pcYtvInJvKR4wjxw50uo5b1FJRET2tGrVSo2oFBkZqW5A0bNnT3VF8rPPPmOBEZF7BcxERET2+Pr6SmBgoPp/wYIFpVmzZrJixQoVMN+4cUNee+01Wbdundy6dUuKFy8u7777rnTr1s3y+caNG0vFihXFy8tLZatxJfPjjz+W5557Tn32zz//lLx588qUKVOkdevW6jNr1qyRJk2ayMKFC2X48OFy7NgxqVKlinz33XdSoUIF/lBEboqd/oiIKM07cOCAbNq0ydJ87+HDh1K9enVZtGiReq9///7ywgsvyLZt26w+h0A5V65c6vXXX39dXnnlFdW0o169eqrpX4sWLdTn0DzQaMiQITJ+/HjZvn275M6dWzUdRKabiNxTimeYiYgoddWYUUMu372con1X0JQiMEug7Oi/w+HPIcubJUsWiYqKUsOPenp6yldffaXey58/v7z99tuWaREML1u2TH7//XepVauW5fXKlSvL+++/r/6PjPGnn36qAuh+/fqp10aMGCHTpk2Tffv2qfsCGK+G4r4BetCNNtRz586VLl26OKFEiMjVMGAmInIzCJYv3En7N4xC0wgEs/fu3ZMvv/xSdf7r1KmTei86OlrGjBmjAmTc/CoiIkIF1ZkyZbKaR6VKlSz/R9OMnDlzqmYaOjTJgKtXr1p9rm7dupb/58iRQ0qXLi2HDx9OtnUlorSNATMRkZtBpjclGTPMiZE5c2YpUaKE+v8PP/ygssXff/+99O3bV8aNGyeTJk2SiRMnqgAY0w4aNEgFzkY+Pj5Wz7Ecxtf0YU1jYmIeYw2JKL1LtYD5p59+kmeffVZ16jBCZTd79mzp0aNHai0aEVG6lphmEY8LgWhYWJj4+/urJhVJhc+iU9/gwYNVp72NGzeqUZaef/55y/egg165cuWcstxbtmyRQoUKqf+jUyHmXbZsWafMm4hcT6p1+uvdu7eEhobGef3OnTvqPSIiIiN01kOziq+//lpKliypRsxAR0A0lXjppZfkypUrTiswjPccEhKiOhRiTGi0e+7QoQN/ECI3lWoZZv0Sna3z589LQEBAqiwTERGlXWjDjOHgPv/8c9m9e7ecPHlSWrZsqdotY5QMBLT2EjFJgc6BAwcOlOPHj6th5RYsWBDnBltE5D5SPGCuWrWq5XbYxjv+6Z04Tp06pQarJyIi9/Xjjz/aff2dd95RD5g3b57pPDCmsq3Tp0/bTeDYql+/vsouExGlSsCsX9Las2ePygxgyCAdzt6LFCli6QVNREREROS2d/pDYNy1a9c4nf6IiIiIiNKSVOv017RpU7l27ZrlOe7ChCGBZsyYkVqLREREbg6300YTjWzZsqX2ohBRGpJqATOGBVq9erX6/+XLl6VZs2YqaH7vvfdU72QiIiIiIrcOmNGZQr99Ke7UhIHnMTzQrFmz4u3sQURERETkNgFzZGSkpf3yypUr5amnnlL/L1OmjFy6dCm1FouIiIiIKG0EzOXLl5fp06fL+vXr1eDz+lByFy9elJw5c6bWYhERERERpY2A+bPPPpNvvvlGdbDo1q2bVK5cWb0+f/58S1MNIiIiIiK3vdMfAuXr169LWFiYZM+e3fI67taEuzYREREREbl1hhkwdM/OnTtVpvnOnTuWm5cwYCYiIneDRBKGV00q3MUQd9HFjcFIVFkkdDdI2ztD4jO3b99m8VHaCZjPnDmjRsZo3769DBgwwDImM5pqvP3226m1WERElAb06tVLBS+2D72/S3oJchMDI0jp5eDp6SkFChSQ3r17y9WrVx+rnPU78KYFTz75pLz55pt21z2xY2NjAIHWrVs7celEPvzwQ6lSpYpT50muIdWaZAwcOFBq1Kghe/futerk17FjR+nXr19qLRYREaURCI5nzpxp9Zq73x3W399fjh49KjExMer4iYAZneWXLVuW2ouW5gQGBqb2IlA6kmoZZoyO8f7776smGEa4ZfaFCxdSa7GIiCiNQHCMoMf40Pu84PI5jh84lug+//xzyZMnj1y5csWS/X3ttdfUIyAgQHLlyiUffPCBag6oCw8PV1c18+fPL5kzZ5batWureRtt3LhRzQvNBfH9LVu2lFu3bqns7Nq1a2XSpEmWzC+aRej3GkB2M0uWLJI3b1554YUXVL8d3b179+Tll19WAXBQUJCMHz/eoTLBd6Ac8uXLp+b/xhtvqKFZHzx4EGfa6Oho6du3rxQtWlT8/PykdOnSalmN2dL//e9/8s8//1iWX1/3c+fOSZcuXVRWN0eOHOpqsL5usH37dmnevLkqU5Rto0aNZNeuXabNQ9DUwfgdjwvLXa1aNcmYMaMUK1ZMRo0aJVFRUfE2ycC9HpAdxvRI2OE9e01Y0FQU7+P3rlevnjpB0bPc+A6cqOjlxftGuI9UyzDj7Bg7s63z589L1qxZU2WZiIjcxuEJIkcmJDxdjmoijeZbv7b2KZGbj4KjeJUZLFJ6ULI2hUAgigDm5MmTKhj+448/VICqQ0CIoBF3kt2xY4fqWF6oUCHLlUwE04cOHZLZs2erIHTu3Lkqs71//34pWbKkCqaCg4OlT58+Ktj09vZWd6nF8QvPjx07JhUqVLDcoTZ37twqMGzatKm8+OKL8uWXX6pgdtiwYSoAXbVqlZpu6NChKhDH9yEAfvfdd1XAmdjL/QiEcTw1Boo6vI5mGygTXMlFwIj1R4COZcGJwuHDh1Xnez2Tj+AY90nASUHdunXVCQnW+eOPP1blsm/fPnWign5HPXv2lClTpqgTEAT8bdq0kePHj6fIMRzL1aNHD5k8ebI0aNBATpw4odYNRo4cGWd6rGO7du3UMv7666+qWWh8TWlwx2GsD35LnNTgt8dv9eyzz6oToaVLl6qTFMDJArkJLZV06dJF69evn/p/lixZtJMnT2p37tzRmjZtqvXq1cvp31e4cGGkFOI8Xn31VfX+gwcP1P9z5MihZc6cWXv66ae1y5cvW83jzJkzWps2bTQ/Pz8td+7c2ttvv61FRkZaTbN69WqtatWqWoYMGbTixYtrM2fOjLMsX331lVoeX19frVatWtrWrVsTtS6hoaFq2fHX2SIiIrR58+apv8Qy4bbi+vsP6rZDhw6pv1b2jtS0WZLwY2mduDPFa458du9ILTo6Wrt165b6mxg9e/bUvLy8VH1sfHzyySeWacLDw7UqVaqo40m5cuUsxxRdo0aNtLJly2oxMTGW14YNG6Ze0+t0fMeFCxesPhccHKwNHz5c/b9bt27aE088Ee9y4jsGDhxo9dro0aO1Fi1aWL127tw5VW8fPXpUHetwjMDxQS+XGzduqGOL7byMMH1AQIDl+bFjx7RSpUppNWrUUM9PnTqlvmP37t3xzmPAgAFap06drMq5ffv2VtP8/PPPWunSpa3KDWWN5Vu2bJnd+WI9smbNqi1YsCDeZcF2gNdwnIxvHihrHx+fOL87jpfGdcdvNGbMmDjLHRQUZHmO75o7d676/7Rp07ScOXNa7Qfffvut1TJiufB85cqVlmkWLVqkXtM/N3LkSK1y5cpakva5ZD5+U/JKtQwzzt5wBluuXDl5+PChPPfcc+rMFJd3fvvtN6d/Hy4fGTPaOEvE5aTOnTur5+hksGjRInUmjjNGZB2efvppdVYJ+Gzbtm1VJgBn6ehMgLNbHx8fGTNmjJrm1KlTahqckeIW3yEhISrDgLN5rCvMmTNHBg8erG7agkt/EydOVO/hkg8uJRIRpQgffxG//AlPlzG3/dcc+Sy+4zE0adJEpk2bZvUaMqA6ZDpR11aqVEkKFy6ssrm26tSpoy6d65A1xfEHdTqyyPhbqlQpq8+gmYbetwYZZv044ShkvJGFRnMMW8iEIuMcEREh1atXt1ovNJlISGhoqJovssc4dtavX1++++67eKf/+uuv5YcffpCzZ89avjehLDaW/99//42TKcb3YfkBzV7QrBLNK9DpEOV4//599T2PC/EA5m30999/W461+jLi+PzJJ59YXsMyYBmxHLajbeEYi+0EzTF08d3zAdPpcPwGrCOuTJD7SrWAGZeJsMEjgMTfu3fvqstm3bt3V5eYnA2XVow+/fRTKV68uGp3hQro+++/V5dpcBkNcHmqbNmysmXLFlXhLl++XF22w2UYXO5DhTN69Gh1mQ3twFBxIwhGWzG9LRo+v2HDBlWJ6wHzhAkT1KVAdNQAfAaBOiq0d955x+nrTURkV9nBsY+ksG2iYSYmJsk/ANoUlyhRwnQaJDDg5s2b6oHPOArHHS8vL9VmFX+N9GA3KccjzBeX/zHqky0EYAhGkwpBLJpuYJQMzMts+dDMBM0ucEzCiQI+O27cONm6dWuCy49gHicj8R1L0Rzjxo0bqlkKTlbQ3hzfgYAcsHxgbC+Oph6OQNLK9ne3TShhGdGeGIktW8agOCmQCNPpJ1s4QSH35p2qX+7trQJkPFISduhffvlFZXqxM6CyxI7crFkzyzRlypRRZ5ObN29WATP+Yhg8Y9s4BMGvvPKKHDx4UKpWraqmMc5Dn0ZvJ4XvxXcNHz7c8j4qFXwGnyUiIsch24mrg99++61KviCIQ1JDD9bANjhEEgRtkxEgo95GVhLZQ7SDjS/biKuFCM7sQbLEtj8OOqL99ddfqhM7jnO2kKxBUIbjQfny5dVr6ESI9tBI4pjBuiV0EqFDBhad1l599VXLa3qGOKHlR3kiSEWnxPjmPXXqVNUmWO8kaOzUqAfWuBqLcgZnjg+NZUTW2NGyQPYex31cPdBHWsGV58SyV17kHlItYMaZqX7JCzsaKjxcLsJZecOGDZP1u9EzFp0y0MMZLl++rHYC2zEeERzjPX0aY7Csv6+/ZzYNOhtg3VAhYkezN82RI0fiXV7s4HjoMD9AkO/oGbuj9Pk5e76ujGXCcnHlbQXLgiwfMmSpkSXTM4z6MiTmc7i8jiHTjBCAouke6tLnn39eWrRooQJl/K1cubJ88cUXVmP5o4kAgmp0CENmFp3UkGXFsiDYwuV/NK/DawjscE8AdMxDggRN7HAVEfNFcuSll15Sxwo0t0AzDSwHsqsIytHpEFlpNK3AtDimde3aVYYMGaJeQ1YZQSheR3MBdCQbMWKEGp0DxwA0QUAwbFZO+uuOvK+v308//SRLlixRVz8RMCJIxP/1abH8GJIOnf9wTEZ2t1u3bqo8MDIGrqDiijA6yaGDItYHz3HSgXkjcMUxCeWEbLe+/AhKkWzC1Vx8B05K9GYW8W2Lxmy07fu26455PfXUU1KwYEHp1KmTKjtcrUYCC1d/jZ/DA78FOvPhCi+WFdsFthX9e43LZPt/42tIpKH5JbYllAOy9rZDHWI6zBP7nu2Vi7RUN1AaD5jRZgxBMYJk7HC4ZISetxhiBxs8mi/8+eefyTqQOppfYDge9Ih2BWPHjrWb3UAzkeS6K+KKFSuSZb6ujGXCcnHFbQUBJvpe4BK2frk8Neh3c3UUAgsEcggojXDcwIgXGEIOQ5eh2QACNjTFwPED/UbQNAABL0aOwMgGaHaHPiMIXhD0InjSEw/oR4LA6a233lLZUASNGFIMmV5Mg7JD+1mMgoEAUB+SDDfYwPuYHzK4GCkDiREEbQiqEKQi2MRVRpQ7AjuMtoHfAVc2EfAhiYJjHQJt3MALTUowrb5stnACgUAsvvcxb8DxFNNgPVFW+IvvRGCJQB1ZeH0eKB9k0NGeF59fsGCBaheNv1h+fAavo/mHnv3GZ1FuuHqKssBvhBFK8HtgGY1l+/rrr0vNmjVV8K43oUAb4/jWAeyVge264zdG/IDtAA9s52iLjlFTjJ/Fb6I/R7NL/M4I8tF/Cv9HAI3tBNNgufRtVb9KgbLUyxbToO8Tfkc038R2hTbiOOmyXX5877p16+KMXqJ/B7keD/T8S8kvRKCKDRvtdX/++WdZuHChqlBw1g3YuXCZCpfNkgPOkjFeIypAnD0DsgnYAVB5GbPMOCtGhYDsBDIB8+fPt7qkhLNMzAtnmshMIDOOHRGVhA5toTEP7FjYiRDg2p4QIDuCjDfGlHQ0w4zKF5e/4rtcllQ4SOFgj0rB2I7LnbFMWC6uvK0g0ECCAs0DHrdtZ1LgEIMABJk4Y+e7lICgBtlhe50BU1tqlktaldJlgpMt9J3Csd+Zfaewz+HkAcdp230Ox29cmUBM4OzjN6WzDDMuByFARbswVGQzZsxQZ+f62RwCZpzFJxcEsGiXhUttOnRuwMENZ9k4mwa0jcIlG5zFAv6iNy4uK+mdD3BgxAaPM1V9msWLF1t9H6bR54FLefgufI8eMOPSDZ5jVI744HKPvbtbYZmT66CcnPN2VSwTlosrbitouqDfStnYtjel6Je09WVIaan1vWm9XNKi5C4TNCFBkgsZcVwJQH8ijEedmI6ijsCyYx3s1QNppV4gFwiYcclJv10lLkNhQ9Xv3AT4f2Iv3SVmZ0TAjIyusSMG2mzhLBOdANHWDEEwAncEunrwjvZxCIxxuQeXf9BeGZfUcBlND2YxnNxXX32lBqTHZS+cGPz+++9qFAwdvgPfj8tYuASGbDQu+eijZhAREZHz4biNq8X4iyYmaIduHJaOKM11+rO91JJSl6PQbgtZYwSztnDJDmeFyDCj+QOaiaAHsA5t39B8BJ05EEgj0Efgq9/dCdCRAsExmnBgqB10CMD4mPqQcnp7MXQq0XdaDE+HuwbZdgQkIqLH46xbMFP6gGQWHkQuEzBjdAo9K4u2PsjM6pdEjG11nQ1Z4viabKOdERrv4xEftGm2bXJh73atu3fvNp0GzS/MmmAQERERkRsHzMjKGmFYIFsY4oeIiJwnhft3E7kt7mvpU4oHzGhDTEREKUMfBxaj9CTHXVSJyJo+fKPtGMzk2lL1Tn9ERJS80MEZw1mi7wR66Kf0iAzobI0AAs3vOBoEyyW9bytYB+xr2Ofs3eWRXBd/TSKidAydqjEiAMaNxzj0qXF5GjdxQHab4w2zXNxhW0Gwj5vXuPI6UFwMmImI0jmMAY875KXGnf5wMxfc8Qw3duIYtCwXd9hWsL+5aoac4seAmYjIDeAAnhp3+kM7TtweGN/tykGQs7FcWCbkWngKRERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCYYMBMRERERmWDATERERERkggEzEREREZEJBsxERERERCbcKmC+cOGCPP/885IzZ07x8/OTihUryo4dOyzva5omI0aMkKCgIPV+s2bN5Pjx41bzuHnzpnTv3l38/f0lW7Zs0rdvX7l7967VNPv27ZMGDRpIxowZpWDBgvL555/HWZY//vhDypQpo6bBcixevDgZ15yIiIiIksptAuZbt27JE088IT4+PrJkyRI5dOiQjB8/XrJnz26ZBoHt5MmTZfr06bJ161bJnDmztGzZUh4+fGiZBsHywYMHZcWKFbJw4UJZt26d9O/f3/J+WFiYtGjRQgoXLiw7d+6UcePGyYcffigzZsywTLNp0ybp1q2bCrZ3794tHTp0UI8DBw6kYIkQERERkSO8xU189tlnKts7c+ZMy2tFixa1yi5PnDhR3n//fWnfvr167aeffpK8efPKvHnzpGvXrnL48GFZunSpbN++XWrUqKGmmTJlirRp00a++OILyZcvn8yaNUsiIiLkhx9+kAwZMkj58uVlz549MmHCBEtgPWnSJGnVqpUMGTJEPR89erQKwL/66isVrBMRERFR2uE2AfP8+fNVtrhz586ydu1ayZ8/v7z66qvSr18/9f6pU6fk8uXLqhmGLiAgQGrXri2bN29WATP+ohmGHiwDpvf09FQZ6Y4dO6ppGjZsqIJlHb4XATuy3MhoY5rBgwdbLR+mQWBuT3h4uHoYs9gQGRmpHs6kz8/Z83VlLBOWC7cV7j+sV1jXOvN4Qq7HbQLmkydPyrRp01Sg+u6776os8RtvvKEC2549e6pgGZBRNsJz/T38zZMnj9X73t7ekiNHDqtpjJlr4zzxHgJm/DX7Hltjx46VUaNGxXl9+fLlkilTJkkOyHgTy4TbCvcf1inJi3Wte5XJ/fv3U3sRKIncJmCOiYlRmeExY8ao51WrVlVthtEEAgFzWjZ8+HCrjDQyzGhegrbS6Hzo7LNfVFbNmzdX7b2JZcJthfsP6xTnY13rnmWiXyEm1+M2ATNGvihXrpzVa2XLlpW//vpL/T8wMFD9vXLlippWh+dVqlSxTHP16lWreURFRamRM/TP4y8+Y6Q/T2ga/X1bvr6+6mELFUpyVSrJOW9XxTJhuXBb4f7DeoV17eMeR8g1uc0oGRgh4+jRo1avHTt2TI1mAWhGgYA1JCTE6kwQbZPr1q2rnuPv7du31egXulWrVqnsNdo669Ng5AxjOyWcMZcuXdoyIgemMX6PPo3+PURERESUdrhNwPzmm2/Kli1bVJOMf//9V3799Vc11NuAAQPU+x4eHjJo0CD5+OOPVQfB/fv3S48ePdTIFxjyTc9IY3QLdBTctm2bbNy4UV577TXVIRDTwXPPPafaRWPIOAw/N2fOHDUqhrFJxcCBA9VoGxjW7siRI2rYOYwHjXkRERERUdriNk0yatasKXPnzlXtgT/66COVUcYwchhXWTd06FC5d++eGv4NmeT69eurwBY3F9Fh2DgEtsHBwWp0jE6dOqmxm40ja6AzHgLx6tWrS65cudTNUIxjNderV08F7BjCDh0QS5YsqUbIqFChQgqWCBERERE5wm0CZnjyySfVIz7IMiOYxiM+GBEDwa6ZSpUqyfr1602nwfB2eBARERFR2uY2TTKIiIiIiJKCATMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzESpKDommuVPRESUxnmn9gIQuZPwqHDZfH6zhJwMkZBTIRIZEynb+21P7cUiIiIiEx6apmlmE1DaExYWJgEBARL6S5D4Z0rgIkGOaiKN5lu/tvYpkZu77E6OjeHhw4eSMWNG8SgzWKTs4EdvRt4RWVjWsYVs9I9IjuqPnl9YKLLt5YQ/55NF5Mkj1q/tHiJy+reEP5u/rUitb6xfW1pD5MHlhD9b9XORIs89eh52VCQkOG6Z2Ptsq+0ifkGPnv87Q2T/R5bPRkZHSnj0Q3kYFS4R0eGi73HHIkWCL4hcffuq5M6cO/bFjd1Frq5NeHlL9BOpONL6tbkFxCH1fhHJ2/jR8ytrRDY979hnO563/DcyMlJOzOshpb3X2y8XozyNRJ6YJdHRIl5e/70W0lQk7FicSVE8MTEiXvqmXXGESIn+jyZ4cElkac14v0qVeaQIkvenioXIrejScv++iKenSEX/XyXHmaGPptVilwd/PY0r4Rco0mqH9Yy3vSRyYZHpauK7/42qKUU6/C4+Pj6P3lhYRiTyriSo1nSR/E8+en5zp8ja9uKQJw+L+GR99PzwBJEjExL+XAJ1BNYJvxseWCWrcnKgjjDuP/hdo6JifwvPxv+IV+5HdYR2fqF4bH/ZahvAV2FaPMdvpOG1DFnkZr0jsmePyJEjIoGBIjW8h0jgg9/EJ0Ps9B4pXEckyE4doe3/yLxeAf9SIsGrrF8zqSNU1aKJeHikfB2hBxIPWp5Xv8vduyIXL4oUvD1KKmb6Vq0jlsvDM3bfjNFitwX1mofImQeNZPq+n+TQoUuSNWs+CQjwlNGNm0rujMditxdsB/+tG/56ez/ady/mGCERhfqr7yxQQCSH3yXxWhF/HWElOETO3i4tEREihQqJZLj4q8juR3VEvJJYR4Tdj5GA5y9JaGio+Pv7O7aMlCYww+zKEDgkFKk8LGjntWsiDy7YnRyz81PzxsEvzOZdLd7PxREdYfP8gWOfjTQc8HURtxz7bPjNuK/hQOjIZ6PuWz+PibJ8zqpM7NEeNavA+ee12yclj+GzGf57ZPWw3uNCY0TK5Coj58LOPQqYw687trwRoXFfc/i3CZewMJHdu0UyZBDJcidcKjr42QsXYg9Q27eL3LnjIfmveEmZ/Al/du2K69K6uciDByI4RmTKJLJm6BUpnTfuZ1FMekwNkyfclWVnYj+HR4BPtHxeL/7v1MscnukUJYcMk/ZtfF++62f/O41Cw0TGvS9y9WpsGWF5exa/KeWzmq8r5nP7ZjX5+GNPuXwZJ7exj987X5TMGe5IQnbvfCCX98V+X9WqIv7Yjxz9XS0hy3+w/zrw2Yg7BeXmZZETJ0Tu3UNwK1Lv7jXJJY+2YW9907VpQbRqeZjMmyaWExJ/P02+qG39ncb9x0v/baNFGjSMkAOXY3/TO3dEWpd/ILNeuWB3G1DB1n//DwvNKrn/21103754S15sckEk3Hxd1664Kd9/JZI1q8jOnbHb8j+9L0ugf8LltGXDfTm3PTYQxDaRwytKWsc4+NsY6ggl8q54PLhgXq+gOtAC5NR/+QOcQNy4IVLi3HUJ+u+3MduOv58eKjP3xgaW+G1OnhQ5Pcax5R0zOly2nI3dDvGoni9cBpRLeNuHzJmtXx/fPVSeaGPzWb0eNJxTbjt6Xb75Br/6o6D+vSpXxL/Ahbhfgr8o0v+KddL4u/LlkkeT5c8RLeenOLaujRtFydo9j56/3ua+TO7u6D5n5xiU0D5n8ntT2saA2ZUhY+GXQIY5Y277r/nltzu5VTbVx/bs1yPez8XhlcHmuZ9jn0WG2VaG7I591jeH/SyAI7wzWT/39LZ8Z0IZ5sv3rsmK46tl5amVsurUKmkt52WEnUUBL08v8fX2FV8vXymRs6zsbLBRNm0Smb89NoNX5X4uyesR+7165kVlzP77Pw7wOHBeuBAgmw6LChyQZVm3TmRg4fxqGryv/Ze9wTz17Azg+YvtfGWp4QJDo7K+8surjv2uBa0SVN4y4umSUrpJwp89fy2XCpZBDyIvXM8rmX3sBP42DhzJIotXP3oemM1L3ihj/p0oB5RL9hzeUsw39iCODFJEdCY5fzPh5b18O1A++cT6teJ9ckhA1YQ/u2ZTafnoN2O4J3KuST7JkjHhDPMHX/jJot2x/8dv1qB8Bpn9Sn5LoKBnZ7FRYrvUf1tkfV/v5iG37ogcOhQb0L3U0F+618gfm5VDmfw3HbYBVUb/bRM7duaW9u2sl+OfwbmlWlHrdcX3YNsyWrjMX6YYApUsGT1kUMmEfxss/P2HGeT2bVEPCL3n59Bvc/dhbB0RECDyxBMiN2+KePhml0uh+S3rFp8jJ3PIzz9bv3a+XaAq14RM/i2T/Lbp0fNSQd5S8d1H+6qP93/7q9ej/U2/0tGlsZdcDYvdLy9dEnm9dRZ5vXF+ifrviguupqj99r99VHfsUl4J7mS9HL+8mksalU24nM5eDpCNG23W1YHyheWrfGXt4UfPT5b1lfaBDtb9SA5kFcmTRyRfPpHCJQLk5sPYz2IdUR5e3jb1m4hkD8wlw4ZFy6VLR+SJJ0pLaKi3ePrllZsPQ9WVhej/rjjoZRaFsv3vHFHzyqK2eV/f2BOa6Bgvh9f10hXrMOj67Ud1BOaZx84hNN5jC45BCR2rsDJyyaFlo7SFTTJcuUlGMlzSwWX2xYsXS5s2bawvKbsx2zK59eCWrDm9RrVBxuPIdZsmJAaZPLJLcc8mkudesNzd10yKZSspJ094qEuVCOCQzQtNOGZMFtmyieTIEXuZHbAsZ8/GHtQRiFesGJuVwgEeB0BkAfVL86VLiwQFxciNGzekefMcUquWl5oPguJz52Knx/wR1OCBAxkyXbh8jvXFd+nz0gN7vK8yyAGxnz9+PDbgQFYb360H2siC4rIrlk81EfCM/SyCYhzgcDDNn//RetnSTzpQ/jhgI4ucMWPs923YELu+eB/fjYM+pkcGVX9gWbCOp06JXL8eO098PnadNfHwuCXlymWTkiU9VcCgrxMyzkePxs4D5YS/WBfMC+uAeahmCB6x88XrKQm/V+HCscuB36tcOZGmTUUqVBApUSK2PJHh3LUrtikEthWsQ/bssVlIlFN4uMi//8YGLQULiuTKhe0kSk6d2iVPPVVNKlf2lixZYn9vbFsoB3wGz1FOV67E/t7Fi4tUqSJyDRfDHsQuG35b/L11679stv+jwFSHaTFffAfWAcuLcsdvieXHb4t9D9+DbRzri/lge8G88D4+j+/A74ztFPPEMmE58X8/v9jlxW+HdcV3OBu2YfwOWGd1gvFfsIltXN/2sW1iGbGuZcqIlC8f+ztgvbCf6OuKabB/FC0qkjNn7HtYfiw3trEzZ2LLGWWL78J2jHniOdYX66lv+/iN8T4eqDvwV3+vevVH2w9et/1tkuP4ozdv08sI8JthnbFf6/sZ1gUnVtiv8Pti+fCb47fEuqJcUD6YBmVx+nTs6yhrbPuucvym5MWA2QUxYE7h8r4fJhP/nih389yV1WdWy65LuyRGZQnsiPQTOdNA5FSwyMlgkctVkP4wnT8OfKi8Ubnj/yp7EhX7QOCBgzsOAnjgIISDIg5KCD5VBism9v84gFWqFBuk4EBQqlTsQQAHThwE8Hk8x2cxj6CguAc1BJH4fj2rbYYnV7G/Dw7OKEtnlgkCAT3wxAEfvwt+a7SxxAkHZo2DPrKzCO70BwIj/O747fB7Y3psFwh6ECghINBPLLB94TMIiBEcYJtISpCTkPS+neA3QOCJzD4CUTRtQXCN4Ay/RdmysfujXrY4ecLvGh4eJbt3b5TChZ+QDBm81fsI8vGb4YTUHaX3bQUYMLsuNskgt4bABAc4BJUHDsRmXe49iJLTETvkjFeIXPYLkXOySaLQOPKEnRnEeIlcqBUbHJ9sJnK+jki0rwpKGjWKDUT0NrDIVOBAiuCnXr3Ygyimq1w5/myo7bJCQoGsLQTTjsKykuPw++LhbPiNS5Z03vxs2/yS8yB7iQeyvokRGanJnTu3pU0bzaH9n4hSFwNmcgvI3uLSLLJ2K1fGXn5H5haX3i5eRBuEQyLFVooUCxEpvFYki22HR4MraKsQLN7ngqVSQEMpU9RfitQVKdItNsOL7JC9y8WPK7GBMhERETkHA2ZKl5DFRUe4BQtE1q+PvVyK7K5FwJnY4LhOiEixVSJZ4h9WKktkEfG/UUeqZWsntfMES6WqedWlU3Q4So7sIhEREaUtDJgpXUAwPG+eyIoVsZ2SDh+ObcNpkem6+FRdJRnLhIhWNETuZrDXviJW7ky5pWnRphJcNFiCiwVLwSwF0327OiIiIoofA2ZySejotndvbBOLkBCROXMeDU+lZLgr2WqulwINQuROrhA5E7FHItFu0M68smTIIo0KN7IEyBXyVBBPfcyt/zqiEBERkftiwEwuA53eECQjk/y//8W2P7bwipDc1bdKyZYhct0/RE6Gb5HbWpSoGNrmHio+nj5St2Dd2AC5aLDUyl9LfLyYOSYiIiL7GDBTmoWhlzBuLZpZbNkism1b7GgWikeMZCq2T3LXCpHowiFyPfM6uRZzT67hvYe2t1vxkKpBVS0Bcv1C9SVzBpvbURERERHFgwEzpRloXvH33yLHjsV22MNzfSg1dQ+sHCfEp06I5K4dIndyrpY7MdfljP62zbDIJXOUlGbFmqkAuXGRxpIzU86UXRkiIiJKNxgwU6rCnaiWLRP57bfYTLKVLJclU/lVkqVSiETkD5Hbcka1Qb5oJ0AOyhKk2h/rWeSCAQVTcC2IiIgoPWPATCkOdxz79VeRGTNEdu82vJExVEq3WCt+5ULkZrYQOfvwoNwXUQ9bAb4B0qRoE0uAXCZXGfHgQMVERESUDBgwU4pA0wqMh/zzz7HZZNyqV7wfihTZrJpY+JReKVe8dshRLTr2AzbtkDN6Z5QnCj5hGcmiWlA18fbk5ktERETJjxEHJbv9+0VGjhSZOy9aJGiXSNUQyVQhRCICN0iUPIztqAeW9sqihnWrma+mJUCuV7CeCpqJiIiIUhoDZkq2jPKaNZqM/e6orDiOu+mtFBm6RsQvdrBke80syuUuZ2li0ahII8mWMRt/HSIiIkp1DJjJqc7cOi+TF4bI7K0hctE3RKTURZFS9qctFFDIEiDjznpBWYP4axAREVGaw4CZHsvNBzdlzek1svLkSll4METOPTgW+0buuNPm9MupOuo1K9pMNbMonr04O+oRERFRmseAmRLlfuR92XB2g4ScDJGQUyGy69Iu0YyNjw38vDJJo6INLVnkyoGVrW45TUREROQKGDCTqcjoSNl+cbslQN58frNERNvca1oX7S2BUXXk+SeC5akKwVK7QG3J4JWBJUxEREQujQEzWdE0TU4/OC2Tt02WNWfXyNrTa+VOxJ34S+lyZZGTwRL4IFi+H9FQ2jTLwhIlIiKidIUBM1m8ufRN+XX/r3L1/lWRo/YLpli24hJwI1gOLAiWyGNNRO7nlt69Rb7+WsTPj4VJRERE6Q8DZrK4+fBmbLBskDdzXjWCRbNizaRgZLAMe7mw5e58DRqIfPGFSK1aLEQiIiJKvxgwkwU65s09PFfKZCwjXWt1lRYlW0j53OVFxENlkNu9LRIeLpIjh8h334l06CDCu1ETERFReseAmSyeLf+sdC7TWZYvXS5tarURHx8fuX1bpEcPkQULYqdp3To2WM6XjwVHRERE7oFjfJGFr7eveHs+OodauVKkcuXYYDlDBpFJk0QWLWKwTERERO6FGWaKIyZG5JNPPOWjj2JvcY1s8sKFIlWrsrCIiIjI/bhNhvnDDz9Ud5UzPsqUKWN5/+HDhzJgwADJmTOnZMmSRTp16iRXrlyxmsfZs2elbdu2kilTJsmTJ48MGTJEoqKirKZZs2aNVKtWTXx9faVEiRLy448/xlmWr7/+WooUKSIZM2aU2rVry7Zt2yQtePhQ5MABkbFja8moUV4qWO7XT+TwYQbLRERE5L7cJmCG8uXLy6VLlyyPDRs2WN578803ZcGCBfLHH3/I2rVr5eLFi/L0009b3o+OjlbBckREhGzatEn+97//qWB4xIgRlmlOnTqlpmnSpIns2bNHBg0aJC+++KIsW7bMMs2cOXNk8ODBMnLkSNm1a5dUrlxZWrZsKVevWo9OkRq2bhWpVs1Htm8PEl9fTb7/XmTGDBF//9ReMiIiIqLU41YBs7e3twQGBloeuXLlUq+HhobK999/LxMmTJCmTZtK9erVZebMmSow3rJli5pm+fLlcujQIfnll1+kSpUq0rp1axk9erTKFiOIhunTp0vRokVl/PjxUrZsWXnttdfkmWeekS+//NKyDPiOfv36Se/evaVcuXLqM8hY//DDD5LaPD1FMmbUpFChMFm9Olr69EntJSIiIiJKfW7Vhvn48eOSL18+1RSibt26MnbsWClUqJDs3LlTIiMjpVmzZpZp0VwD723evFnq1Kmj/lasWFHy5s1rmQaZ4VdeeUUOHjwoVatWVdMY56FPg0wzILDGdw0fPtzyvqenp/oMPhuf8PBw9dCFhYWpv1hmPJylTh2RGzciZcWK1VK5cnOJjNScNm9XppexM8s6PWC5sEy4nXD/YZ2StHqTXI/bBMxoK4wmFKVLl1bNMUaNGiUNGjSQAwcOyOXLlyVDhgySLVs2q88gOMZ7gL/GYFl/X3/PbBoEuA8ePJBbt26pph32pjly5Ei8y47AHstrC1lvZKeTw4oVK5Jlvq6MZcJy4bbC/Yf1Cuvax3H//v0UKEFKDm4TMKMJha5SpUoqgC5cuLD8/vvv4pfG7+mMjDTaPesQgBcsWFBatGgh/k5uYIyzXwSGzZs3V+MwE8uE2wr3H9Ypzse61j3LRL9CTK7HbQJmW8gmlypVSv7991+1c6K5xO3bt62yzBglA22dAX9tR7PQR9EwTmM7sgaeI6hFUO7l5aUe9qbR52EPRtzAwxYqlOSqVJJz3q6KZcJy4bbC/Yf1Cuvaxz2OkGtyq05/Rnfv3pUTJ05IUFCQ6uSHjTgkJMTy/tGjR9UwcmjrDPi7f/9+q9EscCaMYBid9/RpjPPQp9HngWYf+C7jNDExMeq5Pg0RERERpS1uEzC//fbbari406dPq9EvOnbsqLK93bp1k4CAAOnbt69q9rB69WrVMQ+jWCCIRYc/QPMHBMYvvPCC7N27Vw0V9/7776uxm/Xs78svvywnT56UoUOHqjbJU6dOVU0+MGSdDt/x7bffqmHpDh8+rDoN3rt3T30fEREREaU9btMk4/z58yo4vnHjhuTOnVvq16+vhozD/wFDv2HECtywBCNSYHQLBLw6BNcLFy5UAS4C6cyZM0vPnj3lI9wO7z8YUm7RokUqQJ40aZIUKFBAvvvuOzUv3bPPPivXrl1T4zejkyCGqFu6dGmcjoBERERElDa4TcA8e/Zs0/cx1BzGVMYjPugkuHjxYtP5NG7cWHbv3m06DcZnxoOIiIiI0j63aZJBRERERJQUDJiJiIiIiEwwYCYiIiIiMsGAmYiIiIjIBANmIiIiIiITDJiJiIiIiEwwYCYiIiIiMuE24zCnJ5qmqb9hYWFOn3dkZKTcv39fzZv3vGeZcFvh/sM6JXmwrnXPMtGP2/pxnFwHA2YXdOfOHfW3YMGCqb0oRERElITjeEBAAMvNhXhoPM1xOTExMXLx4kXJmjWreHh4OP3sF4H4uXPnxN/f36nzdlUsE5YLtxXuP6xXWNc6A0IuBMv58uUTT0+2inUlzDC7IOxkBQoUSNbvQLDMgJllwm2F+w/rlOTFutb9yoSZZdfE0xsiIiIiIhMMmImIiIiITDBgJiu+vr4ycuRI9ZdYJma4rbBMHMHthOXiKG4rlJax0x8RERERkQlmmImIiIiITDBgJiIiIiIywYCZiIiIiMgEA2YiIiIiIhMMmMni66+/liJFikjGjBmldu3asm3btnRbOmPHjpWaNWuquyXmyZNHOnToIEePHrWa5uHDhzJgwADJmTOnZMmSRTp16iRXrlyxmubs2bPStm1byZQpk5rPkCFDJCoqStKDTz/9VN1JctCgQeLuZXLhwgV5/vnn1Xr7+flJxYoVZceOHVZ37xoxYoQEBQWp95s1aybHjx+3msfNmzele/fu6oYM2bJlk759+8rdu3fFFUVHR8sHH3wgRYsWVetbvHhxGT16tCoHdyqTdevWSbt27dRd27CvzJs3z+p9Z5XBvn37pEGDBqpuxp1YP//8c3HFMomMjJRhw4ap/Sdz5sxqmh49eqg716bnMqF0ArfGJpo9e7aWIUMG7YcfftAOHjyo9evXT8uWLZt25cqVdFk4LVu21GbOnKkdOHBA27Nnj9amTRutUKFC2t27dy3TvPzyy1rBggW1kJAQbceOHVqdOnW0evXqWd6PiorSKlSooDVr1kzbvXu3tnjxYi1Xrlza8OHDNVe3bds2rUiRIlqlSpW0gQMHunWZ3Lx5UytcuLDWq1cvbevWrdrJkye1ZcuWaf/++69lmk8//VQLCAjQ5s2bp+3du1d76qmntKJFi2oPHjywTNOqVSutcuXK2pYtW7T169drJUqU0Lp166a5ok8++UTLmTOntnDhQu3UqVPaH3/8oWXJkkWbNGmSW5UJtu/33ntP+/vvv3GmoM2dO9fqfWeUQWhoqJY3b16te/fuqr767bffND8/P+2bb77RXK1Mbt++reqGOXPmaEeOHNE2b96s1apVS6tevbrVPNJbmVD6wICZFFRaAwYMsJRGdHS0li9fPm3s2LFuUUJXr15VlfvatWstFbuPj48KBHSHDx9W06CS1w8Mnp6e2uXLly3TTJs2TfP399fCw8M1V3Xnzh2tZMmS2ooVK7RGjRpZAmZ3LZNhw4Zp9evXj/f9mJgYLTAwUBs3bpzlNZSVr6+vOpDDoUOHVDlt377dMs2SJUs0Dw8P7cKFC5qradu2rdanTx+r155++mkVwLhrmdgGh84qg6lTp2rZs2e32n+wTZYuXVpL6+ydRNg7Ocd0Z86ccYsyIdfFJhkkERERsnPnTnW5UOfp6ameb9682S1KKDQ0VP3NkSOH+ovywOVDY5mUKVNGChUqZCkT/MWlxbx581qmadmypYSFhcnBgwfFVaHJBZpUGNfdnctk/vz5UqNGDencubNqYlK1alX59ttvLe+fOnVKLl++bFUuAQEBqlmTsVxwaRnz0WF67Gdbt24VV1OvXj0JCQmRY8eOqed79+6VDRs2SOvWrd22TGw5qwwwTcOGDSVDhgxW+xSakN26dUvSQ92LphsoB2CZUFrlndoLQKnv+vXrqk2iMcgBPD9y5IikdzExMaqd7hNPPCEVKlRQr+FAhwOUXokbywTv6dPYKzP9PVc0e/Zs2bVrl2zfvj3Oe+5aJidPnpRp06bJ4MGD5d1331Vl88Ybb6iy6Nmzp2W97K23sVwQbBt5e3urEzRXLJd33nlHnQThhMnLy0vVH5988olqdwruWCa2nFUG+Iu24rbz0N/Lnj27uCr0iUCb5m7duqn2yuDuZUJpFwNmcnvIqB44cEBlyNzZuXPnZODAgbJixQrVkYYenVAhAzhmzBj1HBlmbC/Tp09XAbM7+v3332XWrFny66+/Svny5WXPnj3qpBOduNy1TChxcLWqS5cuqmMkTkiJ0jo2ySDJlSuXyhLZjnaA54GBgem6hF577TVZuHChrF69WgoUKGB5HeuNpiq3b9+Ot0zw116Z6e+5GjS5uHr1qlSrVk1ldPBYu3atTJ48Wf0fGRx3KxPACAflypWzeq1s2bJqNBDjepntP/iLsjXCyCEYDcAVywUjnyDL3LVrV9UE54UXXpA333xTjT7jrmViy1llkB73KT1YPnPmjDpB17PL7lwmlPYxYCZ1abl69eqqTaIxq4bndevWTZclhKwGguW5c+fKqlWr4lzeQ3n4+PhYlQnaDCJI0ssEf/fv329VueuVv22A5QqCg4PV+iBbqD+QWcVldv3/7lYmgKY6tkMOou1u4cKF1f+x7eAgbSwXNFdAG1RjueBEAyclOmx32M/QptXV3L9/X7WzNcJJN9bHXcvElrPKANNgqDYEmcZ9qnTp0i7Z9EAPljG83sqVK9VQjUbuWCbkIlK71yGlnWHl0Hv7xx9/VL2U+/fvr4aVM452kJ688sorarinNWvWaJcuXbI87t+/bzWEGoaaW7VqlRpCrW7duuphO4RaixYt1NB0S5cu1XLnzu3SQ6jZMo6S4a5lgl783t7eaii148ePa7NmzdIyZcqk/fLLL1bDh2F/+eeff7R9+/Zp7du3tzt8WNWqVdXQdBs2bFAjkbjSEGpGPXv21PLnz28ZVg5DiGH4wKFDh7pVmWBEGQyfiAcOpxMmTFD/10d8cEYZYGQNDKH2wgsvqCHUUFdj+0urQ6iZlUlERIQaWq9AgQKqfjDWvcYRL9JbmVD6wICZLKZMmaKCIYzHjGHmMAZmeoWK3N4DYzPrcFB79dVX1fBFqIw7duyoKnaj06dPa61bt1ZjgCJgeOutt7TIyEgtvQbM7lomCxYsUCcCOKksU6aMNmPGDKv3MYTYBx98oA7imCY4OFg7evSo1TQ3btxQB32MV4xh9nr37q2CC1cUFhamtgvUFxkzZtSKFSumxt41Bj3uUCarV6+2W4/ghMKZZYAxnDG0IeaBExUE4q5YJji5iq/uxefSa5lQ+uCBf1I7y01ERERElFaxDTMRERERkQkGzEREREREJhgwExERERGZYMBMRERERGSCATMRERERkQkGzEREREREJhgwExERERGZYMBMRJRO/fjjj5ItW7bUXgwiIpfHgJmI3N7ly5dl4MCBUqJECcmYMaPkzZtXnnjiCZk2bZrcv3/fJcqnSJEiMnHiRKvXnn32WTl27FiqLRMRUXrhndoLQESUmk6ePKmCY2Rix4wZIxUrVhRfX1/Zv3+/zJgxQ/Lnzy9PPfVUqiwbbsQaHR0t3t5Jq6r9/PzUg4iIHg8zzETk1l599VUVkO7YsUO6dOkiZcuWlWLFikn79u1l0aJF0q5dOzXd7du35cUXX5TcuXOLv7+/NG3aVPbu3WuZz4cffihVqlSRn3/+WWV7AwICpGvXrnLnzh3LNDExMTJ27FgpWrSoCmQrV64sf/75p+X9NWvWiIeHhyxZskSqV6+uAvcNGzbIiRMn1PIg850lSxapWbOmrFy50vK5xo0by5kzZ+TNN99Un8cjviYZyJoXL15cMmTIIKVLl1bLa4TPfvfdd9KxY0fJlCmTlCxZUubPn58MJU9E5DoYMBOR27px44YsX75cBgwYIJkzZ7Y7jR58du7cWa5evaqC2Z07d0q1atUkODhYbt68aZkWge28efNk4cKF6rF27Vr59NNPLe8jWP7pp59k+vTpcvDgQRXgPv/882o6o3feeUd97vDhw1KpUiW5e/eutGnTRkJCQmT37t3SqlUrFcifPXtWTf/3339LgQIF5KOPPpJLly6phz1z585VTU/eeustOXDggLz00kvSu3dvWb16tdV0o0aNUicP+/btU9/bvXt3q/UkInI7GhGRm9qyZYuGavDvv/+2ej1nzpxa5syZ1WPo0KHa+vXrNX9/f+3hw4dW0xUvXlz75ptv1P9HjhypZcqUSQsLC7O8P2TIEK127drq//gs3t+0aZPVPPr27at169ZN/X/16tVqeebNm5fgspcvX16bMmWK5XnhwoW1L7/80mqamTNnagEBAZbn9erV0/r162c1TefOnbU2bdpYnuP733//fcvzu3fvqteWLFmS4DIREaVXbMNMRGRj27ZtqvkEMqvh4eGq6QWyvDlz5rSa7sGDByqrrENTjKxZs1qeBwUFqaw0/Pvvv6oDYfPmza3mERERIVWrVrV6rUaNGlbP8d1o8oEmIsgeR0VFqe/WM8yOQsa6f//+Vq+h/fakSZOsXkNWW4fMO5qg6OtBROSOGDATkdvCqBhocnH06FGr19GGGfQOcwhYEfyijbEtYxthHx8fq/cwbwTe+jwAQS86EhqhrbKRbfOQt99+W1asWCFffPGFWmYs1zPPPKOC7eRgth5ERO6IATMRuS1kjJHx/eqrr+T111+Ptx0z2itj6Dl0DkQWOSnKlSunAmNkhRs1apSoz27cuFF69eqlOuLpwffp06etpkEnPoyoYQYdGjGvnj17Ws0by0ZERPFjwExEbm3q1KmqWQKaQaDZA5ojeHp6yvbt2+XIkSNqtIpmzZpJ3bp1pUOHDvL5559LqVKl5OLFiypbjCDWtgmFPWiqgUwxOvohW1u/fn0JDQ1VASuaPBiDWFsYqQId+9DRD9neDz74IE7GF4H8unXr1MgcCMxz5coVZz5DhgxRnfnQBATrtGDBAjVf44gbREQUFwNmInJrGGINI09gDObhw4fL+fPnVcCJrCsCXAw7hyB18eLF8t5776lRJa5duyaBgYHSsGFDNdSbo0aPHq2GpcNoGRj/Gc05kL1+9913TT83YcIE6dOnj9SrV08FwsOGDZOwsDCraTBCBka9wPqg3XVs/z1rCPjRXhlNOzBaBoa3mzlzphqWjoiI4ueBnn8m7xMRERERuTWOw0xEREREZIIBMxERERGRCQbMREREREQMmImIiIiIkoYZZiIiIiIiEwyYiYiIiIhMMGAmIiIiIjLBgJmIiIiIyAQDZiIiIiIiEwyYiYiIiIhMMGAmIiIiIjLBgJmIiIiISOL3f6el8r4t+s6rAAAAAElFTkSuQmCC",
      "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",
    "# Evolution battles against different degrees of complexity,\n",
    "# which are given by the lengths of target sequences.\n",
    "# Every Evolutionary 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 oneEvolutionaryProcessk4( 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 Evolutionary 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 = oneEvolutionaryProcessk4( 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": [
    "*Our Observations.*\n",
    "\n",
    "Without recombination:\n",
    "> 1. The graphs show that the slope of the ramp decreases as the complexity increases. But the graph of the slope vs length of target sequence is an increasing function. The solution to this contradiction is: if we normalize the slope of the ramp, dividing by the length of the target string, we see a decreasing function. Now, all graphs have the same measure, so they are normalized. And so, one sees a decreasing tendency. \n",
    "> 2. A power model fits the function of the slope of the ramp vs length of the target sequence: $y = 0.0346 * x^{0.5112} \\sim 0.03 * \\sqrt{x}$. The variability is low. \n",
    "> 3. The intersects grows linearly with the length of the target string: $ y = \\frac{1}{2} x + 200$. The  variability in the slope is low and high in the independent term. \n",
    "\n",
    "With recombination rate = 0.01:\n",
    "> 1. A power model fits the function of the slope of the ramp vs length of the target sequence: $y = y = 0.0483 * x^{0.4817} \\sim 0.04 * \\sqrt{x}$.\n",
    "> 2. The intersects grows linearly with the length of the target string: $ y = 0.5126 * x + 176$.\n",
    "> 3. The generation at which the cross-point of the ramp with the plateau has a fuzzy tendency to grow proportionally to the square root of the length of the target sequence. \n",
    "\n",
    "*Our interpretation.*\n",
    "1. The role of recombination is, in our opinion, insignificant.\n",
    "2. The Y-intersect of the ramp is 200 units above that predicted by randomness, which is the half of the length of the target string. Thus, 200 is the initial effect of MAX for a population size of 30.\n",
    "3. The generation at which the cross-point of the ramp with the plateau happens is erratic, dancing around 200. \n",
    "4. Squared roots appear frequently in these fitting models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de85184f",
   "metadata": {},
   "source": [
    "# THE EFFECT OF RESOURCES\n",
    "\n",
    "We have seen how Evolution behaves under the pressure of complexity: if the pressure is low, Evolution functions swiftly. If the pressure augments, Evolution is damped until finally its work is undistinguishable from that of randomness. \n",
    "\n",
    "Let us research now the effect of the resources. One expects that if the population sizes grows, the plateau is achieved faster and is higher. Now, we ask: Are the tendencies described by a power model with a square root somewhere? \n",
    "\n",
    "**Example.** Let us plot the tendency of the number of solved sites when the length of the target string is 10000, and the population size takes on varying values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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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",
      "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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",
      "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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xdPcvNN1VJWvjxo2p76lUXftT6DaOdp28NNcdzNB9Pj3KN5o+8m6pSphCjx+hJZe686j8HXnHXyXZkaX7sd5mGa2fjmeRd/mUj7QPhLbZzKjaauSxQKVjmvbhhx8Om+7CCy90JZ6h6xXtumY1T2VUgqK7wCql8ng1LXSszuqxSqVnoa6//nr3vr4Ti5LHtPLn//3f/4Xdlc2o2mpav5/ZdYzmOJoR7RdplcBFiixBiKTaIZpPaD6Ndp/I6DfTO/6GPtK7Gx95vlWJo3f+Ci3xDqVtpX0zkkowNL3aLobug6+//voB06oGgz7z8kC088yMtPJOrK+LdP2jUrPI6zO1f4pMd782j/pMJVEelXDquKaaFtHyak5dffXVYe/feeed7v0pU6akvqdli3W11cj8qvblqiWi66pQ+r7218iSZrUD0zF3wYIFqe9pP1dJZuixJVbXoxnRMvjVqlKVSeUHteUNpRohyhfe+0oHHbN07AqtUae2b/qNrJQ8Kl8qz6rUMTSPahuo1Lp169aZPhaqZFLbRe9Hnme938hM2me0X0bzSGtfyWrJoz6LXH/55JNP3G+pJljo9WlaJccqjVXNvuycp5+oe1vV3UdJ666hSjh0p9J7PPfcc+59RfW6S6C7DF6JkB66+607BStXrnR3UELpDn3onUbdrVfvi6tXr3avdedGd1u6du2aOj891MWzSlh01z6zdAdCpQi6Y5fW3Um/oS1UgqS77lomj7d8KjnIyGeffebuaFxzzTWp7+nugVd651m7dq27U6w7gLpD5FFPcK1btw67e+pRqUMsRM5H20Tb0MsTnpYtW4bd6fFKqHW3XCUjke/rjlesqTRBeST0oTt1WdGqVauwUliltUr3vOXWuUd3iZVv9Dw0Pyp/626819uV7vqpRGPevHnp/p6m0Z013aGMlvYN9VanO9YqAfFUrFjRlWjqzr23nS6++GJXOqDSKo++q/1Jn2V2nTy686fS9qweQ4YPHx52/Ai9s//uu++6u+IqkQ9dFt3RlMj9PZbbLKP10x1o7y6ftoH2B61XjRo10u3hzI/2YR3HVDIQSnfttawqCcrMumY1T/lRXlOJSejdTe3T3jEoK8eqyOOd7rBLWtNmRej2885FOo7pTvXy5cszPb9YHI/TO45mtA66+66epVUqmxUqFVetj44dO9r999+f5X0i0pNPPnnAcTeth2pGRFNKrPOpX4msSnrTKglS6ab3eej/aKeNZrqDkR3XRZMnT3a9JaoWQlr7UWaohoJXS0J0TNZxLTPnay//q6Q/8lgmn3zyiWWn0P1d+4rysNYprXys2iJa58jrsqZNm4YNG6L9XCWZobLjejSSV7Koa0kdr9Kic7quY5WnQpdD1z6qfeYtxzfffONK4XUsCq0hpOOY9zuZpeOg8qyuN5SHvd/evn27uyacNm2aW7bMHAt1Pa7v6HousjTN2w8ONu2VNtEcs/Ro0KCBxcrOGB23Qo9F2TFPP1FXW/Uu/iOrv4iqa+kgqKovoVXSVP1BJyMVpeuRFmXk0AsRdRsbSlU1xDtZKpOKd/EYSRdPmaXifWXaunXrWla8+eabrlqDLt61zt6GUCClqquR1SRC6eCvC30NdxJK1YMjpxMdxCPp4loHFu2sWg6PqgXEQkbbJDS9I6fzDkaRVSG89zO6AFJVjcgqBTqJ6cCQERXN66I6FiLXx1t3b7mVb3TwUrUAPdLL33L33Xe7am660Na2VbULHWxVdcmjKkcKVJReqv7Rvn17u+KKK8KCwkhaBp1Q0ssXOgD/9ttvrrqRDoAKwlTVSVWSRM9VVcfbnzKzTpnNZ6HHkNATlW4uePueLi5Ch+rQ/q5qTqHViTNallhus4zWT+mqqlGqjq2hi0KX2asKmFnax1V9JPRGS2iVMu8YEO26ZjVP+dHFSCQN26SqX1k9VkXOU0GxLhpiNZyAqgkqWNJFe2SwpgvLzMrKOkZ7HE2PTvgKqLSPqHqwqpmqWYC2ZzQ3yLTeqq6v862qA3sXYVnZJyIpb8WCllHVBFVV268KnQIEVeWL5FXT9AII73+000Yz3cHIjusiLz9GXjco4PGmjVY0xxU/Wh7tv5HLo3yqG1qRx7JYU5XOhx9+2AU2odszrYKAtI7vWj4Fj5Ei1yc7rkfTWj4F4U899ZS7nlSgpWqmutb2zqNaDuWptI7N4lW59NI9cjp9ntVzgpcGaTW1Cj3GhuZDv2PhTz/95PJPZFAfy7TXNXqsrhUzI1bHrdBjUXbMM2bBozKpgpwlS5Yc8JlXkhR5ovfuNqitkNeeym9nTC848NrsefNUXee0TpjJyZlqxnnQtFxqN6cLhbQyuk4CuliOpp1HrMXiRBfNNvGbLtrvR47BqDYHoXSRfigHVY82L+ognt6BUyUR3gXlihUr3ElNdzV1p1+Bh+6sqW6/6K6hTgxqD6ISQbXF0sWi7iqqPUEsqIRRbSN0h05Bitqo6s6dt99kZp0ym88UuIqOIaFBsy4SvQtFnURC26xqeXRDQCfOtEReYMZym2W0fmpHrAs/leKoTaTXrlZtSCLvsmaXaParQ5GnskPkRV56tT+iGRNUgZFKF3QxobYzCkx14aBSCN3USaTt5Uf5SyWEujOvAFV5UO2GFBSfcMIJGX5XpQsqgVb7stALq6zsE5F0o083/PxoX8qohENjOWo+Ok551xOqseFdWOo93WBRqYmuR1S6oPQLzR8qFRZNJ5ou9P1Qek/7rncnPtp5HozsuC6KpVj+ll+treygNnYKrtTWT+dYbVMFR2qDp3FcY3mddKiuR1Wyr/1X/WHoOK7aKV77cLVV03IorVU7Ja3tl5Xrz2iPuV4a6NwSWlKb0e/HIo8dbNprPdIa5zUtOkb49XsSLeXH9I5F6R23Iq9z9F5oO9LsmKefTOVslaCpYatOPtH8iHcnQzturCJ8r5qWGtf6zTPaA5dKNXQyTSsw9qMOSXRy00VJZCN7nexU3UQn+vQ6CalWrZo7Wan0KLT00SvBDJ1OFIBEUrUrlR6F3uVO9AO8H5WSqbpAqKxWP82u9Va+UQCmg1A0+VvbRxdFeugCSaUACuR0p92rXqCdW1WP9NCNBzWC1jTpXehrGZRv0ssXCmhCDxL6bQWrCl5VeqE7/ZdcckmW1ykzVEry2GOPubunocGj3/6ujh9U/SUW+TRW66dOaHRzQ50PRAYqXudLkpll1j6u0mnV4ggtffSqVXrHgMzKbJ7y493xDaUG+d6NnawcqzTP0BIAHf90ceDN07szrfQNFU0Jhqp5qkqUAmZdUIbejIoU7fbKzuNxNPuESh/1ULrpgk0Xl6oBkx7tdzoPKQ28mzix3Cd0LNO50I+CU1VLTY86/tF5M62OWXTDRg916qB11kPXI6qZEHrjVtW0xbuQVQme1lFV9iLpWib0gjfaeR6M7Lgu8vKj9pvQ/Uj5PrLE8FCc77U82n+VP0Ovi1Q7TftwVo9l0ayHzm06n+rmSmj1PAWP0dLyRV6DSeR72XE9mh7dRNVDNSh0c13nUDX5UAmrlkOBl7a9aoGkx0t3bZfQEjs1Z9HxMLR6ZrTHXC8NdA0dy+t85R9Vs09vn8tM2qdFNbKirTWla3Q1z4uFhg0buhscWr/QKrk6xuhaztt+3nrruBUab+kGoGIOr4PL7Jqnn6jbPIraK2hBdLddBwG/uwbaqEpwVWtNKyqONuoPpTt1yqQ6iaTVu1voPL2Td2Tmj6TEVjse9QaZ1gkmo7shXpVVVbNRr2KhD7VjVPUAXSxntD5aD/XG6VEG8NqNhl4AasOr97LQ9VHAqztRqo6WVVp+vzQ61HTg0gEh9OEFWLESbf5Ij+6eqcqlTlZp3XgIzYs6iYfSXSxdnChvafvrwi2y+pz2H90xSquKQegyqAqs7kiGlvxr/9RdVrUfDC1l0IlcJyBVV9VD+Sr0gjoz65RZOtmpPZiqxml50xK5r6nkTO1/QvcPj+rnq8Q/M2K1fppP5LKqfWZkW6XM5DHtw8oHzz777AE9Y+rCI7PBXlbzlB8FIaHrqQtwnaS85cvKsSryeKdeAsWbp/KwAjK1nwmlkgU/3l3u0O2lmzdpfVfbK5pqrNl5PE6PbjCG9pzpXUAp8Mtoe+qGhC441ZusznPZsU/Eqs2jSlRUSh760PWDqORFr70LPrXbVAAWuh21jXVBrYAxtNdOrZ9qfeiC0aN2grrpoV44PZmZZ1Zlx3WRbq6ppEU9TYaKPJbE4rwXDS//q1fYUF4Nkoya8kQrvfVQftbxMrSETOfGyJ65M6LrstmzZ7tqr6Gl65HXctlxPRpJN3jVnjWUzuG6bvX2e9280XrrxnDkeUmvvesP9emhGynKz6E1BXRDJ3K5vOAs9JirNI2s2q4q65pWtQbSataWlfys45TWT4UykTVDvPXLTNonUpvHCy+80F2fhfY9odpWun5QrRLvhoduoOlGn9I7NC9rH1f+1nyyc54xLXlUIKQLUlVzU1sPNR5Wompj6q6FPtMGVzF66EWBLmCV2RVM6a6bVlI7piJdlSpkhjKLVvTyyy93d9BVaqKdQXcs1QhbF6jeAdNrh6ETkjKadq7QUpZQyoA66at6kzckgA7sSnx1OuJ1cxxKO65OuLogTi+wUfUJtY3SHX+dNNLaSXQHQHeRdVdLG1ZVCb32fqF3q1QtQBdTqouvNmte1/CqBqQujbNK6aQLDB3YdWGpk3NmhmPJqbz8oYsq5QtdNGhHy0yJge7o666U0kv5WwGhtp2qxClNve2oAE8HK+VPlfjpzrbyqU6iuvjTgVv7jXZe7VOq5qHvq4MdXZhlRHcedYDTfqbSJV1E6MJE+VNt3iKp9FHVZZVnlY8iG6RHu05ZoZst7dq1c/leeVk3BXSjYN26dW7eOlGFBknaz9WWTg3stUxKPx30VLqj93V3Oa1OrjISi/VTKapObFdddZW7oFQHH7qwiGw3opOqjh06WWs7K2/pd9O646m8p9JM5Udd7Cgf6JikQFvVFSOH0PGjEsxo8pRK5vS76mI9muOIqtQpr2loIeUxXSCqnWdoYJDZY5XOHzpWKm/o3KB8ojbBoSdtdT+vbaf/2ubKK7r496PtozymUi+dC3RMVVWntG4K6pigmypqY3TiiSe6NNN2SUt2HY/To3VVkKAbKsqz2s8VTOl8mt55TXS+1jlS5+/I0kmdu3Q8Oth9IlZtHnVOjxxCxrsppguf0OBXeVv7hbaDLh61vRQg6A689sXQqnEalkLncuVzDZWki1x9T9cl2oezMk9dcOu7KtFSYJsZsb4u0jbUemm/9vYjzUPVGHXTJfQ6Qjc9tB6qvq4bJbqwVClUWtcnWaX9VvubLlK9auPe8CvahpFNUrIives7nVN1LaM00DFE115Kbx231LlhNHQs076i/UOdDnlDdaitnvYFLz2z63o0lKqkaxgR3eRQCZICSR2/vJs+onODrgNUi0n7i9JY5xsdV3WM0DWtqknrGkfTaagObXNdC2ga5eHIc5f2N7Wr1jy1zqq6qSFPIgNZXT8obXQs1He0T+hGi24w6piiNPKG6YqWtpXOg2oSomYXCo6VT3Xu0jWqquxmJu0PRZvHjz/+OHW/1bFDeU1pLdonvar/Oh8rXZVOKlnV/qmbVbqu8ZoweXQc0nd1/aj10809rZPOgaEl+tkxT1+BLFD38L169Qoce+yxbpBRdd1es2ZNN/h36CDZHg0hoK7LNWSDhgrQAKfnnHNO4L333jugW+DIoTLSG5BUr9U1sLrk1TJoAHENNPrNN9+kTqMBoNXlsgbGVrfLoasb2b27rF692i2nplfX1OqyW90rh3ZpHMobZiOjgZSnTp3qphk6dGi6A4NreIJLL73UDa2g9dF6aAgCfW/06NFh037xxReBU0891aW5Blc999xz0x2UWvONZqiO5cuXuyEdNM/Qbr3Tm09aXTjrdeSQAOl1se5t04y6ns6saOaZ1jbXoKjKj+oWOnJYj7SGOAgdcNmzfv16N22VKlVc/lY+V1fs6v46dNgSpbG6o1beUn5VN/EaAkeUx/S6QYMGLh+o62U9f/7556Na/2+//dbtDxokV8MPnHnmmYFZs2alOa26wfa6oI4cxDcz65TV7aihOTQ0jQZxVh5WV+Kav44Jo0aNChu43ete/PHHH3dDnSjtNPixBtTu169favplxzbLaP3Uxb66rvcG5NY+OXv27DSHSdDA394A1KFd9qd1LFD34+qGv1KlSm651AW69p/I4YKiWddo89THH3/s5qfhTzISuj8/+eSTLu20PTRwszekRlaPVXpfQ5JoObV9b7zxRpdPQqnrdw0poWOkprvooovcMALRDNWh46m6IdeyKG179+7tBpuOPLds27bNHYvVBbw+87ZPekOFHMzxOL2u8NOjYTS0zXWu1bZUOpx88slu6I1QkXkw2i7oo9kn4iG984ioG/9HH33UbScNXaNjhAZ8T4sGDm/Tpo07Pmr7amgtdeuf1XlqSJzQbvD9lj8y78T6ukjHzQceeMDNT/lRw1IsW7bMnXN0bRbqpZdectc3Gt4hdD5aZw1XktnhX9KiYcp0jNZwDVo/5SsNFxU6PMnBDNWR0fWdrsl07NTxSfuL0jGta5/0jqPeEBk6tmkelStXDgwcODAwbNgw953IfHOw16MZ+fnnn90wDJqn5q3hQnR+17EnrWvS0047zaWnHlp3rd+KFSvCptM5QNtF69akSRM3NEta21h5VMPEaDoNQq8h6CZNmpTmNbnS64ILLki9xlFe0jF68uTJWT4Wvvrqq4ETTjgh9byv5dPvZzbtD4Xu3bune5yN3Pc1XJrOZUorHY+0XmkNEyhjx451Q6t4+fD+++9310SRsmOeGUnSn4OJtpE9dLdTAwWr1DPa9mEAkFm6y65Ov1TzIa0uvD26o60SU9251F3sWFAJne6MqopRaFtRINGpBFj7hErUEpVK/VTqrhIQleTg4KhUWrV6VHLt1/N7TuO16VNNFMDPoe2aFGlSdafQHr9U1KzqTyqWj6zCAwCxpKpF6rUzo8ARwH90z10X2Rl1UhTv64jQNoex6uwjL4lMT7UbVHVRVTfObYEjkFkEjwlAdep1oFLbGbUhUqNX9aaldpixGm4DANKidiSIL7U98xugOda9TSPr1ObNb+zLQ01tddUOU53VqK2uai2pRoHaNsWy9pLapx/McCw5ZV/Q9ZiCbrUDU3tU9aytzmvSG5szK/yGuFGQmt4Yx0A8ETwmADVcVkN39Qan3vTUWFglj2okDQDI3dTZiToTyQgtTJARdcihTpTUSZqCHK8THa/TjljxxorL6nAsOWVfUBCuIZnU6Y9uFqgWmALI0N7JD5bfEDcaWiNy/HQgEdDmEQCAOFIPeRprKyOxHncVyAr1vpsR9YYZOkZmZuWlfWH+/PkHjMMZWYpLnxdIRASPAAAAAABf4QO8AQAAAACQBto8AlmUkpLiqtdoMN7QQZgBAEDiUrvJrVu3umq2GugeQPQIHoEsUuBYpUoV0g8AgBzot99+s8qVK8d7MYAcheARyCKVOHonH43JGSt79+61iRMnui7WCxQowPbJRqT1oUE6k865Cfk556ezeqTVzV/vPA4gegSPQBZ5VVUVOMY6eCxatKibJ8Fj9iKtDw3SmXTOTcjPuSedaXICZB4VvQEAAAAAvggeAQAAAAC+CB4BAAAAAL4IHgEAAAAAvggeAQAAAAC+CB4BAAAAAL4IHgEAAAAAvggeAQAAAAC+CB4BAAAAAL4IHgEAAAAAvggecUhNmzbNzj33XKtUqZIlJSXZuHHjwj4PBALWt29fq1ixohUpUsRatWplK1euDJtm48aN1q1bNytZsqSVLl3aevbsadu2bQubZtGiRda8eXMrXLiwValSxQYNGnTAsrz77rtWs2ZNN029evXs008/zaa1BgAAAHI+gkccUtu3b7cGDRrYc889l+bnCvKGDRtmw4cPtzlz5lixYsWsbdu2tmvXrtRpFDguXbrUJk2aZOPHj3cB6bXXXpv6+ZYtW6xNmzZWrVo1mz9/vg0ePNgeeughGzFiROo0s2bNsq5du7rAc8GCBdapUyf3WLJkSTanAAAAAJAzJcd7AZC3nH322e6RFpU6DhkyxO6//37r2LGje+/111+38uXLuxLKSy65xJYtW2afffaZzZs3z5o0aeKmeeaZZ6x9+/b2xBNPuBLNUaNG2Z49e+zVV1+1ggULWp06dWzhwoX21FNPpQaZQ4cOtXbt2tldd93lXg8YMMAFo88++6wLXAEAAACEI3hEwli1apWtW7fOVVX1lCpVyk4++WSbPXu2Cx71X1VVvcBRNH2+fPlcSeX555/vpmnRooULHD0qvXz88cftn3/+sTJlyrhpbr/99rDf1zSR1WhD7d692z1CSzglULOmBfJlXIgfaNjQ9o8dG/Ze/vPPt6SFCw+YNn8gYG1277b8hQpZICnJUm65xVJuvfW/CbZuteT69S0a+95/36xRo9TXSZ98YvlvvNH/i8WK2b6IUth899xj+caM8f1qytlnW8rzz4ev0ymnWNL69b7f3f/ooxbo2vW/N1assOR27aJb11mzzCpWTH2d9PLLlv+RRzL8jtK62eGH297WrcPfv+IKS5o+3fc3U3r0sJQHHgh7L7l69aiWd//IkRY4/fT/lverryz/lVdG9d19q1aFvc43YIDle/VV3+8Fmje3/a+/HvZe/jZtLCmianiay3vffRa4+ur/3li71pKbNYtuecePd//37t3r/ie9/bblv/de/+UtX972f/112Hv5rr/e8k2Y4PvdlIsvtpTHHgt7L7luXVV/8P3u/meftUCHDv+98e23lty5s0Vj36JFZiVK/Le8Q4ZYvqFDfb+XmWNEJO8Y4aXv3o0bLblx4+iWl2OE/7Y57jjbP3Fi6mulc6OnnrL811/vjtEZbhuOEdHlw88+M6tRI/W1O0b06RN2LozlMSKQkhLVcgE4EMEjEoYCR1FJYyi99j7T/3LlyoV9npycbIcddljYNNUjLuC9eeozBY/6n9HvpGXgwIHWr1+/A95PWrvWMr58MPuneHGbHtGmsvmPP9phf/xx4PzMrEjI65Xz59uKkO8m79hhHdL4XlpmT51q/4SsU6VZs+zEKL67t0iRA9qANly0yKpF8d11339v30R8t83q1Vbk7799v7t47lz7tVSp1Nclfv3VzopyXadMmmS7ypZNfX3MvHlW1+e7SutC+fO7UudQTZcvt3JR/O6q776zpRHr2jHK5Z07Y4b9GRLIHLFggTWL8ruR26bOd9/ZsVF898/ly212xHfP/OknKxnFd5fPm2c/VaqU+rrwX39Z22jzoQLxqlVT07nq3Ll2QhTfVXX1iRHL2+T77+3IKL77+6JFtjDiu+1/+80K7Nzp+90Fs2bZmpCL1TLLl1uLKNd14uef276iRVNf15g/32pG8d3MHCMiRR4jvpwyhWNEjI4RsjVfPvsyYts03bLF8q1Z4/tdjhHR7TfTv/zStv70U+prd4xYsybsXBjLY4TfORtA+ggegSj16dMnrLRSJY/qjCdQsaJvyWPpY491VWtD5X/pJQtEdPTjVd9VCWehQoVcp0LHNW5sx4R+d+tWCxx5ZFTL3PSMM8JLHgOBqL6bXKzYAcubb9o0Cyxb5vvdCrVrH7iu1apZoHBh3+/WO+kkqxv63RUrol7Xs1R6GFryuGaNBUJKC9Li0rp0aWvdurUVKFDgv+UdPdoCf/7p+5vVGzSwahHrGu3ynnTaaeElj8WKRf3dA7bN/PkWmD/f93tla9Y8cNsMGRLVXfiaJ55oNUK/u3Zt9PmweXObuHp1ajonbd5sgQxK+T2Fypc/cF3Hj7fA6tW+361cv75VivhusvbXKEoeT2jWzBqGfrdChajXtU3btuEljz/8YIEZM3y/l5ljRCTvGKESMQXoZ551FseIGB0jpPgxx4RtG6Xz3089ZSn/dvyWEY4R0e03zc88M7zkcfNmSxk7NuxcGMtjhDvmrV0b1bIBCJcU0NUTEAc6GYwdO9Z1VCM///yzHXPMMa4Dm4YNG6ZOd/rpp7vXaqeodox33HGHq37q2bdvn+sxVb2nqtrqFVdc4QK70CqoX375pZ111lmup1aVPFatWtUFgreGVAd98MEH3Xe+++67qJZfv6FqtZs3b3Y9v8aKLkxUsqQTYmhAg9gjrQ8N0pl0zk3Izzk/nbPr/A3kBfS2ioShqqYVKlSwyZMnhx3g1ZaxadOm7rX+b9q0yfWi6pkyZYqlpKS4tpHeNOqB1Wv/I7obX6NGDRc4etOE/o43jfc7AAAAAMIRPOKQ0niM6vlUD6+THD3/9ddfXUmkSgIffvhh++ijj2zx4sWuFFE9qHqlk7Vq1XK9pF5zzTU2d+5cmzlzpt14442uMx1NJ5deeqnrLEfDcGhIjzFjxrhSy9Aqp7fccovrtfXJJ5+05cuXu6E8vvnmGzcvAAAAAAeizSMOKQVoZ6ptw7+8gK579+42cuRI6927txsLUkNqqITxtNNOc0GeqqV6NBSHgryWLVu6XlY7d+7sxob0qCrKxIkT7YYbbrDGjRtb2bJlrW/fvmFjQTZr1szeeustNyzIvffea8cdd5yrslpXvTECAAAAOADBIw6pM844w3VSkh6VPvbv39890qOeVRX4ZaR+/fo23WeohS5durgHAAAAAH9UWwUAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4IngEAAAAAPgieAQAAAAA+CJ4BAAAAAD4InhEwtm6davdeuutVq1aNStSpIg1a9bM5s2bl/p5IBCwvn37WsWKFd3nrVq1spUrV4bNY+PGjdatWzcrWbKklS5d2nr27Gnbtm0Lm2bRokXWvHlzK1y4sFWpUsUGDRp0yNYRAAAAyGkIHpFwrr76aps0aZK98cYbtnjxYmvTpo0LEP/44w/3uYK8YcOG2fDhw23OnDlWrFgxa9u2re3atSt1Hgocly5d6uYzfvx4mzZtml177bWpn2/ZssXNVwHq/PnzbfDgwfbQQw/ZiBEj4rLOAAAAQKIjeERC2blzp73//vsuQGzRooUde+yxLqjT/xdeeMGVOg4ZMsTuv/9+69ixo9WvX99ef/11W7NmjY0bN87NY9myZfbZZ5/Zyy+/bCeffLKddtpp9swzz9jo0aPddDJq1Cjbs2ePvfrqq1anTh275JJL7Oabb7annnoqzikAAAAAJKbkeC8AEGrfvn22f/9+V5U0lKqnzpgxw1atWmXr1q1zJZGeUqVKuSBx9uzZLgjUf1VVbdKkSeo0mj5fvnyupPL888930yg4LViwYOo0Kr18/PHH7Z9//rEyZcocsGF2797tHqGll7J37173iBVvXrGcJ0jreCJPk865Cfk556cz51cg6wgekVBKlChhTZs2tQEDBlitWrWsfPny9vbbb7tgT6WPChxF74fSa+8z/S9XrlzY58nJyXbYYYeFTVO9evUD5uF9llbwOHDgQOvXr98B70+cONGKFi1qsaYqtzg0SGvSOTchP5POuUl25OcdO3bEfJ5AXkHwiISjto49evSwI4880vLnz2+NGjWyrl27uraJ8dSnTx+7/fbbw0oe1dGO2k6qY55Y3hHVybJ169ZWoECBmM0XpHW8kKdJ59yE/Jzz09mrOQQg8wgekXCOOeYY++qrr2z79u3uAK9eVS+++GI7+uijrUKFCm6a9evXu/c9et2wYUP3XNNs2LDhgOqw6oHV+77+6zuhvNfeNJEKFSrkHpF0UsuOIC+75gvSOl7I06RzbkJ+zrnpzLkVyDo6zEHCUi+qChDVBvHzzz93HeSoqqmCu8mTJ6dOpwBTbRlV3VX0f9OmTWEllVOmTLGUlBTXNtKbRj2whrZ70B3OGjVqpFllFQAAAMjrCB6RcBQoqrdUdY6jgO7MM8+0mjVr2lVXXWVJSUluDMiHH37YPvroIzeUxxVXXGGVKlWyTp06ue+rrWS7du3smmuusblz59rMmTPtxhtvdJ3paDq59NJLXWc5Gv9RQ3qMGTPGhg4dGlYtFQAAAMB/qLaKhLN582bXvvD33393ndx07tzZHnnkkdRqJr1793ZVWjVuo0oYNRSHgs3QHlo1FIcCxpYtW7peVjUPjQ0Z2kOrOrq54YYbrHHjxla2bFnr27dv2FiQAAAAAP5D8IiEc9FFF7lHelT62L9/f/dIj4LOt956K8Pf0RiR06dPP6hlBQAAAPIKqq0CAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwi07Zs2WLjxo2zZcuWkXoAAABAHkHwCF8XXXSRPfvss+75zp07rUmTJu69+vXr2/vvv08KAgAAAHkAwSN8TZs2zZo3b+6ejx071gKBgG3atMmGDRtmDz/8MCkIAAAA5AEEj/C1efNmO+yww9zzzz77zDp37mxFixa1Dh062MqVK0lBAAAAIA8geISvKlWq2OzZs2379u0ueGzTpo17/59//rHChQuTggAAAEAekBzvBUDiu/XWW61bt25WvHhxq1atmp1xxhmp1Vnr1asX78UDAAAAcAgQPMLX9ddfbyeddJL99ttv1rp1a8uXL1hgffTRR9PmEQAAAMgjqLaKqKiH1fPPP9+VPu7fv98WLlxozZo1s1NPPTWmKah5P/DAA1a9enUrUqSIHXPMMTZgwADXSY9Hz/v27WsVK1Z007Rq1eqAtpcbN250paUlS5a00qVLW8+ePW3btm1h0yxatMh1BKSqt6qaO2jQoJiuCwAAAJCbEDwiqmqrr7zySmpwd/rpp1ujRo1cwDV16tSYpuDjjz9uL7zwghsaRONI6rWCumeeeSZ1Gr1WT6/Dhw+3OXPmWLFixaxt27a2a9eu1GkUOC5dutQmTZpk48ePd1Vsr7322rCxKtV2U9Vw58+fb4MHD7aHHnrIRowYQY4AAAAA0kDwCF/vvfeeNWjQwD3/+OOPbdWqVbZ8+XK77bbb7L777otpCs6aNcs6duzoenI96qij7MILL3RB3ty5c1NLHYcMGWL333+/m05jTb7++uu2Zs0aGzdunJtGQac69nn55Zft5JNPttNOO80Fn6NHj3bTyahRo2zPnj326quvWp06deySSy6xm2++2Z566ilyBAAAAJAGgkf4+uuvv6xChQru+aeffmpdunSx448/3nr06GGLFy+OaQqqKuzkyZPthx9+cK+/++47mzFjhp199tnutQLXdevWuaqqnlKlSrkgUT3Civ6rqqqq2no0vdpqqqTSm6ZFixZWsGDB1GlUerlixQrXiywAAACAcHSYA1/ly5e377//3rUxVImeqpXKjh07LH/+/DFNwXvuucdVKa1Zs6abt6rJPvLII64aqihw9JYpchm9z/S/XLlyYZ8nJye7sSpDp1G7ysh5eJ+VKVPmgGXbvXu3e3i0nLJ37173iBVvXrGcJ0jreCJPk865Cfk556cz51cg6wge4euqq66yiy66yAWPSUlJqaV+KsVTkBdL77zzjqtS+tZbb7nqpOqYR20uK1WqZN27d4/r1ho4cKD169fvgPcnTpxoRYsWjfnvqb0mDg3SmnTOTcjPpHNukh35WTe/AWQNwSN8qSOZunXruqE6VGW1UKFC7n2VDKqkMJbuuusuN0+1QRSNI7l69WoXuCl49KrPrl+/3gWzHr1u2LChe65pNmzYEDbfffv2uR5Yve/rv74TynvtTROpT58+dvvtt4eVPKrTILXJVK+usbwjqpOlhkUpUKBAzOYL0jpeyNOkc25Cfs756ezVHAKQeQSPiIo6rpHQHk2zoyRQdwO9cSQ9ClJTUlLcc1U1VXCndpFesKiTgEpBe/Xq5V43bdrUNm3a5HpRbdy4sXtvypQpbh5qG+lNo85+dHLyTko6SdWoUSPNKquioNkLnEPp+9kR5GXXfEFaxwt5mnTOTcjPOTedObcCWUeHOfCldocaa/HII4904zz+/PPP7n2Nx+gN4REr5557rmvj+Mknn9gvv/xiY8eOdT2gaoxJUbVZVWN9+OGH7aOPPnId9lxxxRWuWmunTp3cNLVq1bJ27drZNddc43ppnTlzpt14442uNFPTyaWXXuo6y9H4jxrSY8yYMTZ06NCwkkUAAAAA/yF4hC8FcyNHjnTjK4b2TqqqrBoOI5Y0pIZKOa+//noXBN555532f//3fy549fTu3dtuuukmN27jiSeeaNu2bXMd+RQuXDh1GrWbVHvMli1bWvv27d1wHaFjOKqHVrVVVO+tKp284447rG/fvmFjQQIAAAD4D9VW4UvjKCrwUiB23XXXpb6vsR813mMslShRwo3jqEd6VPrYv39/90iPelZVpzsZ0RiR06dPP6jlBQAAAPIKSh7h648//rBjjz32gPfVhpDurgEAAIC8geARvmrXrp1mCd17771nJ5xwAikIAAAA5AFUW4UvtQVUz6oqgVRp4wcffGArVqxw1VnHjx9PCgIAAAB5ACWP8NWxY0f7+OOP7YsvvrBixYq5YHLZsmXuPY2/BAAAACD3o+QRUWnevLkbBxEAAABA3kTwiKjt2bPHNmzY4KquhqpatSqpCAAAAORyBI/wtXLlSuvRo4fNmjUr7P1AIOCGzdi/fz+pCAAAAORyBI/wdeWVV1pycrLrHKdixYouYAQAAACQtxA8wtfChQtt/vz5VrNmTVILAAAAyKPobRVRjfP4119/kVIAAABAHkbwCF+PP/649e7d26ZOnWp///23bdmyJewBAAAAIPej2ip8tWrVyv1v2bJl2Pt0mAMAAADkHQSP8PXll1+SSgAAAEAeR/AIX9WrV7cqVaoc0MuqSh5/++03UhAAAADIA2jziKiCxz///POA9zdu3Og+AwAAAJD7ETzCl9e2MdK2bduscOHCpCAAAACQB1BtFem6/fbb3X8Fjg888IAVLVo09bP9+/fbnDlzrGHDhqQgAAAAkAcQPCJdCxYsSC15XLx4sRUsWDD1Mz1v0KCB3XnnnaQgAAAAkAcQPMK3l9WrrrrKhg4daiVLliS1AAAAgDyK4BG+XnvtNVIJAAAAyOMIHpGmCy64wEaOHOlKG/U8Ix988AGpCAAAAORyBI9IU6lSpVJ7WNVzAAAAAHkbwSPSrao6ZcoUa9GiBdVWAQAAADDOI9LXunVr27hxY+rrU045xf744w+SDAAAAMiD8sV7AZC4NERHqKVLl9ru3bvjtjwAAAAA4ofgEQAAAADgi+AR6VKHOV6nOWm9BgAAAJB30GEOMqy22rJlS0tODmaTHTt22LnnnmsFCxYMm+7bb78lFQEAAIBcjuAR6XrwwQfDXnfs2JHUAgAAAPIogkdEHTwCAJAI1J/bokVm339vpsox6gi8SBGzww83W7PGbP16swoVzEqWNDvySLOjjzarWjU4bXbZuze4XKGVc/75x+yXX8x27jQ77LDg8uTPb1aunNmePcHnsny5moYEX6uTcy1nSorZ4sVmf/9tVrZscPry5YPrpUeZMmZ//RVcZ807t7UqUVr+/ntx27TJ7Igj4r00ADwEjwAA4JAEAwrsFAxUr25WtOiB0/z6q9m2bcHPFAz99FMwSPz9d7OtW4Of679er16dud9XQKYgS4Hcvn1mpUubVakSfK9ECbN8+czWrTMrVCj4O5q/grZTTzWrVy8YqCrIK1Eiv+3Z08QmTMjnghoFrvPmBT9XwKf56X11Tq756L1IhQub7doVDBb1uwo8D4aWWUGyAmQFk3qt36hRw6xSpeD6FSsWTFOlrdZbAagCUgWgW7YE5/PNN2YbNgQDcaWRvqMgVdurVq3/AvM//zRbuDD4XQW3+rxJk+B8lGYKoLXtduwwK1UqGPRu3hwMpvVQHtBnWs6VK81+/DGYBvrd7duDnwcCybZtW0srVmyfXXnlwaUPgNgheAQAADGn4ODmm83mzAkGCT//HAwcRAGTAhsFcAoGFdwokJk/P/r5KwiqXz/4vWOPDf7/7rtg6Z4CPgWCCkQVwK1aFQzmFBh5FESuXZvxb/z2m9no0cFHeF+DR9qsWWl/RyWHIUMkuyBNy6rf1m/u3x8MHEXP9ShePBhIKrhT8Kn3FLwpYFMaKfDSNArctF56KH01X/3Xuil99cg9kqxgwf22fn0uK1IFcjiCRwAAkOlSxHffDZYKSsWKwQBOwY8CGwUxn3wSLMkKu+hIDpZmKRhatizteaukSwGRSqI0TwVfqnaq4KlRo2BwqNKsk04KPo+GSv9UaqagTgGXlkMlZgoOVcqlAFa/p1I6PVcJ2CmnBL/zxRfBkrHjjzdr2FCla/ts+vTvrXz5OvbPP/ldsHfiiWaNGwe/p++oRE7PNT+V8oVS4KiqrCqN8wK/o44KBtSZoe8pKNf8FASrNNZbH32mwHnJkuA6a530W6LSQZWWKjgVlRJqm+j9pk2DpYheqaje07ZaseK/dVKpqrZ/69bB0k5tAwXtmkaBrn5P36tdO3hTQOmh39d21ef6r4fmpd/RPHQTQMuh4FrLoul27NhrK1dOsI4dzzazf+v3Aog7gkcAAHAABRsjRgQDHQUGCiYUlOjCXu9FBoZpUcA3fHgw2FOAWbduMEhQsKPAU4GJqlkq0NFDQZhKJEWlb16bwIOlwKxy5eDDo4BNAV9GFLy2aBH+3t69AStVapW1b1/LChQ4cAEVTGZE1Ulr1gw+VxCVVQocvfkp4NMjWkpblQJrHtpGCvoVEKbXblKfaXpt+8wGuVmlYP6XXwKH5scARI3gEb5ef/11u/jii62Qd6b61549e2z06NF2xRVXkIoAkAMpiFBbPQUNXsmPgrqPPjL76qtg27b06JRw+eXB/yrxUmmjgj2VRKm0TaVO99wTDNIiqUROj4zEKnBE2mmrtomhMupwx6tOCwAEj/B11VVXWbt27aycuocLsXXrVvcZwSMAJB6VFin4U4ckoQ9VwVSJn0r/VLrjVV9Mi6qMXnRRsO2dAg6VcqlapALNDh3CS/IAALkfwSN8BQIBS0rjluTvv/9upVQXCQAQd6pSunJlaXvxxXy2YIHZhx8G2/X5UbVFtQPU9/VfAaMCw5Ytg+0K0+oVFQCQNxE8Il0nnHCCCxr1aNmypSWHDJC1f/9+W7VqlSuRBAAc2qqm6kFU7RDV9nDqVLPp01XKmGx7954eNq3u+2n4huOOC7bf03891M5QHZ/osK4qptk5/iEAIPfgdIF0derUyf1fuHChtW3b1orr9vS/ChYsaEcddZR17tyZFASAg6BeMr/8MthLqXqlVPtBdVajHjFDh5bweg3V+95QD+GSrESJ3dasWQFr0CCf6w3ztNOCVU0BAIgFgkek68EHH3T/FSRecsklB3SYAwCInsYhHD8+2IOpBpVXu8ECBYLBo9onZoa+q6EjNISFOj7p2lXH6r22ZMln1qFDeytQ4BB1iQkAyFMIHuHrrLPOsj///NMq/9szwty5c+2tt96y2rVr27XXXksKAsC/1JnM6tVm06YFxzFUCeLy5cGOatRJjaqcpuXkk4PBoMa406FWPZWqN1KNBRg5NILaJKoH08jeSNX5zdKlbAoAQPYheISvSy+91AWJl19+ua1bt85atWpldevWtVGjRrnXffv2JRUB5GoK+hT8KTj0HqJqpd9+azZ/frD30vQGvvcoGOzRw0w1/tXOUJ3UaPzDyIHkAQBIRASP8LVkyRI7SV3umdk777xj9erVs5kzZ9rEiRPtuuuuI3gEkGOpuqgCQPVKqoBQA6HroedqV6hSQ1UxVYleeqWGkdQRjTqlUXtDlSBqQHiVKmqA9SOPZPxCAEDORfAIX3v37k1t7/jFF1/Yeeed557XrFnT1upWOwAksL/+CvZMqkBQ/9evD1Yn1ftLlgT/R0vDVigI1EOBZ8mS6pk6OKSFhsLVfwWPAADkRgSP8FWnTh0bPny4dejQwSZNmmQDBgxw769Zs8YOV3/vAJAgFNAtXGj20ktmixYFA0a/sQ41nEWZMsGHgkLvv3op1TAXJ55o1qhRsHqpOrgBACCvIniEr8cff9zOP/98Gzx4sHXv3t0aNGjg3v/oo49Sq7MCQHb1UKqHOoPRQ+0OVeFBJYYK+lQpYufO4HiHs2YFO6b5888D51OtmlmNGsEqpKo6qo5pFCDqvbp1Gc4CAIBoEDzC1xlnnGF//fWXbdmyxcrolvy/1IlOUdXhAoCDoLaEq1aZ/fBDsK3htm3B4Ss+/TTYEU1mKaDUMLV6eO0NOVQBAHDwCB4RlUAgYPPnz7effvrJ9b5aokQJK1iwIMEjgCxTJzSvvGL2xhvRtzssXjw4rmHt2sEgUb2VKjDUe02bmtWrF+ysRtMBAIDYIniEr9WrV1u7du3s119/td27d1vr1q1d8KjqrHqt9pAAEA1VKX3vPbPXXzf7+uv/3lf7QpUQqrMZBYP6r1rx55wTbIOotoYa2kJVVQEAQHwQPMLXLbfcYk2aNLHvvvsurIMctYO85pprSEEA6VJ7xQUL1Eb6aOvdO9m1SVSnNu4ElBwMDnv2NGvXLvgaAAAkLk7V8DV9+nSbNWuWq6Ya6qijjrI//viDFATyKAWB6rzmq6+Cj59/DpYsqjppSkrwc/V4umePuiitl/q9xo3NunY1u+wys/Ll47oKAAAgEwge4SslJcX2pzE69u+//+6qrwLIO9TjqQLFiRPN3n472LGNnzJlAlap0t/Ws2cZu/TS/ASMAADkUASP8NWmTRsbMmSIjRgxwr1OSkqybdu22YMPPmjt27cnBYE8MnbinDlmw4aZLVv232dqg1i/vlnr1sEhL8qVC1ZVVcmjAk2VMlapss8mTJjpjhcFCuSP56oAAICDQPAIX08++aS1bdvWateubbt27XK9ra5cudLKli1rb6voAUCupLaKkyebffCB2ezZ/71/2GFm555r1qFDsM1ikSIZz0dBJAAAyPkIHuGrcuXKrrOcMWPGuP8qdezZs6d169bNivhdNQLIUSWMM2aYqZLB3LnBcRdDe0Nt3jw4buK995pVqBDPJQUAAPFA8IjoMkpysgsW9QCQO6gp85QpwSBx1apgO8bFi//7XH1kqRfU004zu/RSsyOPjOfSAgCAeCN4hK+///47dYiO3377zV566SXbuXOnnXvuudaiRQtSEMhhNm8OVkd9/vng/1CqTKB7RJ06mTVpQm+oAADgPwSPSNfixYtdgKiA8bjjjrPRo0dbu3btbPv27ZYvXz57+umn7b333rNOusoEkNC2bDH7/HOzCRPMRo8227kz+H7RouoUy6xatWCHNxdeaFa6dLyXFgAAJCKCR6Srd+/eVq9ePRs1apS98cYbds4551iHDh1cyaPcdNNN9thjjxE8AglKHdVMmxbs8OZ//zPbvv2/z44/3qxtW7Nevcxq1YrnUgIAgJwiX7wXAIlr3rx59sgjj9ipp55qTzzxhK1Zs8auv/56V+qoh4LH5cuXx/Q3jzrqKDcUSOTjhhtucJ+rt1c9VzXa4sWLW+fOnW39+vVh8/j1119dkFu0aFErV66c3XXXXbZv376waaZOnWqNGjWyQoUK2bHHHmsjR46M6XoA8fLnn2a6v6OeUCtVMmvVKlg9VYHjcceZ3Xqr8r+Zdl0Nu0HgCAAAokXJI9K1ceNGq/Bvl4oK1IoVK2ZlypRJ/VzPt27dGvOAdb968fjXkiVLrHXr1talSxf3+rbbbrNPPvnE3n33XStVqpTdeOONdsEFF9jMmTPd5/quAkct96xZs2zt2rV2xRVXWIECBezRRx9106xatcpNc91117lS1cmTJ9vVV19tFStWdEOSADkxYFRA+PDDZosWhX+mcRc1BuNVV5mddVZwXEYAAICsIHhEhlTql9HrWDviiCPCXqta7DHHHGOnn366bd682V555RV766237CxdBZvZa6+9ZrVq1bKvv/7aTjnlFJs4caJ9//339sUXX1j58uWtYcOGNmDAALv77rvtoYcesoIFC9rw4cOtevXqbvxK0fdnzJjh2nASPCInee89swceCJYihqpf3+ySS8zUn9VJJ5kVKBCvJQQAALkJwSMydOWVV7qqnV6VUZXWqQRSdu/ena2pt2fPHnvzzTft9ttvd0Hr/Pnzbe/evdZK9fD+VbNmTatatarNnj3bBY/6r3aaChw9Cgh79eplS5cutRNOOMFNEzoPb5pbVZ8PSGB79gQ7vFEbxp9+Mps92ywlJfiZqp+2b2929926CRPvJQUAALkRwSPS1b1797DXl1122QHTqEpodhk3bpxt2rTJBbCybt06V3JYOqIrSAWK+sybJjRw9D73Pstomi1btrghSIporII0KFgODZg1vSig1SNWvHnFcp7I2Wm9dKnZyy/nszFj8tlff4WX/l999X4bMCDF/h1Nx0m01ckp6ZzTkc6kc26SnfmZYxGQdQSPSJeqhMaTqqieffbZVkm9fiSAgQMHWr9+/Q54X1Vl1TlPrE2aNCnm80TOSusdO5Jt7Nhj7YMPjrP9+4P9m5Ups8uaN//datT4xypV2mbVq2+xOXMsR0jUdM5tSGfSOTfJjvy8Y8eOmM8TyCsIHpGQVq9e7dotfqD6ef9SJziqyqrSyNDSR/W26nXso/9z584Nm5fXG2voNJE9tOp1yZIl0y11lD59+rgqtKElj1WqVLE2bdq478byjqhOluooSB39IPskYlqrv6hx45LszTfz2dSpSbZ9e7CksX37FLvuuhRr1Sq/JSdXMzM9coZETOfciHQmnXOT7MzPXs0hAJlH8IiELfXUMBvqFdXTuHFjdwJR76gaokNWrFjhhuZo2rSpe63/Gl5kw4YN7vuik4+Cu9q1a6dO8+mnn4b9nqbx5pEetf302n+G0jJlxwVxds0XiZnW06ebffihAsdge0aP2jIOGGB2wQX5LCkpZ4+ulAjpnBeQzqRzbpId+ZnjEJB1BI9IOCkpKS54VJvL5OT/sqiG5ujZs6cr/TvssMNcQKixJhX0qbMcUSmggsTLL7/cBg0a5No33n///W5sSC/wU6c/zz77rPXu3dt69OhhU6ZMsXfeeccNAQIcSoFAMGB89VWzjz/+732NiHP99WadOummCcNrAACAxEDwiISj6qoqTVRgF0nDaeTLl8+VPKrzGvWS+rxGQP9X/vz5bfz48a53VQWV6hlWQWj//v1Tp9EwHQoUNWbk0KFDrXLlyvbyyy8zTAcOmX37VLpuNny42bffBt/Ll8+sW7dgj6nnnKOxVdkgAAAgsRA8IuGo9DCgIpk0FC5c2J577jn3SE+1atUOqJYa6YwzzrAFCxYc9LICmfXzz2Zdu5p5TXNVIN6rl9nll5s1akR6AgCAxEXwCACHwLZtZoMGmQ0dqs4azNTn0wMPmF16qTpxYhMAAIDER/AIANlcRXXECLNhw9TBU/C9Zs3M3n7brGpVkh4AAOQcBI8AkA02bzZ7+WWz1183W7Qo+J46AB4yxKxLF7OQvqAAAAByBC5fACCGli8PVk19661g9VRR5zd3323Ws6dZxYokNwAAyJkIHgEgBv76y+zFF80efdRsx47gexpaVENudOxoVrkyyQwAAHI2gkcAOAgqXVSp4siRZrt2Bd8780yze+4xa9UqOAQHAABAbkDwCABZ8M8/wU5wFDT+8kvwPQ21cdttwaE48ucnWQEAQO5C8AgAmaAhSDXMqIbZ2LQp+N4RR5iNHh0scUxKIjkBAEDuRPAIAFFassTszjvNPv88+Lpu3WCbxvPPZ6xGAACQ+xE8AoCP7dvN7rvP7JlnzFJSzAoUMHvyyWDgSPVUAACQVxA8AkA61qwxGzXK7IknzDZsCL7XuXOwR9XjjyfZAABA3kLwCAARPvvM7JVXzD74IFjSKEcfbfbss2Znn01yAQCAvIngEQD+tXmz2Y03mr355n9JcsopZtdcY3b55cHqqgAAAHkVwSOAPG/vXrOXXjLr1y9YPVVjM/bqFQwaGzTI88kDAADgEDwCyNP27k2yzp3zu6qqoraMGruxadN4LxkAAEBiIXgEkGd9+63ZPfe0sJ9+ymdFigQ7xlFpI9VTAQAADkTwCCDPCQTMhg41u+OOZEtJKW2lSgVszJgka9s23ksGAACQuPLFewEA4FD64Qez1q3NbrtNPakm2amn/mGLF+8jcAQAAPBBySOAPGPcOLNu3cx27DArXNjskUf229FHf2MVKrSP96IBAAAkPEoeAeR6u3cHh+A4//xg4HjmmWZLlpjddFOKJSXFe+kAAAByBkoeAeRqv/9u1rmz2dy5wdeqrjpokFlysnpajffSAQAA5BwEjwByrb//NmvZMtjOsUwZszffNGtPDVUAAIAsIXgEkCv9/LO5TnB+/NGsalWzqVPNqleP91IBAADkXLR5BJDr7Ntn1rXrf4HjhAkEjgAAAAeLkkcAucr27cEeVdXGsXRps5kzzSpXjvdSAQAA5HwEjwByjeXLzS691GzBArNChcz+9z8CRwAAgFih2iqAHC8QMHv6abOGDYOBY9myZlOmmJ13XryXDAAAIPcgeASQ46up3n578KHxHM8+2+zbb82aNYv3kgEAAOQuBI8AcqSUFLM33jA7/nizIUOC72n8xk8+MatSJd5LBwAAkPvQ5hFAjrN1q9lll5l99FHwtYbgePJJs/PPj/eSAQAA5F6UPALIUWbNCrZtVOCoTnEee8zs++8JHAEAALIbJY8AcoQ9e8z69zcbODBYZVXjN44ZY3bKKfFeMgAAgLyB4BFAwvvrL7P27c3mzQu+vvxys2eeMStVKt5LBgAAkHdQbRVAQlu82Oz004OBY5kyZu+8Y/b66wSOAAAAhxoljwASduzG554zu/PO4BAcFSoEx26sVSveSwYAAJA3UfIIIOH8/bdZx45mN90UDBxVZXXhQgJHAACAeKLkEUBC2bTJrHVrswULzAoWNBs8OBhEJiXFe8kAAADyNoJHAAlDpYudOpmtXm1WrpzZ558Hh+UAAABA/FFtFUBCmDw52DGOAsejjzabOJHAEQAAIJEQPAKIu1deMWvXzmzLFrPmzc2+/dasQYN4LxUAAABCETwCiKtRo8yuvtps3z6zrl2DJY6M3wgAAJB4CB4BxI3GbLzyyuDzW24JBpKFC7NBAAAAEhHBI4C4eO21YEmjShy7dTN76il6VAUAAEhkBI8ADrlnnjHr0cMsJcXsmmvM/vc/s3wcjQAAABIal2sADqkXXjC7+ebg89tvN3vxRbP8+dkIAAAAiY7gEcAhs3y52R13BJ/ff7/ZE09QVRUAACCnIHgEcEhMm2Z20klmO3eanXWWWf/+BI4AAAA5CcEjgGz3ww9mnTubbd1q1rSp2RtvEDgCAADkNMnxXgAAudvcuWbnnmv2119mjRqZTZ5sVqRIvJcKAAAAmUXJI4Bs8/77ZqefbrZhg9kJJ5hNmEDgCAAAkFMRPALIFm++adali9muXWYdOph99ZVZuXIkNgAAQE5F8Agg5t56y6x7d7NAIDiO44cfmpUoQUIDAADkZLR5BBBTb79tdvnlZikpwcBx+HCzfNymAgAAyPG4pAMQ08DxssuCgWPPngSOAAAAuQnBI4CYUAljt27BwLFHD7MRIyhxBAAAyE0IHgEctGHDzHr1CrZxvP56s5deInAEAADIbQgeARyU114zu+WW4PN77zV79lkCRwAAgNyI4BHAQY3jePXVwed33GH28MNmSUkkKAAAQG5E8AggSz7/3Kxr1/86xxk8mMARAAAgNyN4RML5448/7LLLLrPDDz/cihQpYvXq1bNvvvkm9fNAIGB9+/a1ihUrus9btWplK1euDJvHxo0brVu3blayZEkrXbq09ezZ07Zt2xY2zaJFi6x58+ZWuHBhq1Klig0aNOiQrWNOt2CB2QUXmO3da9ali9mLLxI4AgAA5HYEj0go//zzj5166qlWoEABmzBhgn3//ff25JNPWpkyZVKnUZA3bNgwGz58uM2ZM8eKFStmbdu2tV27dqVOo8Bx6dKlNmnSJBs/frxNmzbNrr322tTPt2zZYm3atLFq1arZ/PnzbfDgwfbQQw/ZCHURigytW2d23nlmO3aYtW5t9uabZvnzk2gAAAC5XXK8FwAI9fjjj7tSwNfUC8u/qlevHlbqOGTIELv//vutY8eO7r3XX3/dypcvb+PGjbNLLrnEli1bZp999pnNmzfPmjRp4qZ55plnrH379vbEE09YpUqVbNSoUbZnzx579dVXrWDBglanTh1buHChPfXUU2FBJsLt3GnWqZPZ77+b1ahh9s47ZgULkkoAAAB5ASWPSCgfffSRC/i6dOli5cqVsxNOOMFe0rgP/1q1apWtW7fOVVX1lCpVyk4++WSbPXu2e63/qqrqBY6i6fPly+dKKr1pWrRo4QJHj0ovV6xY4Uo/cSC1bbz8cjMloQqCP/7YrHRpUgoAACCvoOQRCeXnn3+2F154wW6//Xa79957XenhzTff7IK87t27u8BRVNIYSq+9z/RfgWeo5ORkO+yww8KmCS3RDJ2nPgutJuvZvXu3e4RWfZW9e/e6R6x484rlPGPhgQfy2fvv57cCBQL23nv77aijAq7NY06WqGmd25DOpHNuQn7O+enMMR/IOoJHJJSUlBRXYvjoo4+61yp5XLJkiWvfqOAxngYOHGj9+vU74P2JEyda0aJFY/57aq+ZKL75prw9/vgp7vn1139rW7f+bp9+arlGIqV1bkY6k865Cfk556bzDjXaB5AlBI9IKOpBtXbt2mHv1apVy97XgIJmVqFCBfd//fr1blqPXjds2DB1mg0bNoTNY9++fa4HVu/7+q/vhPJee9NE6tOnjysRDS15VPtMdbyjXl1jeUdUJ8vWrVu7joPibckSVVcNHip69dpvgwfXNzM9cr5ES+vcinQmnXMT8nPOT2ev5hCAzCN4REJRT6tqdxjqhx9+cL2iiqqaKribPHlyarCok4DaMvbq1cu9btq0qW3atMn1otq4cWP33pQpU1ypptpGetPcd9997uTknZR0kqpRo0aaVValUKFC7hFJ38+OwCO75psZisHPP99s61az0083GzJE1VZzX9eqiZDWeQHpTDrnJuTnnJvOHO+BrKPDHCSU2267zb7++mtXbfXHH3+0t956yw2fccMNN7jPk5KS7NZbb7WHH37Yda6zePFiu+KKK1wPqp3UDei/JZXt2rWza665xubOnWszZ860G2+80fXEqunk0ksvde0oNf6jhvQYM2aMDR06NKxkMa9T804l6erVZscea6bCX3pWBQAAyLsoeURCOfHEE23s2LGuimj//v1dSaOG5tC4jZ7evXvb9u3b3ZAaKmE87bTT3NAchQsXTp1GQ3EoYGzZsqXrZbVz585ubMjQHlrVVlFBqUony5Yta3379mWYjhADBqhX2mCPquPHmx1++KHKBQAAAEhEBI9IOOecc457pEeljwos9UiPelZVqWVG6tevb9OnTz+oZc2tZs3SmJvB5y+/HBzTEQAAAHkb1VYBhPn9d7POndXJkNlFFwWfAwAAAASPAFJt327WsaPGujSrW9fslVdIHAAAAAQRPAJwUlLMrrzS7NtvzcqWNfv4Y7PixUkcAAAABBE8AnD69TN77z11YW42dqzZUUeRMAAAAPgPwSMAe+cdM6//oREjzE47jUQBAABAOIJHII9bvtysR4/g8zvvDFZdBQAAACIRPAJ5vIOcCy8M/j/zTLPHHov3EgEAACBRETwCedhtt5ktXWpWoYKZhsXMnz/eSwQAAIBERfAI5FEffmj20ktmSUnBwFEBJAAAAJAegkcgD9I4jldf/V87R1VZBQAAADJC8AjkMYFAsIOcv/4ya9DAbMCAeC8RAAAAcgKCRyCPUac4EyaYFSpkNmpU8D8AAADgh+ARyENmzDC7777g82eeMatTJ95LBAAAgJyC4BHII3btCo7hqGqr3bubXXNNvJcIAAAAOQnBI5BHDBli9tNPZpUqmQ0dGu+lAQAAQE5D8AjkAWvXmj3yyH9tHkuVivcSAQAAIKcheARyOVVTvesus23bzE46yaxbt3gvEQAAAHIigkcglxsxItirar58weqq+g8AAABkFpeRQC42e7bZTTcFn6va6imnxHuJAAAAkFMRPAK51Lp1ZhdeaLZ3r1nnzmZ33x3vJQIAAEBORvAI5EJ79ph16WK2Zo1Z7dpmr71mlpQU76UCAABATkbwCORCw4aZzZhhVrKk2dixZiVKxHuJAAAAkNMRPAK5zK5dZk88EXz+5JNmxx8f7yUCAABAbkDwCOQy/fubrV9vVqWK2RVXxHtpAAAAkFsQPAK5yJw5Zo89Fnz+9NNmBQvGe4kAAACQWxA8ArlESkpwWI5AwOzyy4M9rAIAAACxQvAI5BL/+5/ZvHnBznEGDYr30gAAACC3IXgEcoFNm8zuuSf4/MEHzSpUiPcSAQAAILcheARygb59zTZsMKtRI1h1FQAAAIg1gkcgh/vuO7Pnngs+f/ZZOskBAABA9iB4BHKw/fvNrrsu2FlOly5mrVrFe4kAAACQWxE8AjmYhuP4+utgJzlPPhnvpQEAAEBuRvAI5FDr1pn16/dfEFmlSryXCAAAALkZwSOQQ913n9m2bWYnnWTWo0e8lwYAAAC5XXK8FwBA5s2YYfbqq8HnQ4aYJSWRigAQCARs3759tl8NwrPJ3r17LTk52Xbt2pWtv5PXHUw658+f3303iZMjEHMEj0AOs3evWa9ewedXX23WtGm8lwgA4m/Pnj22du1a27FjR7YHqBUqVLDffvuN4CSB07lo0aJWsWJFK1iwYLYsH5BXETwCOYyG41iyxKxsWbPHHov30gBA/KWkpNiqVatciVOlSpVcwJBdpU76rW3btlnx4sUtXz5a/2SXrKazgk7dSPjzzz9dnjjuuOPYTkAMETwCOcjff5v17x98PnCg2eGHx3uJACD+FCwo2KhSpYorccpO+h39XuHChQlKEjSdixQpYgUKFLDVq1enzgNAbHDLDMhBFDhu2mRWv77ZVVfFe2kAILFQEgjyApC9CB6BHOKHH8yefz74XGM65s8f7yUCAABAXkLwCOQQvXub7dtn1qGDWatW8V4aAEBeceWVV1qnTp3ivRgAEgDBI5ADfPaZ2YcfBksbBw+O99IAAGIVlKljH+9x+OGHW7t27WzRokUxS+CHHnrIGjZsGNV0ocviPb744gsbOnSojRw5MnXaM844w2699daYLSOAnIPgEUhwu3ebXXdd8PnNN5vVqhXvJQIAxIqCRQ0xosfkyZPd+ITnnHNOXBK4Tp06qcviPVq0aGGlSpWy0qVLx2WZACQWgkcgwb3+utnq1WZHHmk2YEC8lwYAEEuFChVy4xnqoRLCe+65x41tqKEmPHp90UUXuQDusMMOs44dO9ovv/yS+vnUqVPtpJNOsmLFirlpTj31VNfTqEoL+/XrZ999911qSWJoCWIkBa7esngPDXsSWm1Vz7/66itXGunNU8uiZdBzBcBNmjRxvd42a9bMVqxYEfYbH374oTVq1Mj1gHr00Ue75dunNhn/DrOhEtCqVau6HlNr1aplt9xyS+p3n3/+eTf0hr5bvnx5u/DCC2O6LQD4Y6gOIIHt3282aFDw+Z13mhUrFu8lAoCcIRAw27Ej9vNNSTHbvj3YjCCtESQ0UkhWh5jUuIZvvvmmHXvssa4Kq+zdu9fatm1rTZs2tenTp7sA7+GHH06t3qoeZhXYXXPNNfb222+7oSnmzp3rArmLL77YlixZYp999pmrfioqRTwYChp/+OEHq1u3rvX/d+yoI444IjWYve++++zJJ59071133XXWo0cPmzlzpvtMy3/FFVfYsGHDrHnz5vbTTz/Ztdde6z578MEH7f3337enn37aRo8e7QJHff7jjz+6z7/55hu7+eab7Y033nBB6caNG938ABxaBI9AAnv/fTOdNw87zOzqq+O9NACQcyhwLF48O+asiDH9KpzbtmXuRt/48eOt+L8Lun37dqtYsaJ7zxt2ZMyYMW7Mw5dfftkFhPLaa6+5EkaV9qmUb/Pmza6q6zHHHOM+V+Dl0by9EkU/ixcvTl0WqV27tgtEQyn4VGmkShbTmucjjzxip59+unuuUtQOHTrYrl27XGmhShn1Xvfu3d3nKnkcMGCA9e7d2wWPv/76q5tnq1atLH/+/G4dzzzzTDetPlPJqtazRIkSVq1aNTvhhBOiT2gAMUG1VSBB7d37XzXVm27KrosgAEA8KThauHCheyhQUynj2Wef7aqdiqqcqvRNAZMCOz1UdVUBmUrm9FxVSfW9c88915UMqq1iVtSoUSN1WfRQSWBm1ddAxP9SICwbNmxIXReVVnrroYdKTLW8O3bssC5dutjOnTtdUKkSSQXRXpXW1q1bu4BRn11++eU2atQo9x0AhxYlj0CCevrpfLZkiZlqLil4BABET9VHVQoYayoF3LJli5UsWTK1dDDydzNDpWmqpupRCaNK91566SVXPVVVWRs3buyCpUiqGuqVRKpKp6qnqqTy/vvvt0mTJtkpp5ySqWVRiWLosmRFgQIFUp97JaVKM9G6qPTxggsuOOB7KpmsUqWKayOpKrYTJ060O++807VzVBtLBc/ffvutK23VZ3379nXtI+fNm0dnPsAhRPAIJJiNG81WrixtDz4YvCh5+ulgAAkAiJ7iluxoJ644SO3RNe+02jweLAVcCkpVAifqXEYBYbly5VzAmh5V4dSjT58+rn3kW2+95YJHBYT7tcAxlNV5al0UHGYUoKqjHJWgqrqr2keqIyBVp9V3Vf1WVVr1UDVXVWudMmVKmsEogOxB8AgkmEcfzWfDhgXbi5x4otlll8V7iQAA2WX37t22bt069/yff/6xZ5991pXQKYCSbt262eDBg10Pq6ryWblyZVel9YMPPnBtBdWhzogRI+y8886zSpUqueBs5cqVLvCSo446ylatWuWqoeq7KsFTD68HQ/OcM2eO6yTHq0YbDZUWqs2ielNVT6kKklWVVZ36qJRVPcEqKD355JNdSeQ777zjgklVV1UV1p9//tkNHVKmTBn79NNPXYmmqtoCOHRo8wgkmORk3dXdb+XKBWzYsKz32gcASHyqaqq2gXooaFI1zHfffdfOOOMM97k6ppk2bZoLuFTCps5wevbs6do8qiRSny9fvtw6d+5sxx9/vGsreMMNN9j//d//ue/rffXMqraVquaqHlkPlqqTqkMbdaijeaozm2ioXaaCQFU7PfHEE13JqHpXVXAoKklUdV0NNaJhS1RdVUN7qOdZfaaA+ayzznJpMHz4cLcuGpsSwKGTFNCgOgAyTW1e1C5FvdxlVJUos3QXWXdU27dvH9Z2BLFHWh8apDPpnN0USKl0rXr16q7EKjv5tXlEYqRzRnkiu87fQF7AUQ8AAAAA4IvgEQAAAADgi+ARAAAAAOCL4BEAAAAA4IvgEQAAAADgi+ARAADkCnQgD/ICkL0IHgEAQI7mDWu0Y8eOeC8KEoSXFxjyCoit5BjPDzgoDz30kPXr1y/svRo1argBkL1xm+644w4bPXq07d692w04/Pzzz1v58uVTp9dgxb169bIvv/zSihcvbt27d7eBAwdacvJ/2X3q1Kl2++2329KlS61KlSp2//3325VXXsnWA4AcSAPWaxD5DRs2uNdFixa1pKSkbBt/cM+ePe58xDiP2Ser6azSZwWOygvKE8obAGKH4BEJp06dOvbFF1+kvg4N+m677Tb75JNP7N1333UD/N544412wQUX2MyZM93n+/fvtw4dOliFChVs1qxZtnbtWrviiivcncdHH33UTaNBgzXNddddZ6NGjbLJkyfb1VdfbRUrVnTBKAAg59FxX7wAMrsoONm5c6cVKVIk2wJUHHw6K3D08gSA2CF4RMJRsJjWAX/z5s32yiuv2FtvvWVnnXWWe++1116zWrVq2ddff22nnHKKTZw40b7//nsXfKo0smHDhjZgwAC7++67XalmwYIFbfjw4Va9enV78skn3Tz0/RkzZtjTTz9N8AgAOZQCDN0ELFeunO3duzfbfkfznjZtmrVo0YIqkdnoYNJZ01PiCGQPgkcknJUrV1qlSpWscOHC1rRpU1fltGrVqjZ//nx3MmnVqlXqtDVr1nSfzZ492wWP+l+vXr2waqwqTVQ1VlVRPeGEE9w0ofPwprn11lszXC5Vk9XDs2XLFvdfyxTLCxVvXtl58QPS+lAiT5POh1p2Bg6qTrlv3z73GwQolpDprO/qkR7Or0DWETwioZx88sk2cuRI185RVU7V/rF58+a2ZMkSW7dunSs5VFWUUAoU9Znof2jg6H3ufZbRNAoGvSoyaVEQG9keU1TaqfY1sTZp0qSYzxOkdTyRp0nn3IT8nHPTmY6VgKwjeERCOfvss1Of169f3wWT1apVs3feeSfdoO5Q6dOnj+tkx6NgU53ttGnTxkqWLBmz39EdUZ0sW7duTZWobEZaHxqkM+mcm5Cfc346ezWHAGQewSMSmkoZjz/+ePvxxx/dCUQ9r23atCms9HH9+vWpbST1f+7cuWHz0OfeZ95/773QaRQAZhSgFipUyD0i6aSWHV2BZ9d8QVrHC3madM5NyM85N505twJZR/CIhLZt2zb76aef7PLLL7fGjRu7A756R+3cubP7fMWKFW5oDrWNFP1/5JFHXG976jRBdOdSgWHt2rVTp/n000/DfkfTePPI7GDUsb6DqbutqlKj+XKCy16k9aFBOpPOuQn5Oeens3fe9s7jAKKXFGDPQQK588477dxzz3VVVdesWWMPPvigLVy40PWgesQRR7iObxT4qV2kAsKbbrrJfU/DcnhDdaiHVXW4M2jQINe+UYGnhuIIHaqjbt26dsMNN1iPHj1sypQpdvPNN7shQDIzVMfvv//uqq0CAICc57fffrPKlSvHezGAHIWSRyQUBWRdu3a1v//+2wWLp512mhuGQ89Fw2losGCVPKrnUwV7zz//fOr31SPb+PHjXZCpksRixYpZ9+7drX///qnTaJgOBYoaM3Lo0KHuxPHyyy9nepgOBag68ZQoUSKmY315bSk171i2pQRpHS/kadI5NyE/5/x0VrnJ1q1b3XkcQOZQ8ggk4AmzVKlSblxLgkfSOjcgT5POuQn5mXQG8rJ88V4AAAAAAEDiI3gEAAAAAPgieAQSjIYDUUdBaQ0LAtI6JyJPk865CfmZdAbyMto8AgAAAAB8UfIIAAAAAPBF8AgAAAAA8EXwCAAAAADwRfAIAAAAAPBF8AgkmOeee86OOuooK1y4sJ188sk2d+7ceC9SjjJt2jQ799xzrVKlSpaUlGTjxo0L+zwQCFjfvn2tYsWKVqRIEWvVqpWtXLkybJqNGzdat27drGTJkla6dGnr2bOnbdu27RCvSWIbOHCgnXjiiVaiRAkrV66cderUyVasWBE2za5du+yGG26www8/3IoXL26dO3e29evXh03z66+/WocOHaxo0aJuPnfddZft27fvEK9N4nrhhResfv36Li/q0bRpU5swYULq56Rx9njsscfc8ePWW28lrWPooYcecuka+qhZsyZpDOQgBI9AAhkzZozdfvvtbqiOb7/91ho0aGBt27a1DRs2xHvRcozt27e7dFMQnpZBgwbZsGHDbPjw4TZnzhwrVqyYS2NdhHsUOC5dutQmTZpk48ePdwHptddeewjXIvF99dVXLjD8+uuvXTrt3bvX2rRp49Lfc9ttt9nHH39s7777rpt+zZo1dsEFF6R+vn//fhc47tmzx2bNmmX/+9//bOTIkS64R1DlypVdIDN//nz75ptv7KyzzrKOHTu6/EkaZ4958+bZiy++6IL2UOTn2KhTp46tXbs29TFjxgzSGMhJAgASxkknnRS44YYbUl/v378/UKlSpcDAgQPjulw5lQ5xY8eOTX2dkpISqFChQmDw4MGp723atClQqFChwNtvv+1ef//99+578+bNS51mwoQJgaSkpMAff/xxiNcg59iwYYNLt6+++io1XQsUKBB49913U6dZtmyZm2b27Nnu9aeffhrIly9fYN26danTvPDCC4GSJUsGdu/eHYe1yBnKlCkTePnll0njbLB169bAcccdF5g0aVLg9NNPD9xyyy3uffJzbDz44IOBBg0apPkZaQzkDJQ8AglCpS8qXVA1Sk++fPnc69mzZ8d12XKLVatW2bp168LSuFSpUq56sJfG+q+qqk2aNEmdRtNrW6ikEmnbvHmz+3/YYYe5/8rLKo0MTWtVT6tatWpYWterV8/Kly+fOo1Kgbds2ZJasgYLK6kdPXq0K91V9VXSOPZUmq7S8NB8S36OLTUTULOCo48+2tXyUNV10hjIOZLjvQAAgv766y93cRh6IS16vXz5cpIpBhQ4emkamcbeZ/qvtnehkpOTXVDkTYNwKSkprm3YqaeeanXr1k1Nx4IFC7pAPKO0TmtbhG4rmC1evNgFi6parbajY8eOtdq1a9vChQtJ4xhSYK7mAqq2Gon8HBu6Uaeq6TVq1HBVVvv162fNmze3JUuWkMZADkHwCAA46NIaXfyFtl1C7OhCW4GiSnffe+896969u2tDitj57bff7JZbbnHtd9VZGbLH2WefnfpcbUoVTFarVs3eeecd14EZgMRHtVUgQZQtW9by589/QG+Uel2hQoW4LVdu4qVjRmms/5EdFKn3T/XAynY40I033ug6Ffryyy9d5y6haa2q2Js2bcowrdPaFqHbCuZKF4899lhr3Lix6+VWHUINHTqUNI4hVQHWft+oUSNX00APBejqXEvPVSJOfo491Uw4/vjj7ccffyQ/AzkEwSOQQBeIujicPHlyWHVAvVaVNRy86tWruwuU0DRW+zq1ZfTSWP8V8Ohi0jNlyhS3LXSXHEHqj0iBo6pQKn2UtqGUlwsUKBCW1hrKQ+2bQtNaVTJDg3WV/GhIClXLRNqUF3fv3k0ax1DLli1dXlQJr/dQu2e1yfOek59jT0Mg/fTTT27oJI4ZQA4R7x57APxn9OjRrufPkSNHul4/r7322kDp0qXDeqOEf2+JCxYscA8d4p566in3fPXq1e7zxx57zKXphx9+GFi0aFGgY8eOgerVqwd27tyZOo927doFTjjhhMCcOXMCM2bMcL0vdu3alaQP0atXr0CpUqUCU6dODaxduzb1sWPHjtRprrvuukDVqlUDU6ZMCXzzzTeBpk2buodn3759gbp16wbatGkTWLhwYeCzzz4LHHHEEYE+ffqQ1v+65557XA+2q1atcvlVr9Xz78SJE0njbBba2yr5OTbuuOMOd8xQfp45c2agVatWgbJly7remkljIGcgeAQSzDPPPOMuuAsWLOiG7vj666/jvUg5ypdffumCxshH9+7dU4freOCBBwLly5d3gXrLli0DK1asCJvH33//7YLF4sWLu2EjrrrqKheU4j9ppbEer732Wuo0Csivv/56N7RE0aJFA+eff74LMEP98ssvgbPPPjtQpEgRdxGpi8u9e/eS1P/q0aNHoFq1au54oMBa+dULHEnjQxs8kp8P3sUXXxyoWLGiy89HHnmke/3jjz+SxkAOkqQ/8S79BAAAAAAkNto8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAAAAAHwRPAIAAAAAfBE8AgAAAAB8ETwCAJCNRo4caaVLlyaNAQA5HsEjACAhrFu3zm655RY79thjrXDhwla+fHk79dRT7YUXXrAdO3ZYTnDUUUfZkCFDwt67+OKL7YcffojbMgEAECvJMZsTAABZ9PPPP7tAUSV0jz76qNWrV88KFSpkixcvthEjRtiRRx5p5513XlzSNxAI2P79+y05OWunzCJFirgHAAA5HSWPAIC4u/76611w9s0339hFF11ktWrVsqOPPto6duxon3zyiZ177rluuk2bNtnVV19tRxxxhJUsWdLOOuss++6771Ln89BDD1nDhg3tjTfecKWApUqVsksuucS2bt2aOk1KSooNHDjQqlev7oK6Bg0a2HvvvZf6+dSpUy0pKckmTJhgjRs3dkHsjBkz7KeffnLLoxLR4sWL24knnmhffPFF6vfOOOMMW716td12223u+3qkV21VpanHHHOMFSxY0GrUqOGWN5S++/LLL9v5559vRYsWteOOO84++uijbEh5AACiR/AIAIirv//+2yZOnGg33HCDFStWLM1pvECsS5cutmHDBhfYzZ8/3xo1amQtW7a0jRs3pk6rIG/cuHE2fvx49/jqq6/sscceS/1cgePrr79uw4cPt6VLl7pg77LLLnPThbrnnnvc95YtW2b169e3bdu2Wfv27W3y5Mm2YMECa9eunQtqf/31Vzf9Bx98YJUrV7b+/fvb2rVr3SMtY8eOddVz77jjDluyZIn93//9n1111VX25Zdfhk3Xr18/F0gvWrTI/W63bt3C1hMAgEMuAABAHH399dcBnY4++OCDsPcPP/zwQLFixdyjd+/egenTpwdKliwZ2LVrV9h0xxxzTODFF190zx988MFA0aJFA1u2bEn9/K677gqcfPLJ7rm+q89nzZoVNo+ePXsGunbt6p5/+eWXbnnGjRvnu+x16tQJPPPMM6mvq1WrFnj66afDpnnttdcCpUqVSn3drFmzwDXXXBM2TZcuXQLt27dPfa3fv//++1Nfb9u2zb03YcIE32UCACC70OYRAJCQ5s6d66qYqsRt9+7drnqqSv8OP/zwsOl27tzpShs9qq5aokSJ1NcVK1Z0pZXy448/us53WrduHTaPPXv22AknnBD2XpMmTcJe67dVLVbVaFWquG/fPvfbXsljtFSSee2114a9p/aeQ4cODXtPpZ0elciqmq63HgAAxAPBIwAgrtS7qqqlrlixIux9tXkUr7MZBW8KBNUmMVJom8ICBQqEfaZ5Kwj15iEKANUJTyi1bQwVWYX2zjvvtEmTJtkTTzzhllnLdeGFF7rAMztktB4AAMQDwSMAIK5UkqiSwGeffdZuuummdNs9qn2jhvNQxzoqXcyK2rVruyBRpYWnn356pr47c+ZMu/LKK10nNl4g+ssvv4RNow5w1DNrRtQZkObVvXv3sHlr2QAASGQEjwCAuHv++edd1U1VFVXVUFXZzJcvn82bN8+WL1/uej1t1aqVNW3a1Dp16mSDBg2y448/3tasWeNKERXQRVYzTYuqs6oEUZ3kqBTvtNNOs82bN7vgTdVCQwO6SOrxVJ3iqJMclQI+8MADB5QEKqidNm2a6+FVQWrZsmUPmM9dd93lOsJRNVmt08cff+zmG9pzKwAAiYjgEQAQdxq2Qj2YaozHPn362O+//+6CL5XGKdjTUB4K2D799FO77777XO+kf/75p1WoUMFatGjhhs+I1oABA9xQH+p1VeNLqsqrSjXvvffeDL/31FNPWY8ePaxZs2YuKLz77rtty5YtYdOop1X1nqr1UTvNYN834RT8qn2jqr+q11UNGfLaa6+5oT4AAEhkSeo1J94LAQAAAABIbIzzCAAAAADwRfAIAAAAAPBF8AgAAAAA8EXwCAAAAADwRfAIAAAAAPBF8AgAAAAA8EXwCAAAAADwRfAIAAAAAPBF8AgAAAAA8EXwCAAAAADwRfAIAAAAAPBF8AgAAAAAMD//D8K36f98lHspAAAAAElFTkSuQmCC",
      "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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",
      "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",
    "# Evolution 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 oneEvolutionaryProcessk5( 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 Evolutionary 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 = oneEvolutionaryProcessk5(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": [
    "*Observation by the AI of Visual Studio Code.*\n",
    ">\n",
    "> The heights of the plateaus are very similar for all the population sizes, and they are all around 0.5, which means that the best fitness is around half of the length of the target sequence. This suggests that the population size does not have a significant impact on the height of the plateau.\n",
    "\n",
    "*Our Observations for a task 10000 bits long.*\n",
    "\n",
    "1. Randomness + MAX create populations whose proportion of solved sizes is above the half. \n",
    "2. With a population size of 10, the proportion of solved sites in the initial population is 0.6 and with 20, it is 0.64. This means that more than 6000 sites are solved with so small populations.\n",
    "3. The marginal gain of solved sites rapidly diminishes and with 5000 chromosomes, it reaches 0.75 and tends to a minimal grow.  The curves seem to stack up around 0.75. \n",
    "4. The graph of the reached proportion of solved sites or the height of the plateau against population size is reminiscent of a square root and a logarithmic function. \n",
    "\n",
    "*Outputs are very stable.*\n",
    "\n",
    "In one experiment, the output was: \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",
    "In another experiment, the output was the following:\n",
    "\n",
    "Populations sizes and normalized realized plateau height.    \n",
    "population size 10: 0.6145.     \n",
    "population size 30: 0.6388.     \n",
    "population size 50: 0.6562.     \n",
    "population size 300: 0.6975.     \n",
    "population size 500: 0.7117.     \n",
    "population size 2000: 0.7364.     \n",
    "population size 3000: 0.7382.     \n",
    "population size 4000: 0.747.     \n",
    "population size 5000: 0.7494.     \n",
    "\n",
    "For the exercises below, you must choose one of these two outputs.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Exercise.** Test a square root model to fit the data of the graph of Height of the plateau vs population size.\n",
    ">\n",
    "> *Answer.* The following code test a power model $y = a x^b$ to fit the data of the graph of Height of the plateau vs population size. The expected power $b$ must be around 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cf1a8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#program powerModelForNormalizedPlateauHeights.py\n",
    "# Evolution 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": [
    "*Our Observation.* The model produces a very good fit. The power is not a square root. It is 0.0319.\n",
    "\n",
    "**Exercise.** Test a log model $y = a + b \\log x$ to fit the data of the graph of Height of the plateau vs population size.\n",
    ">\n",
    ">*Answer.* The following code tests a log model $y = a + b \\log x$ to fit the data of the graph of Height of the plateau vs population size. "
   ]
  },
  {
   "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",
    "# Evolution 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": [
    "*Our Observation.* The log model produces a very good fitting.\n",
    "\n",
    "We have two models that fit data quite well, the power and the logarithmic models. They are based on observation, more exactly, on the shape of the graph of observed normalized plateau heights vs population size. Observation is the first step of the Scientific Method. Next, we need mathematical models to explain observations. Nevertheless, are we forgiving that we already have a theory to understand Evolution?\n",
    "\n",
    "**Exercise.** The fundament of Evolution is the selection of the fittest, a procedure that is modelled by MAX. Use MAX to propose a model for the dependency of the observed normalized plateau heights on population size. Test your model.\n",
    "\n",
    "*Answer.*  The selection of the fittest under recursive variability for our problem of matching a binary string of length $H$ is modelled by MAX. This procedure takes the max of $H$ binomial distributions $Bi(H, 1/2)$. This means, that Evolution takes the best chromosome, which is that in which the number of solved sites is maximal, given that randomness posits in each site a 1 with probability $1/2$ and a 0 with the same probability.  So, let us explore a simple directive: Evolution cannot go beyond the power of MAX. The mean value of MAX is proportional to the square root of the log of the population size $x$. That is, $\\overline{max} = k \\sqrt{\\log x} $. This allows to propose that the Normalized height of the plateau must follow a square root log model: $y = a + b \\sqrt{\\log (x)} $. We need to test this model.  \n",
    ">\n",
    ">The following code tests the model that proposes that the Normalized height of the plateau $y$ must follow a square root log model: $y = a + b \\sqrt{\\log (x)} $, where $x$ is the population size."
   ]
  },
  {
   "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",
    "# Evolution 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": [
    "*Observation.* The square-root-model is very good.\n",
    "\n",
    "**Exercise.** We have tested three models and all of them are good. Formulate a quantitative criterion to decide which is the best and implement it. \n",
    ">\n",
    "> *Answer.* Our models are functions defined over the same set, which is moreover discrete. So, they look like the coordinates of a vector or array. To measure the difference, we use the Cartesian distance between vectors. "
   ]
  },
  {
   "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",
    "# Evolution 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": [
    "*Our observation.* We compare three models of the Normalized Plateau Heights as function of population size, a Power Model, a Log Model and a Square-root-of_log Model. By the graphic, the best model is the Square-root-of_log Model because its fitting is the best as for low population sizes as for high ones. The others two seem to produce predictions far above observed ones for large value of population size. This subjective impression agrees with the outputs of the objective measure that is the square root of the sum of deviations squared for each point. The error produced by the Square-root-of_log Model is 0.0105 while for the Log Model is 0.0202 and for the power model is 0.0241. So, the precision of the Square-root-of_log Model is very high and doubles that of the other two.\n",
    "\n",
    "**Exercise.** A very simple use of MAX has allowed us to propose a successful model of one observable of Evolution. But, the posed problem is complicated as well as Evolution. Those complications are captured by the Underdog Model, which is by far much more complicated than MAX. Nevertheless, a direct application of MAX has been successful. Explain this mystery, i.e., find the prediction of the Underdog Model, and prove that with some approximation, the model just proposed as a direct application of MAX is recovered. \n",
    ">\n",
    ">*Answer.* The prediction of the Underdog Model and its test are found below. \n",
    "\n",
    "**Exercise.** Calculate what is the cost of getting perfection, i.e., of reaching a normalized plateau height of 1.\n",
    ">\n",
    "> *Answer.* We use the Square-root-of_log Model to calculate the prediction of the cost of perfection for a task of matching 10000 binary sites. It says that the normalized plateau height $y$ as a function of the population size $x$ is\n",
    ">>\n",
    ">> $ y = 0.4591 + 0.0997 * [(log(x))** 0.5]$\n",
    ">>\n",
    "> Thus, if $y = 1$, \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",
    "So, to match 10000 bits, we need $6 \\times 10^{12} $ chromosomes. This is no problem for a super computer. \n",
    "\n",
    "**Challenge.** Use MAX to prove that the cost of perfection for Evolution, the needed population size, is an exponential function of the length of the input, i.e., of the complexity level.  We say simply: the cost of perfection for Evolution is exponential in the length of the input.\n",
    "\n",
    "**Exercise.** According to what you see in this contribution, What prediction of the Evolutionary Theory is a blatant failure and converts it into an obviously false theory? \n",
    ">\n",
    "> *Answer.* We see here that \n",
    ">\n",
    ">> 1. Evolution is very efficient to solve very simple problems, and to produce rough approximations of complex ones.  \n",
    ">> 2. Evolution can perfectly solve complex problems. But, \n",
    ">> 3. The cost of getting perfection involves an exponential function that must be growing in the complexity of the task. \n",
    ">> 4. If resources are not enough, the height of the realized plateau is somewhere in within mediocrity and perfection. \n",
    ">> 5. If time is unbounded, you can wait until a happy fluctuation solves your problem without the need of a large population size. When time is unbounded, this is reproducible. This also can happen in finite time but is not reproducible, is not scientific. If someone believes this, his or her faith is blind. \n",
    ">> 6. It is cheaper to produce short strings with function than large ones. Therefore, we expect the arising of a very rich supra-alphabet, with a very large quantity of symbols that are rather short binary chunks. If time is unbounded, those chunks may give rise to genes. If time is bounded, as in our present universe, Evolution will explore with its fluctuations the infinite world of low quality.  Thus, we have here a mandatory prediction: If we arouse by Evolution, then the world, the fossil record and your own body, all must be filled in products whose quality is in within mediocrity and perfection. To take a decision on the Evolutionary Theory, just look at your body. If it filled in myriads of defects of anatomy and physiology, the Evolutionary Theory is true. If you find only some hundreds of imperfections in your body, the Evolutionary Theory is false because imperfections are expected by myriads. Also, go to a paleontological museum. If it is filled with every kind of crooked skeletons, the Evolutionary Theory is true. But if you find fossils that marvel you and specialists, the Evolutionary Theory is false.\n",
    ">\n",
    "> In short: the mandatory prediction of the Evolutionary Theory is that the fossil record and our own body must be filled in myriads of defects, as anatomical as physiological. To take a decision, look at your own body. \n",
    "\n",
    "**Exercise.** Clearly and sharply explain why all Scientists consider that the Evolutionary Theory is a fact of Nature. Do you see an error somewhere?\n",
    ">\n",
    "> *Answer.* Various factors collaborate to push Scientists to believe that the Evolutionary Theory is a fact of Nature, i.e., that all species arouse by Evolution:\n",
    ">> 1. [The creation of a new species of butterfly by hybridization of two mother species has been reproduced in the lab](https://doi.org/10.1038/nature04738). This means that hybrids are fertile and can reproduce among themselves and that crosses of the new species with anyone of the mother species are infertile and/or that these are prevented by species specific signals. [It seems that wing coloration is not enough to prevent courtship](https://doi.org/10.1038/s41586-021-03330-8), so ethological and biochemical signaling might be decisive. The error is to imagine that this is a proof of the Evolutionary Theory. This is false because the numbers of species of vertebrates and of plants are of the order of millions, and of microorganisms in the order of hundreds of millions. So, the common sense predicts thousands of reproducible speciation events and that each girl must know how to make new species of plants at home. False. If it is recognized that there is a battle against complexity, and that Evolution needs time, the corresponding experiments must be carried out, for instance, chimps must be selected in the zoo to walk upright. This must be done. \n",
    ">> 2. According to the results that we see here, Evolution matches more than 6000 bits of 10000 almost instantaneously with just a population size of 10. This is simply astonishing! This implies that if life arouse by Evolution, there must not exist fossil record of the battle of Evolution against complexity from nothing to something above mediocrity. Victorious results as these, referring to diverse fields of biology, are reported with relative high frequency.  Example: [The biomorphs of Dawkins](https://cs.lmu.edu/~ray/notes/biomorphs/), that shows that ideograms of morphology variation can be generated by very simply algorithms. In general, it seems to me that the power of Applied Evolution, both in silico and in nature, is constrained only by our imagination. Plus the burden of complexity. \n",
    ">> 3. High-complexity instances of hard problems are undertaken by routine and proven to be solvable by Evolution, for instance, [the creation by Evolution of software and hardware](https://link.springer.com/journal/10710). What one must imagine is that Evolution can solve any instance if enough time is granted to it. This might be true, but the error is to ignore the fulfillment of a law: huge volumes of garbage must be produced when Evolution battles against complexity. And, what is Evolutionary Garbage? It is the generality of individuals with myriads of every sort of malformations and malfunctions. There is no exception to this rule. Not even one. That is why the Evolutionary Theory is obviously false.\n",
    "\n",
    "\n",
    "**Exercise.** Answer to the objection against the claim that Evolution has an exponential cost in the length of the binary target string.  \n",
    ">\n",
    ">> The proposed claim about the Evolutionary Theory rests on experimental evidence, which was produced by simulations of Evolution, by Genetic Algorithms. So, it is a statistical extrapolation. Now, every statistical generalization has a very restricted range of validity because very soon, the uncertainty is so large that anything becomes possible, just as if no experiment were made.\n",
    ">\n",
    "> *Answer.* The objection is right. Extrapolations that are based on statistical generalizations have a very limited range of validity because the uncertainty grows very rapidly.  That is why mathematical models are needed to make extrapolations.\n",
    "\n",
    "Let us turn to our math model.\n",
    "\n",
    "**Exercise.** Use the Underdog Model to prove that the cost of Evolution is exponential in the complexity of the task, i.e., in the length of the target binary string.\n",
    ">\n",
    "*Answer.* In the next section, we review our math model based on MAX, and then we prove the assertion of the exponential cost. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5544a45a",
   "metadata": {},
   "source": [
    "# THE UNDERDOG MODEL\n",
    "\n",
    "Our purpose is to understand Evolution. This means to build mathematical models that capture its essence predicting its behavior in its battle against complexity. To that aim, we have given to Evolution the task of solving the *Binary Shakespeare Challenge*, which consists in matching a binary target string of length $H$.  This length faithfully represents complexity because the higher it is, the more resources needs Evolution to complete the task. Evolution uses the population and time. The population permits to make experiments in parallel, the time allows making experiments serially.  We have modelled the Evolution to solve this challenge by the Mutation-MAX Duo, which is a weak version of Evolution and consists of Selection of the fittest, modeled by MAX, followed by its cloning to generate the next generation, and mutation to create variability. Our model does not contain recombination. We would say that recombination shuffles the cards, adding more variability to that produced by mutation, and curing the homogenization caused by cloning of the fittest. Nevertheless, we have been unable to detect a possible important role of recombination.\n",
    "\n",
    "Our mathematical model of Evolution explains the macro-observables using micro-observations. Our macro-variable has been the number of solved sites that represents the portion of the solved task and that grows with generations. Evolution gathers small progress of this variable generation after generation.  The micro-observations register the fluctuations that are created by mutation due to its stochastic nature, where a fluctuation is a deviation off the mean or expected value of the given observable. The change of the macro-observable is a reflection of the micro-observables. Or, *The fuel of Evolution consists of fluctuations*. \n",
    "\n",
    "The fluctuations can be positive or negative and the Underdog Model says that Evolution runs on both types, not only positive. The relation changes over time, over generations, but it seems to us that the dominant behavior is sufficient to produce good predictions.  Accepting this, the micro-observations are included with the following flavors: \n",
    "\n",
    " 1. The fluctuation in the number of touched by mutation sites are very negative. \n",
    " 2. The fluctuations in the number of touched and solved sites are equilibrated, positive and negative fluctuations play a similar role in the plateau. In the ramp are positive. \n",
    " 3. The fluctuation in the number of untouched by mutation sites are positive except at the beginning.\n",
    " 4. The fluctuations in the number of untouched and solved sites are very positive. We have super powerful positive fluctuations. \n",
    "\n",
    "Moreover, if the tendency is to go downward, the larger is the population size, the deeper is the fall. If, by contrary, the tendency is to go upward, the larger is the population size, the higher is the rising. This means that all fluctuations, positive or negative, are connected to MAX and to the number of monkeys. \n",
    "\n",
    "\n",
    "The specifications of our model are the following:\n",
    "\n",
    "**********\n",
    "THE UNDERDOG MODEL\n",
    "**********\n",
    "\n",
    "\n",
    "We divide the process of mutation in two stages. In the first, sites are touched or not by mutation. In the second, mutation posits a one or a zero at random in each touched site. \n",
    "\n",
    "The underdog model is defined by the following constraints:\n",
    "\n",
    "> $\\mu = $ mutation rate, constant\n",
    ">\n",
    "> $H = $ length of the target string, total number of sites. The target string consists of $H$ ones. \n",
    ">\n",
    "> $m = $ 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 in the fittest 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",
    "> $I$ is an observable, whose predicted value can be tested. Because each chromosome is a binary string and created at random with equals probabilities of 1 and 0, the half of sites is expected to be solved. This is represented by $\\frac{H}{2}$. The other term corresponds to the MAX-imal value due to the variation of solved sites among the population of chromosomes. This is the first effect of fluctuations, a population effect that depends on  $m$, the number of chromosomes. These fluctuations are positive.\n",
    ">\n",
    "\n",
    "The second observable prediction is the number of touched sites, $T$. If the mutation rate is $\\mu$, and the length of the target string is $H$, the expected value of $T$ is $T = \\mu H$. But there is variability among the population. Max chooses the best, which may differ from the expected value. The expected value of $T$ according to Max is constant and equal to\n",
    ">\n",
    "> $T= $ number of touched sites $ = \\mu H +  \\lambda_T \\sqrt{H \\mu (1 - \\mu) } \\sqrt{ \\ln (m)} $\n",
    ">\n",
    "> where the second term represents the population effect picked up by MAX.\n",
    "> \n",
    "The third observable is  $ 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",
    "> Next, we have:\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",
    "We also have:\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",
    "> We used an approximation of $r$ in the previous work because the resultant model is easily solvable. Let us ignore it for the moment. \n",
    ">\n",
    "> Finally, 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 to specify are 4: $\\lambda_k$, $\\lambda_T$, $\\lambda_s$, $\\lambda_r$. An assignation has already been considered.\n",
    "\n",
    "**********\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99958924",
   "metadata": {},
   "source": [
    "\n",
    "## Predicting the cost of perfection\n",
    "\n",
    "According to the results of our simulations, when one augments the number of chromosomes, the curves describing the proportion of solved or matched sites pile up around 0.75. Thus, perfection seems unreachable. Contrary to this appreciation, we think that Evolution can reach perfection but with an exponential cost. This is shown as follows:\n",
    "\n",
    "**Theorem.** The cost that Evolution must pay for perfection is exponential in the length of the binary target string.\n",
    ">\n",
    "> *Proof.* Perfection must be the stable state of the Evolutionary Process and means that all sites $H$ are solved, i.e., $M_t = M_{t+1} = H$, and that the fraction of solved sites $p_t = \\frac{M_t}{H} = 1$. Hence, the equation\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)} $.\n",
    "\n",
    "becomes\n",
    "\n",
    "$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$ is the number of sites that are touched by mutation. It is expected value is $T = \\mu H$, where $\\mu$ is the mutation rate per site, and $H$ is the complexity of the task, which is given by the length of the target binary string. Therefore, \n",
    "\n",
    "> The expected population size $m$ to achieve perfection is\n",
    ">>\n",
    ">> $  m   = e^{\\frac{ \\mu H}{4 (\\lambda_s)^2 }} $\n",
    ">>\n",
    "This equation says that the cost of perfection in an exponential function of the complexity. \n",
    "\n",
    "**Exercise.** Prove that the cost of imperfection is also exponential.\n",
    ">\n",
    "> *Answer.* An exact solution to this question would require to solve the system of recursive equations of the Underdog Model. This would say what is the value of $M_t$ for each generation $t$ in terms of the initial condition and the parameters of the model. We do not find that realizable. But, we can pose an upper bound to the possibilities of Evolution. So, let us try an approximation: let us replace the fraction of solved sites $p_t$ by $1/2$, that gives to the expression $p(1-p)$ the maximal value. Thus,\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)} $.\n",
    "\n",
    "becomes\n",
    "\n",
    "$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",
    "When one decides to get satisfied with imperfection, one declares that $M =\\omega H $ where $\\omega $ is the target fraction of perfection, say, 0.9.  We have:\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",
    "This equation says that if you want to surpass randomness, you must pay an exponential cost in the complexity of the task. \n",
    "\n",
    "Also, taking derivatives to both sides with respect to $\\omega$: If you want to improve the quality of your solution, to rise the height of a plateau, you must pay an exponential cost in the length of the target binary string. \n",
    "\n",
    "**Exercise.** Explain the scientific meaning of the exponential cost that Evolution must pay to reach perfection and to rise quality.\n",
    ">\n",
    "> *Answer.* The Evolutionary Theory says that Evolution is the creator of every species on Earth. The exponential cost of any quality above randomness means that the Evolutionary Theory is scientific. That is: it produces mandatory predictions whose violation implies that the ET is irremediably false. The most direct one is the following: \n",
    ">\n",
    ">> If a species appeared by Evolution, apart from some cases, there shall exist tons of Evolutionary Garbage that witness a tremendous battle of Evolution against complexity. Or, maybe somebody thinks that it is an easy affair to make a new species with morphological divergence? Now, what is Evolutionary Garbage? It is the totality of individuals that Evolution used to try to do something better. Thus, the Evolutionary theory has a mandatory prediction: the fossil record and our own bodies must be filled in every kind of malformations and malfunctions. To know which species were created by Evolution, go to the museum. If you see a skeleton filled in myriads of malformations, it represents a species that arouse by Evolution. By the same token, if your skeleton has thousands of malformations, and your physiology millions of failures, let me advise you to believe that your species appeared by Evolution and that the Evolutionary Theory is the true explanation of your existence. Otherwise, the Evolutionary Theory is false and Evolution is not your creator.\n",
    "\n",
    "**Exercise.** The Scientific Method says that Evolutionary Theory is false. Briefly express whether you accept this verdict and what you believe in.\n",
    ">\n",
    "> *Answer.* I believe that the Scientific Method is a tool for rejecting theories but not for constructing them. However, we love to invent theories and explanations and reject a belief only when it is convenient for us. In my particular case, its rejection is convenient to me because this satisfies my intuition, and, moreover, because I am Christian and firmly believe that the biblical God created me with an extremely great effort, and that in practical terms\n",
    ">>\n",
    ">>> **The Kingdom of God is less judgment and more salvation**, because Jesus said: *\"If anyone hears my words but does not keep them, I do not judge that person. For I did not come to judge the world, but to save the world [John 12:47]\"*.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612105f6",
   "metadata": {},
   "source": [
    "## Specifying the Underdog Model\n",
    "\n",
    "Our model has parameters that must be specified.  We already have proposed some assignations, but we can use our present simulations to try to give better values. \n",
    "\n",
    "**Concourse.** Use simulations to find an assignation of parameters of the Underdog Model with as much range of validity as possible.\n",
    "\n",
    "*We inaugurate* this concourse with an estimation of one parameter.  We restrain ourselves here to constant assignations, but most possibly, parameters must be functions of the population size and complexity. \n",
    "\n",
    "**Estimation of $\\lambda_s$**. To propose an estimation of the parameter $\\lambda_s$, we can compare two results that refer to the cost of perfection. The first is theoretical, the second is statistical. \n",
    "\n",
    "*Theoretical result.*\n",
    ">\n",
    "> The expected population size $m$ to achieve perfection, i.e., all $H$ binary sites are solved, is\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",
    "Taking logs at both sides:\n",
    ">>\n",
    ">> $ \\log (m)  = \\log (e^{ \\frac{100}{4 (\\lambda_s)^2}}) = \\frac{100}{4 (\\lambda_s)^2}$\n",
    ">>\n",
    "\n",
    "*Statistical Result.*\n",
    ">\n",
    "> We used the Square-root-of-log Model to calculate the prediction of the cost of perfection for a task of matching 10000 binary sites. It says that the normalized plateau height $y$ as a function of the population size $x$ is\n",
    ">>\n",
    ">> $ y = 0.4591 + 0.0997 * [(log(x))** 0.5]$\n",
    ">>\n",
    "Rewriting this result, using the nomenclature of the theoretical model, we get:\n",
    ">>\n",
    ">> $ \\frac{M}{H} = 0.4591 + 0.0997 * [(\\log(m))** 0.5]$\n",
    ">>\n",
    "The condition for perfection is $M = H$, or $ \\frac{M}{H} = 1$. So,\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",
    "*Equating both results, the theoretical with the statistical, we have:*\n",
    ">>\n",
    ">> $ \\log (m) = \\frac{100}{4 (\\lambda_s)^2} = 29.4337$\n",
    ">>\n",
    "Solving for $\\lambda_s$, we obtain an estimation for our parameter:\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",
    "\n",
    "### Prediction of the height of the plateau\n",
    "\n",
    "The Underdog Model can predict the dependency of the height of the plateau as function of the length of the target string and the population size. This can be done as follows. We have the equation\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",
    "Since, $p_t = \\frac{M_t}{H} $, this equation becomes the recurrent relation\n",
    "\n",
    "$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)} $.\n",
    "\n",
    "If the plateau is not an artifact of our imagination that confounds a lowly increasing function with a plateau, it is defined by\n",
    "\n",
    "$M_{t+1} = M_{t} $,\n",
    "\n",
    "that is\n",
    "\n",
    "$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",
    "**Exercise.** Ask your AI Assistant to solve $M_t$ from the anterior equation, digest and report the solution.\n",
    ">\n",
    "> *Answer.* The answer by Gemini of Google was indeed very clear and well organized. Our understanding of its work is the following:  \n",
    "\n",
    "1. Reorganization:\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})} $.\n",
    "\n",
    "\n",
    "2. To make the algebra manageable, let us define the following constants:\n",
    "\n",
    "$ 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. With the use of these constants, the original equation becomes:\n",
    "\n",
    " $M_{t}=A+KM_{t}+L\\sqrt{KM_{t}(1-\\frac{M_{t}}{H})}$. \n",
    "\n",
    "4. To isolate the square root term, subtract $KM_{t}$ from both sides:\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. Notice that \n",
    "\n",
    "$1 - K = 1 - \\frac{H - T}{H} = \\frac{T}{H}$. \n",
    "\n",
    "Thus, \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",
    "Let $x = \\frac{M_t}{H}$, then  $M_t = Hx$. Substituting these, the equation becomes:\n",
    "\n",
    "$T  \\frac{M_{t}}{H} = Tx=A+L\\sqrt{KHx(1-x)} $\n",
    "\n",
    "$Tx-A=L\\sqrt{KHx(1-x)}$\n",
    "\n",
    "6. Square both sides and form a quadratic:\n",
    "\n",
    "$(Tx-A)^{2}=L^{2}KHx(1-x)$\n",
    "\n",
    "$T^{2}x^{2}-2ATx+A^{2}=L^{2}KHx-L^{2}KHx^{2}$\n",
    "\n",
    "Rearranging into standard quadratic form \n",
    "\n",
    "$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.  Let $B = L^2 K H $ \n",
    "\n",
    "Recalling that\n",
    "\n",
    "$L = \\lambda_r \\sqrt{\\ln m}$ \n",
    "\n",
    "and that\n",
    "\n",
    "$K = \\frac{H - T}{H}$\n",
    "\n",
    "we get\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. The quadratic formula becomes:\n",
    "\n",
    "$(T^{2}+B)x^{2}-(2AT+B)x+A^{2}=0$\n",
    "\n",
    " \n",
    "\n",
    "Solving for $x$, and using :\n",
    "\n",
    "$x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a}$\n",
    "\n",
    "$x = \\frac{-b \\pm \\sqrt{D}}{2a}$ (see below for $D$)\n",
    "\n",
    "$x = \\frac{(2AT+B) \\pm \\sqrt{D}}{2(T^{2}+B)}$\n",
    "\n",
    "with \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",
    "In short:\n",
    "\n",
    "$ D =  B(4AT-4A^{2} + B)$\n",
    "\n",
    "Recalling that \n",
    "\n",
    "$ A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $, \n",
    "\n",
    "the term $4 A T - 4 A^2 = 4A(T-A)$ simplifies significantly:\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",
    "Thus, \n",
    "\n",
    "$D = B (T^2 - 4 \\lambda_s^2 T \\ln m + B)$.\n",
    "\n",
    "9. Final Solution\n",
    "\n",
    "The solution of $M_t = H x$ is, therefore:\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",
    "Let us recall that\n",
    "\n",
    "$ A = \\frac{T}{2} + \\lambda_s \\sqrt{T \\ln m} $\n",
    "\n",
    " and \n",
    " \n",
    " $B = L^2 K H = \\lambda_r^2 (H - T) \\ln m$\n",
    "\n",
    " we finally obtain\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",
    "Guided by an analogy with sequential testing context, Gemini advices to take the positive root because it is the upper value. \n",
    "\n",
    "**Exercise.** Rewrite the found formula for the height of the plateau in a format appropriate for programming. \n",
    ">\n",
    "> *Answer.* A series of short formulas is more manageable than one single long one. The height of the plateau is determined according to the Underdog Model by:\n",
    ">\n",
    "> $\\mu = $ mutation rate, constant\n",
    ">\n",
    "> $H = $ length of the target string, total number of sites\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 $t$. The $M_t$ found here is the height of the plateau.\n",
    ">\n",
    "> $T= $ number of touched by mutation sites $ = \\mu H +  \\lambda_T \\sqrt{H \\mu (1 - \\mu) } \\sqrt{ \\ln (m)} $\n",
    ">\n",
    "> where the first term is the expected value, and the second represents the population effect picked up by MAX.\n",
    ">\n",
    ">$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",
    "> $D = B (T^2 - 4 \\lambda_s^2 T \\ln m + B)$.\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",
    "> We already know from the condition for perfection that \n",
    "> \n",
    "> $\\lambda_s = 0.9216$\n",
    ">\n",
    "> Recombination is ignored because we have not detected a significant role in the battle of Evolution against complexity. Anyway, recombination might be very important once genes are included because full functions now can be easily distributed among offspring. \n",
    "\n",
    "**Exercise.** Test the prediction of the Underdog Model about the height of the plateau as a function of population size and complexity.\n",
    ">\n",
    "> *Answer.* The output of a previous program for the normalized plateau height was\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",
    "The parameters were the usual ones in this contribution:\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",
    "The code that compares these observed results with those predicted by our formula is the following, in which the values of parameters were adjusted by hand, i.e., by Rudimentary Evolution.\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": [
    "*Our Observations.* \n",
    "1. The fit of predicted height of plateau to observed is very good, with a maximal error in absolute value of 5\\% obtained for a population size of 10. For all other values, the error is less than 2\\%. For high values of population size, the fit is very accurate. \n",
    "2. The good coincidence between observed and predicted induces the feeling of being in the right track, i.e., that the Underdog Model is the correct description of the work of Evolution that uses resources at hand to battle complexity.\n",
    "3. The value of $\\lambda_s = 0.805$ and $\\lambda_r = 0.009$ are less than 1.  Both refer to the weight of fluctuations created by the variability in the solved sites, the first in the sites that have been touched and solved by mutation, and the second for those that have not touched.  This shows that the most important channel for the action of MAX is through the positive fluctuations in the number of solved sites due to mutation. \n",
    "\n",
    "*Criticism.* When a model has many free parameters, many assignations can be found to fit a small set of data. This accusation attacks our model, unless it passes stringent tests. \n",
    "\n",
    "*Answer.* We accept the criticism. To diminish the freedom of free parameters, we can enlarge the data set.\n",
    "\n",
    "**Exercise.** Enlarge the range of values of the two main independent variables, population size and length of the target string, and see how good is the fitting of observed plateau heights by the prediction of the Underdog Model. \n",
    ">\n",
    "> *Answer.* In the next program, an Evolutionary Process is run for each pair of values, population size and length of target string, that are taken from a grid generated by two lists of values. The output is the height of the plateau, which is the last value of fitness when a reasonable number of generations has been run. The output of this program is processed for another one in the sequel to compare observed with predicted. There, we refine the assignation of values obtained by hand, i.e., by Rudimentary Evolution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 = 0.832\n",
      "Population size 5, Target length 1500: Normalized best fitness = 0.6966666666666667\n",
      "Population size 5, Target length 2500: Normalized best fitness = 0.6692\n",
      "Population size 5, Target length 5000: Normalized best fitness = 0.6148\n",
      "Population size 5, Target length 10000: Normalized best fitness = 0.5865\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 = 0.8773333333333333\n",
      "Population size 50, Target length 2500: Normalized best fitness = 0.7948\n",
      "Population size 50, Target length 5000: Normalized best fitness = 0.7178\n",
      "Population size 50, Target length 10000: Normalized best fitness = 0.6586\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 = 0.9333333333333333\n",
      "Population size 200, Target length 2500: Normalized best fitness = 0.8568\n",
      "Population size 200, Target length 5000: Normalized best fitness = 0.7668\n",
      "Population size 200, Target length 10000: Normalized best fitness = 0.6893\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 = 0.9586666666666667\n",
      "Population size 500, Target length 2500: Normalized best fitness = 0.8844\n",
      "Population size 500, Target length 5000: Normalized best fitness = 0.7928\n",
      "Population size 500, Target length 10000: Normalized best fitness = 0.7094\n",
      "Normalized plateau heights: [[np.float64(1.0), np.float64(0.832), np.float64(0.6966666666666667), np.float64(0.6692), np.float64(0.6148), np.float64(0.5865)], [np.float64(1.0), np.float64(1.0), np.float64(0.8773333333333333), np.float64(0.7948), np.float64(0.7178), np.float64(0.6586)], [np.float64(1.0), np.float64(1.0), np.float64(0.9333333333333333), np.float64(0.8568), np.float64(0.7668), np.float64(0.6893)], [np.float64(1.0), np.float64(1.0), np.float64(0.9586666666666667), np.float64(0.8844), np.float64(0.7928), np.float64(0.7094)]]\n"
     ]
    },
    {
     "data": {
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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: 0:33:36.531240\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 Evolutionary 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 oneEvolutionaryProcessk6(population_size, length_of_target_sequence, numberOfGenerations, target_sequence, mutation_rate, recombination_rate, alphabet):\n",
    "    \"\"\"Runs one evolutionary 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 Evolutionary 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 Evolutionary 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 = oneEvolutionaryProcessk6(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": [
    "*Observation.* The tendency of normalized plateau heights for different lengths of target sequences is repetitive. Such tendency denotes the battle of Evolution against complexity: there is a balance of resources and complexity. For a given complexity, if the resources augment, the height of the plateau also augments but very slowly. If the complexity augments, the whole behavior is pushed down: complexity smashes the available resources against the floor given by mediocrity and not so slowly.\n",
    "\n",
    "**Challenge.** Convert the anterior observation into an equation that approximately describes the effect of complexity over resources that are available to Evolution. Would you deduce such equation from the Underdog Model? \n",
    "\n",
    "**Comparison between observed and predicted.** The code that compares these observed results with those predicted by the Underdog Model, as given by the just ound formula, is the following, in which the values of parameters were adjusted by hand, i.e., by Rudimentary Evolution.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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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",
      "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 evolutionary 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": [
    "*Our observations.*\n",
    "1. Our formula produces intermediate imaginary values for low population size numbers because of the square root of a negative value. To remedy this, we redefined it as zero. \n",
    "2. Anyway, our formula predicts a value of the height of the plateau greater than 1. So, the height was trimmed to 1. \n",
    "3. Our values for the parameters for this program were $\\lambda_s = 0.7$ and $\\lambda_r = 0.015$, which were different from those found in a previous program $\\lambda_s = 0.805$ and $\\lambda_r = 0.009$. The difference is not that large, but it is worrisome to see that the optimal values wander from one experimental setting to the other. \n",
    "4. Our failure with low population size numbers points to an earlier problem of the same nature that was resolved with varying coefficients. \n",
    "5. The variance was high for small population sizes and low otherwise. \n",
    "6. Predictions and observations agree to a good degree. A wide range of combinations has been used to diminish the probability of fitting by mere luck. It seems, nevertheless, that the fitting can be improved."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1193a104",
   "metadata": {},
   "source": [
    "**Exercise.** Compare the observed best-fitness-histories with those predicted by the Underdog Model with the purpose of finding the best assignation of the parameters of the Underdog Model. Use changes by hand (Rudimentary Evolution) or Evolution to the full. To that aim, you must diminish the total error of predictions with respect to what is observed in simulations. So, you must test different assignations to the parameters of the Underdog Model and choose the best. If you choose Evolution, that will be done automatic and recursively. \n",
    "> \n",
    ">*Answer.* We choose to use Evolution to refine the assignation of values to the four parameters of the Underdog Model. \n",
    ">\n",
    "> Our commitment is to understand Evolution, specifically, how resources are used to battle complexity. Resources come in the form of population size and generations of the Evolutionary Process, while complexity is given by the task of matching a target binary string of a predefined length. The larger is the length, the higher is the cost in generations to reach to a stable state, which may be different from perfection. If resources are augmented, the stable state or plateau is higher. Perfection can be found, but the cost is exponential in the length of the input. We have developed and tested the Underdog Model of the battle of Evolution against complexity, whose distinctive ingredient consist of fluctuations, of deviations off the mean value of diverse observables. We have found that the predictions of this model are quite good, but four parameters received assignations that were done by a semi-empirical approach, part theory, part trial and error. This process is tedious and is no more than Rudimentary Evolution. So, here, we ask Evolution for help, which is the expert in trial and error. This is done in the following program.  Former work has shown that Evolution can produce poor results against high complexity. But, here, the graph of the best fitness along an Evolutionary Process is rather simple. Thus, our bet is that Evolution would help us in this case. \n",
    "> \n",
    "> To prevent a good fitting due to a small test set, we use diverse combinations of the two aforementioned independent variables (population size, length of target sequence), just as it was done in the previous program.  Each couple produces an observed fitness history plus a predicted fitness history, which the correspondent differences. This measures the discrepancy between observed and predicted by the Underdog Model. The error is defined as the sum point by point of the absolute value of the difference between observed and predicted by the Underdog Model. We sum over all couples to get a total error for a given assignation. Assignations compete to diminish this error.  The process is time-consuming, so for the master optimizing routine, which is responsible for the optimization of assignations, we use a population size of 10 chromosomes and run each ensuing genetic algorithm during 100 generations. \n",
    "> \n",
    "> The resultant code will be complex because we need to assemble together various pieces of previous works. To begin with, we need an encoder-decoder pair, specifically for numbers of the form $1.23456789$, which corresponds with the form of all found values of the parameters, which all spin around 1. The corresponding snippet follows:\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": [
    "**Exercise.** Run the previous program and verify that it is correct. \n",
    "\n",
    "*The program.* After many trials and changes, the complete program that Evolution uses to optimize parameter assignments is as follows. It takes 40 GB of RAM in a desktop for this program to run smoothly. We recommend using Colab. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8cd09d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lab: Two Genetic Algorithms, one master, the other slave\n",
      "Purpose: Use la Evolución 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 1782231039.676256\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: 528.3088\n",
      "masterGAjk10**\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 1,   best chromosome: 0905357460113578166907800000000015000000, smallest error: 528.3087930029077\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 2,   best chromosome: 0905357460013578166907800000000015000000, smallest error: 478.7776136079381\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 3,   best chromosome: 0905357460013578166907800000000015000000, smallest error: 478.7776136079381\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 4,   best chromosome: 0905357460013578166907800000000015000000, smallest error: 478.7776136079381\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 5,   best chromosome: 0905357460013578166907800000000015000000, smallest error: 478.7776136079381\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 6,   best chromosome: 1905357460013578166907300000000015000000, smallest error: 405.56327213887977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 7,   best chromosome: 0905357460013578166907300000000015000000, smallest error: 341.1022430870018\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 8,   best chromosome: 0905357460013578166907300000000015000000, smallest error: 341.1022430870018\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 9,   best chromosome: 0905357460013578166907300000000015000000, smallest error: 341.1022430870018\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 10,   best chromosome: 0905351460013578166907300000000015000000, smallest error: 341.1018727679416\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 11,   best chromosome: 1905351460013578166907300000000015000000, smallest error: 405.56287564275067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 12,   best chromosome: 0905351460013578166907300000000015000060, smallest error: 341.1020320763221\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 13,   best chromosome: 0905351460013578166907300000000015000060, smallest error: 341.1020320763221\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 14,   best chromosome: 0905351460013578166907300000000015000060, smallest error: 341.1020320763221\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 15,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 16,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 17,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 18,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 19,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 20,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 21,   best chromosome: 0905351460013578166907300000000016000060, smallest error: 344.19920027565763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 22,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 23,   best chromosome: 1605351460013578166907300000000016000060, smallest error: 389.9587389218549\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 24,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 25,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 26,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 27,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 28,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 29,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 30,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 31,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 32,   best chromosome: 0605351460013578166907300000000016000060, smallest error: 329.19058451341976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 33,   best chromosome: 0605351460017578166907300000000016000060, smallest error: 323.318227003684\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 34,   best chromosome: 0605351460017578166907300000000016000060, smallest error: 323.318227003684\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 35,   best chromosome: 0605351460017578166907300000000016000060, smallest error: 323.318227003684\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 36,   best chromosome: 0005351460017578166907300000000016000060, smallest error: 338.30309315662765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 37,   best chromosome: 1005351460017578166907300000000016000060, smallest error: 344.2710052055909\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 38,   best chromosome: 0005351460017578166907300000000016000060, smallest error: 338.30309315662765\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 39,   best chromosome: 0005351460017578466907300000000016000060, smallest error: 338.3027554449711\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 40,   best chromosome: 0005353460017578466907300000000016000060, smallest error: 338.302658677204\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 41,   best chromosome: 0005353460017578466907300000000016000060, smallest error: 338.302658677204\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 42,   best chromosome: 0005353460017578466907300000000016900060, smallest error: 338.1695324875872\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 43,   best chromosome: 0005353460017578466907300000000016900060, smallest error: 338.1695324875872\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 44,   best chromosome: 0005353460017578466907300000000016900060, smallest error: 338.1695324875872\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 45,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 46,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 47,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 48,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 49,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 50,   best chromosome: 0005553460117578466907300000000016900060, smallest error: 637.6599025609987\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 51,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 52,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 53,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 54,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 55,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 56,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 57,   best chromosome: 0005553460017578466907300000000016900060, smallest error: 338.16014027952724\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 58,   best chromosome: 0007556460017578466907300000000016900060, smallest error: 338.066080928\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 59,   best chromosome: 0007556460017578466907300000000016900060, smallest error: 338.066080928\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 60,   best chromosome: 0007556460017578466907330000000016900060, smallest error: 339.7371228614062\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 61,   best chromosome: 0007556460017578466907330000000016900060, smallest error: 339.7371228614062\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 62,   best chromosome: 0007556460017578466907330000000016900060, smallest error: 339.7371228614062\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 63,   best chromosome: 0007556460017578466907330000000016900060, smallest error: 339.7371228614062\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 64,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 65,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 66,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 67,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 68,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 69,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 70,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 71,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 72,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 73,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 74,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 75,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 76,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 77,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 78,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 79,   best chromosome: 1007556460017578466907330200000016900060, smallest error: 350.6127380896339\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 80,   best chromosome: 0007556460017578466907330200000016900060, smallest error: 339.75075979230417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 81,   best chromosome: 0007556460017578466907380200000016900060, smallest error: 344.0640741194567\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 82,   best chromosome: 0007556460017578466907380200000014900060, smallest error: 341.0845772290286\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 83,   best chromosome: 0007556460017578466907380200000014600060, smallest error: 340.98150230048896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 84,   best chromosome: 0007556460017578466907380200000014600060, smallest error: 340.98150230048896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 85,   best chromosome: 0007556460017578466907380200000014600060, smallest error: 340.98150230048896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 86,   best chromosome: 0007556460017578466907380200000014600060, smallest error: 340.98150230048896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 87,   best chromosome: 0007556460017578466907380200000014600060, smallest error: 340.98150230048896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 88,   best chromosome: 0007556460017578466907380200000014400060, smallest error: 340.957717194556\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 89,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 90,   best chromosome: 0007557460117578466907380200000014400060, smallest error: 635.5077462539258\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 91,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 92,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 93,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 94,   best chromosome: 1007557460017578466907380200000014400060, smallest error: 348.26712157190076\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 95,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 96,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 97,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 98,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 99,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 100,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 101,   best chromosome: 1007557460017578466907380200000014400060, smallest error: 348.26712157190076\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 102,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 103,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 104,   best chromosome: 0007557460017578466907380200000014400060, smallest error: 340.95766961043\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 105,   best chromosome: 0007557460017678466907380200000014400060, smallest error: 340.80281447082757\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 106,   best chromosome: 0007557460017678466907380200000014400060, smallest error: 340.80281447082757\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 107,   best chromosome: 0007557460017678466907380200000014400060, smallest error: 340.80281447082757\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 108,   best chromosome: 0077557460017678466907380200000014400060, smallest error: 337.58171861830135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 109,   best chromosome: 0077557460017678466907380200000014400060, smallest error: 337.58171861830135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 110,   best chromosome: 0077557460017678466907380200000014400060, smallest error: 337.58171861830135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 111,   best chromosome: 0077557460017678466907380200000014400060, smallest error: 337.58171861830135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 112,   best chromosome: 0077557460017678466907380200000014400060, smallest error: 337.58171861830135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 113,   best chromosome: 0077557460017678466907380200000014400060, smallest error: 337.58171861830135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 114,   best chromosome: 0077557460017678566907380200000014400060, smallest error: 337.5815673180194\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 115,   best chromosome: 0077557460017678566907380200000014400060, smallest error: 337.5815673180194\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 116,   best chromosome: 0077557460017678566907380200000014400060, smallest error: 337.5815673180194\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 117,   best chromosome: 0077657460017678566907380200000014400060, smallest error: 337.5771631971584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 118,   best chromosome: 0077657460017678566907380200000014300060, smallest error: 337.57126112453835\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 119,   best chromosome: 0077657460017678566907380200000014300060, smallest error: 337.57126112453835\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 120,   best chromosome: 0077657460017678566907380200000014300060, smallest error: 337.57126112453835\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 121,   best chromosome: 0077657462017678566907380200000014300060, smallest error: 337.57126103613297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 122,   best chromosome: 0077657462017678566907380200000014300060, smallest error: 337.57126103613297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 123,   best chromosome: 0077657462017678566907380200000014300060, smallest error: 337.57126103613297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 124,   best chromosome: 0077657662017678566907380200000014300060, smallest error: 337.57125219558463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 125,   best chromosome: 0077657662017678566907380200000014300060, smallest error: 337.57125219558463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 126,   best chromosome: 0077657662017678566907380200000014300060, smallest error: 337.57125219558463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 127,   best chromosome: 0077657662017678566907380200000014300060, smallest error: 337.57125219558463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 128,   best chromosome: 0077657662017678566907380200000014300060, smallest error: 337.57125219558463\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 129,   best chromosome: 0077657642017678566907380200000014300060, smallest error: 337.5712530796397\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 130,   best chromosome: 0477657642017678566907380200000014300060, smallest error: 325.8013640732179\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 131,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 132,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 133,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 134,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 135,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 136,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 137,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 138,   best chromosome: 0477654642017679566907380000000014300060, smallest error: 325.7865401554166\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 139,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 140,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 141,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 142,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 143,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 144,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 145,   best chromosome: 0477954642017679566907380000000014300060, smallest error: 325.784463270467\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 146,   best chromosome: 0477955642017679566907380000000014300060, smallest error: 325.784456357067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 147,   best chromosome: 1477955642017679566907380000000014300060, smallest error: 378.11178054593927\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 148,   best chromosome: 0477955642017679566907380000000014300060, smallest error: 325.784456357067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 149,   best chromosome: 0477955642017679566907380000000014300060, smallest error: 325.784456357067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 150,   best chromosome: 0477955642017679566907380000000014300060, smallest error: 325.784456357067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 151,   best chromosome: 0477955642017679566907380000000014300060, smallest error: 325.784456357067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 152,   best chromosome: 0477955642017679566907380000000014300060, smallest error: 325.784456357067\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 153,   best chromosome: 0477955642017679566907380000000014300050, smallest error: 325.78444487011626\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 154,   best chromosome: 0477955642017679566907380000000014300050, smallest error: 325.78444487011626\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 155,   best chromosome: 0477955642017679566907380000000014300050, smallest error: 325.78444487011626\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 156,   best chromosome: 1477955642017679566907380000000014300050, smallest error: 378.11174677362976\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 157,   best chromosome: 0477955642017679566907380000000014300050, smallest error: 325.78444487011626\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 158,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 159,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 160,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 161,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 162,   best chromosome: 1477955682017679566907380000000014300050, smallest error: 378.1117493755818\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 163,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 164,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 165,   best chromosome: 0477955682017679566907380000000014300050, smallest error: 325.78444459358025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 166,   best chromosome: 0477955682017679596907380000000014300050, smallest error: 325.78440260213097\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 167,   best chromosome: 0477955682017679596907380000000014300050, smallest error: 325.78440260213097\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 168,   best chromosome: 0477955682017679596907380000000013300050, smallest error: 325.0916065370222\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 169,   best chromosome: 0477955682017679596907380000000013300050, smallest error: 325.0916065370222\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 170,   best chromosome: 0477955682017679596907380000000013300050, smallest error: 325.0916065370222\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 171,   best chromosome: 0477955682017679596907380040000013300050, smallest error: 325.0933023015247\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 172,   best chromosome: 0477955682017679596907380040000013300050, smallest error: 325.0933023015247\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 173,   best chromosome: 1477955682017679596907380020000013300050, smallest error: 375.08228380078003\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 174,   best chromosome: 0477955682017679596907380020000013300050, smallest error: 325.09245442027975\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 175,   best chromosome: 0477955682017679596907380020000013300050, smallest error: 325.09245442027975\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 176,   best chromosome: 0477955682017679596907380020000013300050, smallest error: 325.09245442027975\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 177,   best chromosome: 0477955682017679596907380020000013300050, smallest error: 325.09245442027975\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 178,   best chromosome: 1477955682017679596907340020000013300050, smallest error: 371.6388965164417\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 179,   best chromosome: 0477955682017679596907340020000013300050, smallest error: 324.14189341531977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 180,   best chromosome: 0477955682017679596907340020000013300050, smallest error: 324.14189341531977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 181,   best chromosome: 0477955682017679596907340020000013300050, smallest error: 324.14189341531977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 182,   best chromosome: 0477955682017679596907340020000013300050, smallest error: 324.14189341531977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 183,   best chromosome: 0477955582017679596907340020000013300050, smallest error: 324.1418948742962\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 184,   best chromosome: 0477955582017679596907340020000013300050, smallest error: 324.1418948742962\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 185,   best chromosome: 0477955582014679596907340020000013300050, smallest error: 326.8990182976896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 186,   best chromosome: 0477955582014679596907340020000013300050, smallest error: 326.8990182976896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 187,   best chromosome: 1477955582014679596907340020000013300050, smallest error: 375.30295632383445\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 188,   best chromosome: 0477955582014679596907340020000013300050, smallest error: 326.8990182976896\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 189,   best chromosome: 0477555582014679596907340020000013300050, smallest error: 326.9048831524114\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 190,   best chromosome: 0477555582014879596907340020000013300050, smallest error: 326.6486938790285\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 191,   best chromosome: 0477555582014879596907340020000013300050, smallest error: 326.6486938790285\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 192,   best chromosome: 0477555582014879596907340020000013300050, smallest error: 326.6486938790285\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 193,   best chromosome: 0477555582014879596907340020000013300050, smallest error: 326.6486938790285\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 194,   best chromosome: 0477555582014879596907320020000013300050, smallest error: 325.91214036843274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 195,   best chromosome: 0477555582014879596907320020000013300050, smallest error: 325.91214036843274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 196,   best chromosome: 0477555582014879596907320020000013300050, smallest error: 325.91214036843274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 197,   best chromosome: 0477555582014879596907320020000013300050, smallest error: 325.91214036843274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 198,   best chromosome: 0477555582014879896907320020000013300050, smallest error: 325.9118325227082\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 199,   best chromosome: 0477555582014879896907320020000013300050, smallest error: 325.9118325227082\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 200,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 201,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 202,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 203,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 204,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 205,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 206,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 207,   best chromosome: 0477555582014879896907320020000013308050, smallest error: 325.9093934580409\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 208,   best chromosome: 0477555582014879896907320020000013508050, smallest error: 325.86651545562387\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 209,   best chromosome: 0477555582014879896907320020000013508050, smallest error: 325.86651545562387\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 210,   best chromosome: 0477555582014879896907320020000013508050, smallest error: 325.86651545562387\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 211,   best chromosome: 0477555582014879896907320020000013508051, smallest error: 325.86651535350137\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 212,   best chromosome: 0477555582014879896907320020000013508051, smallest error: 325.86651535350137\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 213,   best chromosome: 0477555582019879896907320020000013508051, smallest error: 325.6561907459359\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 214,   best chromosome: 0477555582019879896907320020000013568051, smallest error: 325.4664565189131\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 215,   best chromosome: 0477555582018879896907320020000013568051, smallest error: 324.4884272923963\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 216,   best chromosome: 1477555582018879896907320020000013568051, smallest error: 370.88663667878274\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 217,   best chromosome: 0477555682018879896907320020000013568051, smallest error: 324.4884257198666\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 218,   best chromosome: 0477555682018879896907320020000013568051, smallest error: 324.4884257198666\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 219,   best chromosome: 0479556682018879896907320020000013568051, smallest error: 324.4572226767988\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 220,   best chromosome: 0479556682018879896907320020000013568051, smallest error: 324.4572226767988\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 221,   best chromosome: 0479556682018879896907320020000013568051, smallest error: 324.4572226767988\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 222,   best chromosome: 1479556682018879896907320020000013568051, smallest error: 371.0091548095683\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 223,   best chromosome: 0479556686018879896907320020000013568051, smallest error: 324.45722261487515\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 224,   best chromosome: 0479556686018879896907320020000013568051, smallest error: 324.45722261487515\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 225,   best chromosome: 0479556686018879896907320020000013568051, smallest error: 324.45722261487515\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 226,   best chromosome: 0479556686018872896807320020000013568051, smallest error: 324.45227159053894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 227,   best chromosome: 0479566686018872896807320020000013568051, smallest error: 324.45211677945565\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 228,   best chromosome: 0479566686018872896807320020000013568051, smallest error: 324.45211677945565\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 229,   best chromosome: 0479566686018872896807370020000013568051, smallest error: 323.6619996282982\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 230,   best chromosome: 0479566686018872896807370020000013568051, smallest error: 323.6619996282982\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 231,   best chromosome: 0479566686018872896807370020000013568051, smallest error: 323.6619996282982\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 232,   best chromosome: 0479566686018872896807370020000013568051, smallest error: 323.6619996282982\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 233,   best chromosome: 0479566686018872896807370020000013568051, smallest error: 323.6619996282982\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 234,   best chromosome: 0479566686018872896807370020000013568051, smallest error: 323.6619996282982\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 235,   best chromosome: 0479566686018875896807370020000013565051, smallest error: 323.6604928981803\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 236,   best chromosome: 0479566686018875896807370020000013565051, smallest error: 323.6604928981803\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 237,   best chromosome: 0479566686018875896807370020000013565051, smallest error: 323.6604928981803\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 238,   best chromosome: 0479566686018875896807370020000013565051, smallest error: 323.6604928981803\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 239,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 240,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 241,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 242,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 243,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 244,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 245,   best chromosome: 0479566686018878896807370020000013565051, smallest error: 323.6583514847714\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 246,   best chromosome: 0489566686018878896807370020000013565051, smallest error: 323.5626535644633\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 247,   best chromosome: 0489566686018878896807370020000013565051, smallest error: 323.5626535644633\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 248,   best chromosome: 0489566686018878896807370020000013565051, smallest error: 323.5626535644633\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 249,   best chromosome: 0489566686018878896807370020000013565051, smallest error: 323.5626535644633\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 250,   best chromosome: 0489566686018878896807370020000013565051, smallest error: 323.5626535644633\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 251,   best chromosome: 0489566686018878896807370020000013585051, smallest error: 323.5590379116891\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 252,   best chromosome: 0489566686018878896807370020000013585051, smallest error: 323.5590379116891\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 253,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 254,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 255,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 256,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 257,   best chromosome: 1489966686018878896807370020000013585051, smallest error: 374.1423830445598\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 258,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 259,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 260,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 261,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 262,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 263,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 264,   best chromosome: 0489966686018878896807370020000013585051, smallest error: 323.55543030097766\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 265,   best chromosome: 0489966686018878896807370020000013589051, smallest error: 323.5547401254889\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 266,   best chromosome: 0489966686018878896807370020000013589051, smallest error: 323.5547401254889\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 267,   best chromosome: 0489966686018878896807370020000013589051, smallest error: 323.5547401254889\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 268,   best chromosome: 0489966686018878896807370020000013589051, smallest error: 323.5547401254889\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 269,   best chromosome: 0489966686018878896807370020000013589051, smallest error: 323.5547401254889\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 270,   best chromosome: 0489966686018878896807370020000013589051, smallest error: 323.5547401254889\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 271,   best chromosome: 0489966686018878896907370020000013589051, smallest error: 323.55474005405375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 272,   best chromosome: 0489966686018878896907370020000013589051, smallest error: 323.55474005405375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 273,   best chromosome: 0489966686018878896907370020000013589051, smallest error: 323.55474005405375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 274,   best chromosome: 0489966686018878896907370020000013589051, smallest error: 323.55474005405375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 275,   best chromosome: 0489966686018878896907370020000013589051, smallest error: 323.55474005405375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 276,   best chromosome: 0489966686018878896907370020000013589051, smallest error: 323.55474005405375\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 277,   best chromosome: 0489968686018878896907370020000013589051, smallest error: 323.5547221185945\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 278,   best chromosome: 0489968686018878896907370020000013589051, smallest error: 323.5547221185945\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 279,   best chromosome: 0489968686018878896907370020000013589051, smallest error: 323.5547221185945\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 280,   best chromosome: 0489968686018878896907370000000013589051, smallest error: 323.5542308115458\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 281,   best chromosome: 0489968686018878896907370000000013589051, smallest error: 323.5542308115458\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 282,   best chromosome: 0489968686018878896907370000000013589051, smallest error: 323.5542308115458\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 283,   best chromosome: 0489968686018878896907370000000013589051, smallest error: 323.5542308115458\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 284,   best chromosome: 0489968686018878896907370000000013589051, smallest error: 323.5542308115458\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 285,   best chromosome: 0489968688018878896907370000000013589051, smallest error: 323.55423079360946\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 286,   best chromosome: 0489968688018878896907370000000013589051, smallest error: 323.55423079360946\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 287,   best chromosome: 1489968688018878896907370000000013589051, smallest error: 374.14978780831865\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 288,   best chromosome: 0489968688018878896907370000000013589051, smallest error: 323.55423079360946\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 289,   best chromosome: 1489968688018878896907370000000013509053, smallest error: 373.9723034885818\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 290,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 291,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 292,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 293,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 294,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 295,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 296,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 297,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 298,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 299,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 300,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 301,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 302,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 303,   best chromosome: 1489968688018878896907370000000013509053, smallest error: 373.9723034885818\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 304,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 305,   best chromosome: 0489968688018878896907370000000013509053, smallest error: 323.57013308042997\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 306,   best chromosome: 0489968688018878896907370000000013549053, smallest error: 323.5616029907895\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 307,   best chromosome: 0489968688018878896907370000000013549053, smallest error: 323.5616029907895\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 308,   best chromosome: 0489968608018878896907370010000013549053, smallest error: 323.5618420464631\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 309,   best chromosome: 0489968608018878896907370010000013549053, smallest error: 323.5618420464631\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 310,   best chromosome: 0489968608018878896907370010000013549055, smallest error: 323.56184165330825\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 311,   best chromosome: 0489969608018878896907370010000013549055, smallest error: 323.5618325269154\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 312,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 313,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 314,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 315,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 316,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 317,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 318,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 319,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 320,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 321,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 322,   best chromosome: 0489969609018878896907370010000013549055, smallest error: 323.5618325177887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 323,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 324,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 325,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 326,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 327,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 328,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 329,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 330,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 331,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 332,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 333,   best chromosome: 0489969309018878896907370010000013549055, smallest error: 323.56183525570606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 334,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 335,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 336,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 337,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 338,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 339,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 340,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 341,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 342,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 343,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 344,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 345,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 346,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 347,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 348,   best chromosome: 1486969309018878896907370010000013549055, smallest error: 373.870593749257\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 349,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 350,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 351,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 352,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 353,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 354,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 355,   best chromosome: 1486969309018878896907370010000013549055, smallest error: 373.870593749257\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 356,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 357,   best chromosome: 0486969309018878896907370010000013549055, smallest error: 323.5892894850608\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 358,   best chromosome: 0486969309018879896907370010000013549055, smallest error: 323.58858442223425\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 359,   best chromosome: 0486969309018889896907370010000013549055, smallest error: 323.5815506591853\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 360,   best chromosome: 1481969309018889896907370010000013549055, smallest error: 373.5411831362776\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 361,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 362,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 363,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 364,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 365,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 366,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 367,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 368,   best chromosome: 0484969309018889896907370010000013549055, smallest error: 323.6003024007424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 369,   best chromosome: 0485969309018889896907370010000013549055, smallest error: 323.59077229023995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 370,   best chromosome: 0485969309018889896907370010000013549055, smallest error: 323.59077229023995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 371,   best chromosome: 0485969309018889896907370010000013549055, smallest error: 323.59077229023995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 372,   best chromosome: 0485969309018889896907370010000013549055, smallest error: 323.59077229023995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 373,   best chromosome: 0485969309018889896907370010000013549055, smallest error: 323.59077229023995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 374,   best chromosome: 0485969309018889896907370010000013549055, smallest error: 323.59077229023995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 375,   best chromosome: 1485969309018889896907370010000013549055, smallest error: 373.79556418071655\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 376,   best chromosome: 0475969309018889896907370010000013549055, smallest error: 323.6920430010346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 377,   best chromosome: 0475969309018889896907370010000013549055, smallest error: 323.6920430010346\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 378,   best chromosome: 0476969309018889896907370010000013549055, smallest error: 323.6812773757669\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 379,   best chromosome: 0476969309018889896907370010000013549055, smallest error: 323.6812773757669\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 380,   best chromosome: 1476969309018889896907370010000013549055, smallest error: 373.22343342633116\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 381,   best chromosome: 0476969309018889896907370010000013549055, smallest error: 323.6812773757669\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 382,   best chromosome: 0476969309018889898907370010000013549055, smallest error: 323.6812759695145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 383,   best chromosome: 0476969309018889898907370010000013549055, smallest error: 323.6812759695145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 384,   best chromosome: 0476969309018889898907370010000013549055, smallest error: 323.6812759695145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 385,   best chromosome: 0476969309018889898907370010000013549055, smallest error: 323.6812759695145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 386,   best chromosome: 0476969309018889898907370010000013549055, smallest error: 323.6812759695145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 387,   best chromosome: 0476969309018889898907370010000013549055, smallest error: 323.6812759695145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 388,   best chromosome: 0476969309018889898907370610000013949055, smallest error: 323.67269826089824\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 389,   best chromosome: 0476969309018889898507370610000013949055, smallest error: 323.67269860013005\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 390,   best chromosome: 0476969309018889898507370610000013949055, smallest error: 323.67269860013005\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 391,   best chromosome: 0476969309018889898507370610000013949055, smallest error: 323.67269860013005\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 392,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 393,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 394,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 395,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 396,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 397,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 398,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 399,   best chromosome: 0476969309018888898507370610000013949055, smallest error: 323.6735467135083\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 400,   best chromosome: 0476969309018888898507370610000013947055, smallest error: 323.6733399329534\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 401,   best chromosome: 0476969309018888898507370610000013937055, smallest error: 323.67236136575025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 402,   best chromosome: 0476969309018888898507370610000013937055, smallest error: 323.67236136575025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 403,   best chromosome: 0476969309018888898507370610000013937055, smallest error: 323.67236136575025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 404,   best chromosome: 0476969309018888898507370610000013937055, smallest error: 323.67236136575025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 405,   best chromosome: 0476969309018888898507370610000013937055, smallest error: 323.67236136575025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 406,   best chromosome: 0476969309018888898507370210000013937055, smallest error: 323.65944293754603\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 407,   best chromosome: 0476969309018888898507370210000013937055, smallest error: 323.65944293754603\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 408,   best chromosome: 0476969309018888898507370210000013937055, smallest error: 323.65944293754603\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 409,   best chromosome: 0476969309018888898507370210000013937055, smallest error: 323.65944293754603\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 410,   best chromosome: 0476969309018888898507370210000013937055, smallest error: 323.65944293754603\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 411,   best chromosome: 0476969509018888898507370210000013937055, smallest error: 323.65944108417784\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 412,   best chromosome: 0476969509018888898507350210000013937055, smallest error: 323.2220380635197\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 413,   best chromosome: 0476969509018888898507350210000013937055, smallest error: 323.2220380635197\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 414,   best chromosome: 0476969509018888898507350110000013937055, smallest error: 323.2208658302132\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 415,   best chromosome: 0476969509018888898507350110000013937055, smallest error: 323.2208658302132\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 416,   best chromosome: 0476969509018888898507350110000013937055, smallest error: 323.2208658302132\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 417,   best chromosome: 0476969509018888898507350110000013937015, smallest error: 323.22088621305596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 418,   best chromosome: 0476969509018888898507350110000013937015, smallest error: 323.22088621305596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 419,   best chromosome: 0476969509018888898507350110000013937015, smallest error: 323.22088621305596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 420,   best chromosome: 0476969509018888898507350110000013937015, smallest error: 323.22088621305596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 421,   best chromosome: 0476969509018888898507350110000013937015, smallest error: 323.22088621305596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 422,   best chromosome: 0476969509018898898507350110000013937015, smallest error: 323.2160168508559\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 423,   best chromosome: 0476969509018898898507350110000013937015, smallest error: 323.2160168508559\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 424,   best chromosome: 0476969509018898898507350110000013937015, smallest error: 323.2160168508559\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 425,   best chromosome: 0476969509018898898507350010000013937015, smallest error: 323.21488116598573\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 426,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 427,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 428,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 429,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 430,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 431,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 432,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 433,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 434,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 435,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 436,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 437,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 438,   best chromosome: 0476969509018898898507350010000013957015, smallest error: 323.2047210353245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 439,   best chromosome: 0476969909018898898507350010000013957015, smallest error: 323.2047166845719\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 440,   best chromosome: 0476969909018898898507350010000013957015, smallest error: 323.2047166845719\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 441,   best chromosome: 0476969909018898898507350010000013957015, smallest error: 323.2047166845719\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 442,   best chromosome: 0476969909018898898507350000000013957015, smallest error: 323.20459620235994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 443,   best chromosome: 0476969909018898898507350000000013957015, smallest error: 323.20459620235994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 444,   best chromosome: 1476969909018898898507350000000013957015, smallest error: 372.50526593993413\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 445,   best chromosome: 1476969009018898898507350000000013957015, smallest error: 372.5052088980132\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 446,   best chromosome: 0476969009018898898507350000000013957015, smallest error: 323.2046059915541\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 447,   best chromosome: 0476969009018898898507350000000013957015, smallest error: 323.2046059915541\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 448,   best chromosome: 0476969009018898898507350000000013957015, smallest error: 323.2046059915541\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 449,   best chromosome: 0476969009018898898507350000000013957015, smallest error: 323.2046059915541\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 450,   best chromosome: 0476969009018898898507350000000013957018, smallest error: 323.2046045057144\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 451,   best chromosome: 0476969009018898898507350000000013957018, smallest error: 323.2046045057144\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 452,   best chromosome: 0576969009018898898507350000000013957018, smallest error: 322.9662906427234\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 453,   best chromosome: 0576449009018898898507350000400013957018, smallest error: 322.96187297410324\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 454,   best chromosome: 0576149009018898898507350000400013957018, smallest error: 322.959334002908\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 455,   best chromosome: 0576149009018898898507350000400013957018, smallest error: 322.959334002908\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 456,   best chromosome: 0576149009018898898507350000400013957018, smallest error: 322.959334002908\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 457,   best chromosome: 0576149009018898898507350000400013957018, smallest error: 322.959334002908\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 458,   best chromosome: 0576149009018898898507350000400013957018, smallest error: 322.959334002908\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 459,   best chromosome: 0576149009018898898507350000400013957018, smallest error: 322.959334002908\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 460,   best chromosome: 0573149009018898898507350000400013957018, smallest error: 322.93428719086324\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 461,   best chromosome: 0573149009018898898507350000400013957018, smallest error: 322.93428719086324\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 462,   best chromosome: 0573149009018898898507350000400013957018, smallest error: 322.93428719086324\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 463,   best chromosome: 0523141009018898898507350000400013957018, smallest error: 322.85527968304564\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 464,   best chromosome: 0523141009018898898507350000400013957018, smallest error: 322.85527968304564\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 465,   best chromosome: 0523141009018898898507350000400013957018, smallest error: 322.85527968304564\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 466,   best chromosome: 0523141009018898898507350000400013957018, smallest error: 322.85527968304564\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 467,   best chromosome: 0523141009018898898507350000400013957018, smallest error: 322.85527968304564\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 468,   best chromosome: 0523141009018898898507357000400015957018, smallest error: 324.7654083491509\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 469,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 470,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 471,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 472,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 473,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 474,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 475,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 476,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 477,   best chromosome: 0523141009018898898507357000100015957018, smallest error: 324.76538294726134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 478,   best chromosome: 0523141009018898898507357000100015057018, smallest error: 323.45332855391024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 479,   best chromosome: 0523141009018898898507357000100015057018, smallest error: 323.45332855391024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 480,   best chromosome: 0523141009018898898507357000100015057018, smallest error: 323.45332855391024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 481,   best chromosome: 0523141009019898898507357000100015057018, smallest error: 322.509278799811\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 482,   best chromosome: 0523141009019898898507357000100015057018, smallest error: 322.509278799811\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 483,   best chromosome: 0523141009019898898507357000100015057018, smallest error: 322.509278799811\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 484,   best chromosome: 0523141009019898898507357000100015027018, smallest error: 322.4954752365133\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 485,   best chromosome: 0523141009019898898507357000100015027014, smallest error: 322.4954734568447\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 486,   best chromosome: 0523141009019898898507357000100015027011, smallest error: 322.4954721220927\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 487,   best chromosome: 0523141009019898898507357000100015027011, smallest error: 322.4954721220927\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 488,   best chromosome: 0523141009019898898507357000100015027011, smallest error: 322.4954721220927\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 489,   best chromosome: 0523141009019898898507357000100015027011, smallest error: 322.4954721220927\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 490,   best chromosome: 0523141009019898898507357000100015027011, smallest error: 322.4954721220927\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 491,   best chromosome: 0523141009019898898507347000100015027011, smallest error: 322.1939127092214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 492,   best chromosome: 0523141009019898898507347000100015027011, smallest error: 322.1939127092214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 493,   best chromosome: 0523141009019898898507347000100015027011, smallest error: 322.1939127092214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 494,   best chromosome: 0523141009019898898507347000100015027011, smallest error: 322.1939127092214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 495,   best chromosome: 0523141004019898898507347000100015007011, smallest error: 322.19168926043824\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 496,   best chromosome: 0523941004019898898507347000100015007011, smallest error: 322.19134856266135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 497,   best chromosome: 0523941004019898898507347000100015007011, smallest error: 322.19134856266135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 498,   best chromosome: 0523941004019898898507347000100015007011, smallest error: 322.19134856266135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 499,   best chromosome: 0523941004019898898507347000100015007011, smallest error: 322.19134856266135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 500,   best chromosome: 0523941004019898898507347000100015007011, smallest error: 322.19134856266135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 501,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 502,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 503,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 504,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 505,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 506,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 507,   best chromosome: 0523941004019898998507347000100015007011, smallest error: 322.1912987122161\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 508,   best chromosome: 0523941004019898998607347000100015007011, smallest error: 322.19129866237887\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 509,   best chromosome: 1523941004019898998607347000100015007011, smallest error: 376.7050618751808\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 510,   best chromosome: 0523941004019898498607347000100015007011, smallest error: 322.1915479145064\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 511,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 512,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 513,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 514,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 515,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 516,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 517,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 518,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 519,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 520,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 521,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 522,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 523,   best chromosome: 0528941004019898498607347000100015007011, smallest error: 322.19084094347994\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 524,   best chromosome: 0528941004019898498607343000100015007011, smallest error: 322.1015835494145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 525,   best chromosome: 0528941004019898498607343000100015007011, smallest error: 322.1015835494145\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 526,   best chromosome: 0528940004019898498607343000100015007011, smallest error: 322.1015833914704\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 527,   best chromosome: 0528940004019898498607343000150015007911, smallest error: 322.1015560380354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 528,   best chromosome: 0528940004019898498607343000150015007911, smallest error: 322.1015560380354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 529,   best chromosome: 1528940004019898498607343000150015007911, smallest error: 376.7037025756641\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 530,   best chromosome: 0528940004019898498607343000150015007911, smallest error: 322.1015560380354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 531,   best chromosome: 0528940004019898498607343000150015007911, smallest error: 322.1015560380354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 532,   best chromosome: 0528940004019898498607343000150015007911, smallest error: 322.1015560380354\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 533,   best chromosome: 1528940004019898498607343000150015006911, smallest error: 376.701435898251\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 534,   best chromosome: 0528940004019898498607343000150015006911, smallest error: 322.10158771762343\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 535,   best chromosome: 0528940004019898798607343000150015006911, smallest error: 322.1014657228917\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 536,   best chromosome: 0528940004019898798607343000150015006911, smallest error: 322.1014657228917\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 537,   best chromosome: 0528940004019898798607343000150015006911, smallest error: 322.1014657228917\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 538,   best chromosome: 0528940004019898798607343000150015006911, smallest error: 322.1014657228917\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 539,   best chromosome: 1528940004019898798607343000150015006911, smallest error: 376.701191471985\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 540,   best chromosome: 0528940004019898798607343000150015006911, smallest error: 322.1014657228917\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 541,   best chromosome: 0528940004019898791607343000150015006911, smallest error: 322.1014685694596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 542,   best chromosome: 0528940004019898791607343000150015006911, smallest error: 322.1014685694596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 543,   best chromosome: 0528940004019898791607343000150015006911, smallest error: 322.1014685694596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 544,   best chromosome: 0528940004019898791607343000150015006911, smallest error: 322.1014685694596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 545,   best chromosome: 0528940004019898791607343000150015006911, smallest error: 322.1014685694596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 546,   best chromosome: 0528940004019898791907343000150015006911, smallest error: 322.1014684474571\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 547,   best chromosome: 0528940004019898791907343000150015006971, smallest error: 322.10146643764216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 548,   best chromosome: 0528940004019898791907343000150015006971, smallest error: 322.10146643764216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 549,   best chromosome: 0528940004019898791907343000150015006971, smallest error: 322.10146643764216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 550,   best chromosome: 0528940004019898791907343000150015006971, smallest error: 322.10146643764216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 551,   best chromosome: 0528940004019898791907343000150015006971, smallest error: 322.10146643764216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 552,   best chromosome: 0528940004019898791907343000150015006971, smallest error: 322.10146643764216\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 553,   best chromosome: 0528940004019898791907343000150015009971, smallest error: 322.10137188975324\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 554,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 555,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 556,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 557,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 558,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 559,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 560,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 561,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 562,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 563,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 564,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 565,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 566,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 567,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 568,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 569,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 570,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 571,   best chromosome: 0528940002019898791907343000150015009971, smallest error: 322.10137188943787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 572,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 573,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 574,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 575,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 576,   best chromosome: 1528940002019898791907343000150015009970, smallest error: 376.7081484282239\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 577,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 578,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 579,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 580,   best chromosome: 0528940002019898791907343000150015009970, smallest error: 322.10137192064894\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 581,   best chromosome: 0528940002019898791907343000110015009970, smallest error: 322.10137113570664\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 582,   best chromosome: 0528940002019898791907343000110015009973, smallest error: 322.1013710420709\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 583,   best chromosome: 0528940002019898791907343000110015009973, smallest error: 322.1013710420709\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 584,   best chromosome: 0528940002019898791907343000110015009993, smallest error: 322.10137041783264\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 585,   best chromosome: 0528940002019898791907343000110015009993, smallest error: 322.10137041783264\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 586,   best chromosome: 0528940002019898791907343000110015009993, smallest error: 322.10137041783264\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 587,   best chromosome: 0528940002019898791907343000110015009993, smallest error: 322.10137041783264\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 588,   best chromosome: 0527940002019898791907343000110015009993, smallest error: 322.101252098634\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 589,   best chromosome: 0527940002019898791907343000110015009993, smallest error: 322.101252098634\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 590,   best chromosome: 1527740002019898791907343000110015009993, smallest error: 376.6315546735212\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 591,   best chromosome: 0527750002019898791907343000110015009993, smallest error: 322.1013123158501\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 592,   best chromosome: 0527750002019898791907343000110015009993, smallest error: 322.1013123158501\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 593,   best chromosome: 0527750002019898791907343000110015009993, smallest error: 322.1013123158501\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 594,   best chromosome: 0527750002019898791907343000150015009993, smallest error: 322.10131309847475\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 595,   best chromosome: 0527950002019898791907343000150015009993, smallest error: 322.10124971151527\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 596,   best chromosome: 0527950002019898791907343000150015009993, smallest error: 322.10124971151527\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 597,   best chromosome: 0527950002019898791907343000150015009993, smallest error: 322.10124971151527\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 598,   best chromosome: 0527950062019898791907343000150015009993, smallest error: 322.10124969249733\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 599,   best chromosome: 0527950062019898791907343000150015009993, smallest error: 322.10124969249733\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 600,   best chromosome: 0527950062019898791907343000150015009993, smallest error: 322.10124969249733\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 601,   best chromosome: 0527960062019898791907343000150015009993, smallest error: 322.101246522716\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 602,   best chromosome: 0527960062019898791907343000150015009993, smallest error: 322.101246522716\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 603,   best chromosome: 0527960062019898791907343000150015009993, smallest error: 322.101246522716\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 604,   best chromosome: 0527960562019898791907343000150015009993, smallest error: 322.1012463642259\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 605,   best chromosome: 0527960564019898791907343000150015009993, smallest error: 322.10124636359194\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 606,   best chromosome: 0527960564019898791907343000150015009993, smallest error: 322.10124636359194\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 607,   best chromosome: 0527960564019898791907343000150015009993, smallest error: 322.10124636359194\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 608,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 609,   best chromosome: 1527960564019898791907343000150015039993, smallest error: 376.7147571153057\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 610,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 611,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 612,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 613,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 614,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 615,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 616,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 617,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 618,   best chromosome: 1527960564019898791907343000150015039993, smallest error: 376.7147571153057\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 619,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 620,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 621,   best chromosome: 1527960564019898791907343000150015039993, smallest error: 376.7147571153057\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 622,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 623,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 624,   best chromosome: 1527960564019898791907343000150015039993, smallest error: 376.7147571153057\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 625,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 626,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 627,   best chromosome: 0527960564019898791907343000150015039993, smallest error: 322.10102375819434\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 628,   best chromosome: 0527960560019898791907343000150015039983, smallest error: 322.10102368525475\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 629,   best chromosome: 0527960460019898791907343000150015039983, smallest error: 322.1010236689004\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 630,   best chromosome: 0527960460019898791907343000150015039983, smallest error: 322.1010236689004\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 631,   best chromosome: 0527960460019898791907343000150015039983, smallest error: 322.1010236689004\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 632,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 633,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 634,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 635,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 636,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 637,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 638,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 639,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 640,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 641,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 642,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 643,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 644,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 645,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 646,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 647,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 648,   best chromosome: 0527967420019898891907343000150015039983, smallest error: 322.1009811050441\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 649,   best chromosome: 0527067420019898891907343000150015039983, smallest error: 322.1012281040487\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 650,   best chromosome: 0527067420019898891907343000150015039983, smallest error: 322.1012281040487\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 651,   best chromosome: 0527067420019898891907343000150015039983, smallest error: 322.1012281040487\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 652,   best chromosome: 0527067420019898891907343000150015039983, smallest error: 322.1012281040487\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 653,   best chromosome: 0527265420019898891907343000150015039983, smallest error: 322.1011664722299\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 654,   best chromosome: 0527265420019898891907343000150015039983, smallest error: 322.1011664722299\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 655,   best chromosome: 0527265420019898891907343000150015039983, smallest error: 322.1011664722299\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 656,   best chromosome: 0527265420019898891907343000150015039983, smallest error: 322.1011664722299\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 657,   best chromosome: 0527265420019898891907343000150015039983, smallest error: 322.1011664722299\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 658,   best chromosome: 0527265420019898891907343000150015036983, smallest error: 322.1011585261905\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 659,   best chromosome: 0527265420019898891907343000150015036983, smallest error: 322.1011585261905\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 660,   best chromosome: 0527265420019898891907343000150015036983, smallest error: 322.1011585261905\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 661,   best chromosome: 0527265420019898891907343000150015036953, smallest error: 322.10115849923085\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 662,   best chromosome: 0527265420019898894907343000150015036953, smallest error: 322.10115718400215\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 663,   best chromosome: 0527265420019898894907343000150015036953, smallest error: 322.10115718400215\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 664,   best chromosome: 0527265420019828894907343000150015036953, smallest error: 322.1327132606547\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 665,   best chromosome: 0527265420019828894907343000150015036953, smallest error: 322.1327132606547\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 666,   best chromosome: 0527265420019828894907343000150015036953, smallest error: 322.1327132606547\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 667,   best chromosome: 0527265420019828894907343000150015036953, smallest error: 322.1327132606547\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 668,   best chromosome: 0527265420019828894907343000150015036953, smallest error: 322.1327132606547\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 669,   best chromosome: 0527265420019828894907370000150015036953, smallest error: 323.1669399127341\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 670,   best chromosome: 0527265420019828894907370000150015036953, smallest error: 323.1669399127341\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 671,   best chromosome: 0527265420019828894907370000150015036853, smallest error: 323.1668424079853\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 672,   best chromosome: 0527265420019828894907370000150015036853, smallest error: 323.1668424079853\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 673,   best chromosome: 0527265420019828894907370000150015036853, smallest error: 323.1668424079853\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 674,   best chromosome: 0527261420019828894907370000150015036853, smallest error: 323.16683628314536\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 675,   best chromosome: 0527261420019828894907370000120015036853, smallest error: 323.16683460511297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 676,   best chromosome: 0527261420019878894907370000120015036853, smallest error: 323.1141931456787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 677,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 678,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 679,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 680,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 681,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 682,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 683,   best chromosome: 0527261420019878894907370000120015136853, smallest error: 323.21552307460024\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 684,   best chromosome: 0527261420019878894907370000120015156853, smallest error: 323.2371138568584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 685,   best chromosome: 0527261420019878894907370000120015156853, smallest error: 323.2371138568584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 686,   best chromosome: 0527261420019878894907370000120015156853, smallest error: 323.2371138568584\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 687,   best chromosome: 0527261420019878894907370000120015157853, smallest error: 323.2382036601631\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 688,   best chromosome: 0527261420019878894907370000120015157853, smallest error: 323.2382036601631\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 689,   best chromosome: 0527261420019898894907370000120015157853, smallest error: 323.216529832615\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 690,   best chromosome: 0527261420019898894907370000120015157853, smallest error: 323.216529832615\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 691,   best chromosome: 0527261420019898894907370000120015157853, smallest error: 323.216529832615\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 692,   best chromosome: 0527261420019898894907370000120015157853, smallest error: 323.216529832615\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 693,   best chromosome: 0627261420019898894907370000120015157853, smallest error: 324.80837848471435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 694,   best chromosome: 0627261420019898894907370000120015157853, smallest error: 324.80837848471435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 695,   best chromosome: 0627261420019898894907370000120015157853, smallest error: 324.80837848471435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 696,   best chromosome: 0627261320019898894907370000120015157853, smallest error: 324.80837547819453\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 697,   best chromosome: 0627261320019898894907370000120015157853, smallest error: 324.80837547819453\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 698,   best chromosome: 0627261320019898894907370000120015157853, smallest error: 324.80837547819453\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 699,   best chromosome: 0627261320019898894907370050120015157853, smallest error: 324.8117593092919\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 700,   best chromosome: 0627261320019898894907370050120015157853, smallest error: 324.8117593092919\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 701,   best chromosome: 0627261320019898894907370050120015157853, smallest error: 324.8117593092919\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 702,   best chromosome: 0627261320019898894907370050100015157853, smallest error: 324.81175795577616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 703,   best chromosome: 0627261320019898894907370050100015157853, smallest error: 324.81175795577616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 704,   best chromosome: 0627261320019898894907370050100015157853, smallest error: 324.81175795577616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 705,   best chromosome: 0624261320019898894907370050100015157853, smallest error: 324.72309624338084\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 706,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 707,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 708,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 709,   best chromosome: 1624261320019898894907370050100015157453, smallest error: 385.8747006419874\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 710,   best chromosome: 1624261320019898894907370050100015157453, smallest error: 385.8747006419874\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 711,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 712,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 713,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 714,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 715,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 716,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 717,   best chromosome: 0624261320119898894907370050100015157453, smallest error: 639.1825285506915\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 718,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 719,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 720,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 721,   best chromosome: 0624261320019898894907370050100015157453, smallest error: 324.7225274267621\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 722,   best chromosome: 0624261320019298894907370050100015157453, smallest error: 325.4076995954392\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 723,   best chromosome: 0624261320019298894907370050100015157453, smallest error: 325.4076995954392\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 724,   best chromosome: 0624261320019298894907370050100015157453, smallest error: 325.4076995954392\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 725,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 726,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 727,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 728,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 729,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 730,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 731,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 732,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 733,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 734,   best chromosome: 0624261320019298894907370050100015156453, smallest error: 325.40600889624983\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 735,   best chromosome: 0624231320019298894907370050100015156453, smallest error: 325.40512790540623\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 736,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 737,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 738,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 739,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 740,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 741,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 742,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 743,   best chromosome: 0424231320019298894907370050100015156453, smallest error: 324.44286007860063\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 744,   best chromosome: 0424231320019298894907360053100015156453, smallest error: 323.9026888046052\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 745,   best chromosome: 0424221320119298894907360053100015156453, smallest error: 635.6437100139491\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 746,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 747,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 748,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 749,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 750,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 751,   best chromosome: 1424228320019298894907360053100015156453, smallest error: 372.6267000431357\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 752,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 753,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 754,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 755,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 756,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 757,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 758,   best chromosome: 0424228320019298894907360053100015156453, smallest error: 323.90272906318256\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 759,   best chromosome: 0424228320019298894907310053100015156453, smallest error: 323.03695159367953\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 760,   best chromosome: 0424228320019298894907310153100015156453, smallest error: 323.03488770540616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 761,   best chromosome: 0424228320019298894907310153100015156453, smallest error: 323.03488770540616\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 762,   best chromosome: 0424228320019298194907310153100015156453, smallest error: 323.0348036267103\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 763,   best chromosome: 0424228320019099194907310153100015156453, smallest error: 323.0231003197316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 764,   best chromosome: 0424228320019099199907310153100015156453, smallest error: 323.02310010878836\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 765,   best chromosome: 0424228320019099199907310153100015156453, smallest error: 323.02310010878836\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 766,   best chromosome: 0424628320019099199907310153100015156453, smallest error: 323.0159543445883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 767,   best chromosome: 1424628320019099199907310153100015156453, smallest error: 368.66930913809466\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 768,   best chromosome: 0424628320019099199907310153100015156453, smallest error: 323.0159543445883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 769,   best chromosome: 0424628320019099199907310153100015156453, smallest error: 323.0159543445883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 770,   best chromosome: 0424628320019099199907310153100015156453, smallest error: 323.0159543445883\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 771,   best chromosome: 0424648320019099199907310153100015156453, smallest error: 323.01559705522686\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 772,   best chromosome: 0424648320019099199907311153100015156453, smallest error: 323.0038539583606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 773,   best chromosome: 0424648320019099199907311153100015156453, smallest error: 323.0038539583606\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 774,   best chromosome: 0424648320019099199907312153100015156453, smallest error: 322.9941985039297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 775,   best chromosome: 0424648320019099199907312153100015156453, smallest error: 322.9941985039297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 776,   best chromosome: 1424648320019099199907312153100015156453, smallest error: 368.78045842084686\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 777,   best chromosome: 0424648320019099199907312153100015156453, smallest error: 322.9941985039297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 778,   best chromosome: 0424648320019099199907312153100015156453, smallest error: 322.9941985039297\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 779,   best chromosome: 0424688320019099199907312153100015156453, smallest error: 322.99348879146436\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 780,   best chromosome: 0424688320019099199907312153100015156458, smallest error: 322.9934840042952\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 781,   best chromosome: 0424688320019099199907312153100015156458, smallest error: 322.9934840042952\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 782,   best chromosome: 0424688320019099199907312153100015156458, smallest error: 322.9934840042952\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 783,   best chromosome: 0424688320019099199907312153700015156458, smallest error: 322.9934788227844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 784,   best chromosome: 0424688320019099299907312153700015156458, smallest error: 322.9934677523825\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 785,   best chromosome: 0424688320019099299907312153700015156458, smallest error: 322.9934677523825\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 786,   best chromosome: 0424688320019099299907312153700015156458, smallest error: 322.9934677523825\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 787,   best chromosome: 0424688320019099299907312153700015156458, smallest error: 322.9934677523825\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 788,   best chromosome: 1424688320019099299907312103700015156458, smallest error: 368.7801629330862\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 789,   best chromosome: 0426688320019099299907312103700015156458, smallest error: 322.95862245608146\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 790,   best chromosome: 0426688324019099299907312103700015156458, smallest error: 322.95862238608294\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 791,   best chromosome: 0426688327019099299907312103700015156458, smallest error: 322.95862233358287\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 792,   best chromosome: 0426688327019099299907312103700015156458, smallest error: 322.95862233358287\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 793,   best chromosome: 0426688327019099299907312103700015156458, smallest error: 322.95862233358287\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 794,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 795,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 796,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 797,   best chromosome: 1426688327019099299907312103700015157458, smallest error: 368.90885054875787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 798,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 799,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 800,   best chromosome: 1426688327019099299907312103700015157458, smallest error: 368.90885054875787\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 801,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 802,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 803,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 804,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 805,   best chromosome: 0426688327019099299907312103700015157458, smallest error: 322.95767592290025\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 806,   best chromosome: 0426638327019099299907312103700015157458, smallest error: 322.9585509020377\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 807,   best chromosome: 0426638627019099299907312103700015157458, smallest error: 322.9585456521651\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 808,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 809,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 810,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 811,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 812,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 813,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 814,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 815,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 816,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 817,   best chromosome: 0426638627019099299907312106700015157458, smallest error: 322.95852050570977\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 818,   best chromosome: 0426638627019029299907312106700015157458, smallest error: 322.96719857021816\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 819,   best chromosome: 0426638627019029299907312106700015157458, smallest error: 322.96719857021816\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 820,   best chromosome: 0426638627019029299907312106700015157458, smallest error: 322.96719857021816\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 821,   best chromosome: 0426638627019029299907312106700015157458, smallest error: 322.96719857021816\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 822,   best chromosome: 0426638627019029299907317106700015157858, smallest error: 322.9461871037624\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 823,   best chromosome: 0426638827019029299907317106700015157858, smallest error: 322.94618367755464\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 824,   best chromosome: 0426638827019029299907317106700015157858, smallest error: 322.94618367755464\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 825,   best chromosome: 0426638827019029299907317106700015157858, smallest error: 322.94618367755464\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 826,   best chromosome: 0426638827019029299907317106700015157858, smallest error: 322.94618367755464\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 827,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 828,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 829,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 830,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 831,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 832,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 833,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 834,   best chromosome: 0426638877019029299907317106700015157858, smallest error: 322.94618282100214\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 835,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 836,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 837,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 838,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 839,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 840,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 841,   best chromosome: 0426638877019029299907317106700015177858, smallest error: 322.9312086662554\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 842,   best chromosome: 0426638877019829299907317106700015177858, smallest error: 322.9012407584989\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 843,   best chromosome: 0426638877019829299907317106700015177858, smallest error: 322.9012407584989\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 844,   best chromosome: 0426638877019829299907317106700015177858, smallest error: 322.9012407584989\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 845,   best chromosome: 0426638877019829299907317906700015177858, smallest error: 322.88429893739135\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 846,   best chromosome: 0426638877019829299307317906700015177858, smallest error: 322.8842988369141\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 847,   best chromosome: 0426638877019829299307317906700015177888, smallest error: 322.88426196435694\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 848,   best chromosome: 1426638877019829299307317906700015177888, smallest error: 369.02159282708544\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 849,   best chromosome: 0426638877019829299307317906700015177888, smallest error: 322.88426196435694\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 850,   best chromosome: 0426638877019829299307317906700015177888, smallest error: 322.88426196435694\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 851,   best chromosome: 0426638877019829299307317976700015177888, smallest error: 322.88279993485844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 852,   best chromosome: 0426638877019829299307317976700015177888, smallest error: 322.88279993485844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 853,   best chromosome: 0426638877019829299307317976700015177888, smallest error: 322.88279993485844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 854,   best chromosome: 0426638877019829299307317976700015177888, smallest error: 322.88279993485844\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 855,   best chromosome: 0426638877019829299307317976760015177888, smallest error: 322.8827986816906\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 856,   best chromosome: 0426638877019829299307317976760015177888, smallest error: 322.8827986816906\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 857,   best chromosome: 0426638977019829299307317976760015677888, smallest error: 322.42626650674157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 858,   best chromosome: 0426638977019829299307317976760015677888, smallest error: 322.42626650674157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 859,   best chromosome: 0426638977019829299307317976760015677888, smallest error: 322.42626650674157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 860,   best chromosome: 1426638977019829299307317976760015677884, smallest error: 369.84435217864666\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 861,   best chromosome: 0426638977019829299307317976760015677884, smallest error: 322.42626890623245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 862,   best chromosome: 0426638977019829299307317976760015677884, smallest error: 322.42626890623245\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 863,   best chromosome: 0426658977019829299307317976760015677884, smallest error: 322.4259754724669\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 864,   best chromosome: 0426658977019829299307317976760015677884, smallest error: 322.4259754724669\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 865,   best chromosome: 0426658977019829299307317976760015677884, smallest error: 322.4259754724669\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 866,   best chromosome: 1426658977019879299307317976760015677884, smallest error: 369.8197249865522\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 867,   best chromosome: 0426658977019879299307317976760015677884, smallest error: 322.41771670470763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 868,   best chromosome: 0426658977019879299307317976760015677884, smallest error: 322.41771670470763\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 869,   best chromosome: 0526658977019879299307317976760015677884, smallest error: 321.69011567476906\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 870,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 871,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 872,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 873,   best chromosome: 1526658477019879299307317976760015677884, smallest error: 376.22655195114106\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 874,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 875,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 876,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 877,   best chromosome: 0526658477019879299307317976760015677884, smallest error: 321.69011584243316\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 878,   best chromosome: 0526658477019879299307317976760015672884, smallest error: 321.69152160139424\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 879,   best chromosome: 0526658477019879299307317976720015672884, smallest error: 321.6915213092769\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 880,   best chromosome: 0526658477019879299307317976720015672884, smallest error: 321.6915213092769\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 881,   best chromosome: 0526658477019879299307315976720015672784, smallest error: 321.6788015251772\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 882,   best chromosome: 0526658477019879299307315976720015672784, smallest error: 321.6788015251772\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 883,   best chromosome: 0526658477019879299307315976720015672784, smallest error: 321.6788015251772\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 884,   best chromosome: 0526958477019879299307315976720015672784, smallest error: 321.6786468925238\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 885,   best chromosome: 1526958477019879299307315976720015672784, smallest error: 376.10437122913385\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 886,   best chromosome: 0526958477019879299307315976720015672784, smallest error: 321.6786468925238\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 887,   best chromosome: 1526958477019879299307315976720015602784, smallest error: 375.9684266047005\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 888,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 889,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 890,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 891,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 892,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 893,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 894,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 895,   best chromosome: 0526958477019879299307315976720015602784, smallest error: 321.70394348819605\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 896,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 897,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 898,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 899,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 900,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 901,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 902,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 903,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 904,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 905,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 906,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 907,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 908,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 909,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 910,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 911,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 912,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 913,   best chromosome: 0526950477019879299307315976720015602784, smallest error: 321.703947100388\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 914,   best chromosome: 0526950477019879299307315976720015607784, smallest error: 321.70198685705566\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 915,   best chromosome: 0526950477019879299307315976720015607784, smallest error: 321.70198685705566\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 916,   best chromosome: 0526950477019879299307315976720015607784, smallest error: 321.70198685705566\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 917,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 918,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 919,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 920,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 921,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 922,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 923,   best chromosome: 0526950477019879299307315976720015607984, smallest error: 321.70190930080435\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 924,   best chromosome: 0526950477019879299307315936720015607984, smallest error: 321.70176502748404\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 925,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 926,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 927,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 928,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 929,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 930,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 931,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 932,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 933,   best chromosome: 0526950477019879299307315932720015607984, smallest error: 321.7017506597969\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 934,   best chromosome: 0526950477019879299307315932720015607974, smallest error: 321.7017545641775\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 935,   best chromosome: 0526950477019879299307315930720015607974, smallest error: 321.7017473802995\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 936,   best chromosome: 1526950477019879299307315930720015607974, smallest error: 375.97499682020947\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 937,   best chromosome: 0526950477019879299307315930420015607974, smallest error: 321.7017463027333\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 938,   best chromosome: 0526950477019879299307315930420015607974, smallest error: 321.7017463027333\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 939,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 940,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 941,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 942,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 943,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 944,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 945,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 946,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 947,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 948,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 949,   best chromosome: 0526950479019879299307315930420015607974, smallest error: 321.70174630183845\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 950,   best chromosome: 0526958479019879299307315930420015607974, smallest error: 321.7017427241717\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 951,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 952,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 953,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 954,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 955,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 956,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 957,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 958,   best chromosome: 0526958475019879299307315930420015607974, smallest error: 321.70174272596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 959,   best chromosome: 0526958975019879299307315930420015607974, smallest error: 321.70174250235596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 960,   best chromosome: 0526958975019879299307315930420015607974, smallest error: 321.70174250235596\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 961,   best chromosome: 0526958975019879299707315930420015607974, smallest error: 321.7017424711956\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 962,   best chromosome: 0526958975019879299707315930420015607974, smallest error: 321.7017424711956\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 963,   best chromosome: 0526958975019879299707315930420015607974, smallest error: 321.7017424711956\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 964,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 965,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 966,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 967,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 968,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 969,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 970,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 971,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 972,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 973,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 974,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 975,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 976,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 977,   best chromosome: 0526958975019879299707315930420015707974, smallest error: 321.66725444016157\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 978,   best chromosome: 0526958975019879299707315930420015797974, smallest error: 321.6433775349753\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 979,   best chromosome: 0526958975019879299707315930420015797974, smallest error: 321.6433775349753\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 980,   best chromosome: 0526958975019879299707315930420015797974, smallest error: 321.6433775349753\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 981,   best chromosome: 1526958975019879299707315930420015797974, smallest error: 376.356032850277\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 982,   best chromosome: 0526958975019879299707315930420015797974, smallest error: 321.6433775349753\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 983,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 984,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 985,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 986,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 987,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 988,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 989,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 990,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 991,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 992,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 993,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 994,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 995,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 996,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 997,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 998,   best chromosome: 1526958995019879299707315930420015797974, smallest error: 376.35603412925065\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 999,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
      "select_the_least_errorjk10**\n",
      " GENERATION: 1000,   best chromosome: 0526958995019879299707315930420015797974, smallest error: 321.64337752915134\n",
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      "text/plain": [
       "<Figure size 1500x1800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X0rNbb52xz0g179uStUU+++4T3fRTZfMzOrFPfx2YqVTl/mVJPfffB1bccMVNUlRUKMMeHxqwWWKZlPAHHSrNDmwuYz9+K+D0KtueVbFYv3Rv+Td8XjWlVYkm/FWtUkWyI2jqqpqAHty2g3z905emJrQqA6fqE9i7u/WyopJ4+FOZI73LCwD+qJECkLKefPkx2bdvr27u1ap5a8kvyJfZ8/7VCQUObNxUzjr5nJjMp1WL1voC9/nXnpEtWzfrLGsT/5ggu7PLphCPxFUXXqP7Wt10z3W6hktdQO/dlytLViyRCX/8JBM/+lX3X1J9PVSa8qOP7K5rNFS/EpUeWl2Aepu7qcfqQvnND0br/jOqBkslJvAfR8ufmk+gO/7qrn2wlN7hqKZ0KuOcqgFYv2m9Thww+Z9J8uukX+Sy8640Zeyz6pqLB8vCpQv0eqqxovr1GaCbBqrxldRYVGp7qBpDf32OPk7X8KgU7Go79TvWnK1QLdMtg27XqefVMvftdaKeXtW2qP525512gR4EOBpdOneV/7vtQV1OB17SX07td1pxOc3Pl1XrVsn3E7/R6eS921sFyo8OfUL3iVPjOZ118tm69kylyVdJIVQtnxrY14pYrN8Rhx6h+yOpvluXnH2ZDorVuFTGGwteh7Q/VDene+bVJ+XQgzvpMqZqUwO564a7dfrzS248X84+5Txf+nPVR/Gmq0qb5EZKjWulErX0OeY4XTu6fcc2+ejrD3RfviMPO8ry9wFIfgRSAFKW6ksy4fcfdYavT7/9RPIL8nRCCTUe1HWX3xhwoN5oqFTqI558XZ56+XEZPe4N3T9EBW8Xn32pnH316VHVsIx96X0Z/f7renyqbyZ8pfufqL5RN191i1SvXpzs4tT+Z8in336s7/yrlNL16zbQNSxqzB1v/6QG9RrIg3c+osfEUoGXqhF6e/h7YQOpR198KODr6rPRBlJqmV59cqROyqGCWTX46oGND9T7SWXuKw8VBL348Evy9U9f6YQCr7w1XAckKsBV4y2p7w8UGKpg6/iefXWwpbIiBtou115ynbRs1lLe/WSsvPbOq/q1Axo01lkhgwUBkbrwzIt1LZlKgKL2tUovnplZQZo1aaaDaJX50BhgqhT2Kh24Gr/rgy/f101A1X7v1KGTnBcm5Xow5V2/2rXq6ADuuRFP6/Go1O/q1H5n6GQOKkum//qqbHxq3Cm1zmpssGDzUEH7qOfG6FTnqsyoxBcqk6MaM63pAc0sr6eaz4ZN6+TL8Z/Jjl07dPNA9X03X32r1Cj5TQGAUdr8P5ZY6+UMAAAAACmOPlIAAAAAYBGBFAAAAABYRCAFAAAAABYRSAEAAACARQRSAAAAAGARgRQAAAAAWMQ4UiJSVFQkW7ZtkWpVqumBAgEAAACkJo/HIzl7c6RhvYa+cRcDIZAS0UFU33P7JHL/AAAAAHCwXz77Uxo3aBz0fQIpEV0Tpaxdu1Zq1qwpdsnPz5cJEyZI//79pUKFCrYtB9yDMgPKDDjOwGk4N8HtZWb37t3SrFkzX4wQDIGUiK85nwqi7A6kqlatqpfBCYUIzkeZAWUGHGfgNJybkCxlJlyXH5JNAAAAAIBFBFIAAAAAYJHtTfs2b90kL456Xv6a9qfs27dXmh/YQh6/5yk59ODD9Pv3PTVMvv7xS9NnenbrLW8896bv+c7dO+XJlx6T3yf/qjNr9OszQO655f+kWtXQ7RoBAAAAwHWB1K49u+TSmy+SbocfLa8/O1rq1q4rq9etlpo1apmm69Wttzx+z9O+5xUrVjS9P+yxIbJ1+1YZ88JYyS/Il/ufvlcefv4Bee7BFxO2LgDcobCwULfFTlVq3TMzM2Xfvn16WwCUmchlZGTo3w9DpQCwPZB684M3dErBJ+4tDZKaHtCszHQqcGpQr0HA71i+aplMmv6XfDzq89JarNsekBuGXStDbxwmDes3iuMaAHCT7OxsWbdunR4fIlWpdW/cuLHOUsrFICgz1qkO8QcccECZm7oAUo+tgdRvf/+qm+nd8eCt8s+c6TroufDMi+W80y4wTTdj9nTpfUZ3XVN19BHd5dZrbpfatero9+bMny01q9f0BVHKMV166CZ+/y2YIyf26V9mvnl5eZKXn+d7np2b7btTa+edau+8U/luOayhzERO1b6o4KFatWpSr169lA0i9CCDOTl6O6TqNoA1lJnS7aCOuVu3bpUVK1ZIq1atQg7Umco4N8HtZSbS5bA1kFq3ca18/PUHcsV5V8ngS6+XuYv+k6deflynPTzzpLN9zfpUMNS0cVNZu2GNDB/9olx39zXywWuf6Cr2rO1bpW6deqbvVdXutWrUkqztWQHnO3rc6/La2FfLvK7y16s7TXabOHGi3YsAl6HMhKeOC6ompm7duo45UNtF3UlP9W0AaygzpVR6ZlWzrY67NI8NjXMT3FpmcnNznR9IFRV55NCDDpXbBw/Rzzu0P0SWrVwqn3z9kS+QGtj3VN/07dscpP+ddNGJMmP2NOnepUdU8732kuvlivOvNtVI9T23jx4EzO5xpFQB6tevn6Ny6MO5KDORU32CVI1UjRo1pHLlypLKd9X37NmjtwM1UqDMRHcsqVKlihx77LEpfSwJhXMT3F5m1IC8jg+kVL+nNi3bmF5r3aKNTPzzp6CfadakudSpVUfWrF+jA6n6dRvI9h3bTNMUFBToRBb169YPemctUNtmteOcsPOcshxwD8pMeOrOsQocVFOcVG6OU1RUpP96twVAmbFG/W7U74fjbnhsI7i1zES6DLaeRY849EhZuWal6bVV61ZJk0YHBv3Mpi2bdLrz+iXJJzp3PFx2Z++W+Yvn+aaZ9u9UfbHQ6ZDOcVx6AAAAAKnK1kDq8vOu1Akh3nhvpE57/t3Eb+Wzbz+Wi866RL+fk5sjz498RieUWL9xnUydOVlu+b8b9FhTvbr21tO0adlW96N66Ln75b+Fc2TW3JnyxPBH5eQTTiFjHwCUQ8uWLWX48OG+5+ou/FdffcU2hW1WrVqly+Hs2bPZCwBSO5A6rEMneenxETL+l+/lzKtOkVHvjpBhN98np/Y7Xb+vkkksXr5Ybr7vehl46QB54Nn/k0Pad5R3X/nA1DTvmQdekFbNW8ugO67Uac+PPKyLPHzXYzauGQDEhsoQdsMNN0jz5s2lUqVKOmHGgAED5O+//3b1Jn744Yfl8MMPj2g6deHs/+/ggw8WJ3jnnXeka9euOlGR6nem+s189913ti6Tykz73HPPyZFHHqmzM9aqVUs6d+4s999/v2zYsEHc4sorr5QzzzzT9FqzZs1k48aNcuihh9q2XADgiD5SynE9jtf/AqlcqbKMfv6tsN9Ru2ZtBt8FkJTOOeccfWGsLthbt24tmzdvll9++UW2bTP3DU1mHTt2lJ9//rlMFsZg1Pby7wdr7CNnRajP3XXXXfLqq6/K448/ri/4VWfp999/X8444wx56aWX5Oabb5Z4CbSOyv79+3XipP/++08eeeQR6dmzpzRo0EBWrlwpH374obzyyivy1FNPiZ3Udoq2D4S6wapuJgCAE9DTGEDKZq/LzSuw5V+kAwLv3LlT/vrrL3nmmWfk+OOPlxYtWki3bt3k3nvvldNPL665V9SF/qhRo+TUU0/VNSMdOnSQKVOmyLJly+S4447TtRI9evSQ5cuX+z6jHqsL/kaNGkn16tV1rYp/sBKOyoJ4/vnnS+3atXVaefV9qumV1++//66XV81fTaMu6levXi1jx47VF/lz5szx1TCp18Klrjf+q1+/vqkJ4mOPPSaXX365zrw6ePBg/X1qnt98840ccsghujZvzZo1smPHDj1dnTp19LY6+eSTZenSpb7vCvY5f1OnTpUXXnhB1/yogKpt27Z6uz/xxBNy++23y5133qm3j8r8pDK8/fDDD6bPf/nll7oGy5tiN9y29NbOqO9v0qSJHHTQQQG31f/+9z+ZNGmS/Prrr3LrrbdKly5ddG2mqil7/fXX5cknn/RNq/oSq6BKjYekllHVWn322Wem/af2jQrcVflT5USVo8WLF5vm+fXXX+vaL5XBTgX7at+qpE9e6jtGjhypy6wqC2odVIA6aNAg37zV+qjg01gTqW4eqO/2lhG1PIGa9v3xxx+6nKl9pQbKveeee0zzV78BtS3uvvtuvW1V+VHf76V+j+q5t9ZXbV81PQA4vkYKAOywN79QDnkweIbQeFrw6ACpWjH84VdduKp/ql9S9+7d9UVeMCqQePHFF/W/YcOGycUXX6wvalXQpS4Qr776al1D8v333+vps7OzZeDAgfqiVn3vu+++K6eddpq+SFbTR1KroJoYHnPMMTrYU8GOqpk56aSTdG2IqsFRF/7XXnutrglRNSjTp0/XF8EXXHCBzJs3T3788Udf8Kaan5XH888/Lw8++KA89NBD+rlaJhWkqCB0zJgxehDmhg0bykUXXaQDJxUoqaBLbSu1HRYsWOCrJQn0OX9qndS+ue6668q8N2TIEL0fPv/8cx1UqQD3gw8+0EGb17hx4/T2UcFcuG3prXlSAY1a5lDjrKjlUumDjzjiiIDvG1PeqyBK1aCpAKtdu3by559/yqWXXqprsFTg5fXAAw/o5VGB/I033qjLkrdpqVpeFZi+/PLL0rt3bx2gq0BW8e4LRQUqTz/9tO5zp9ZPBXFNmzaVTz/9VG/jyZMn68+pQEgFlCo4XbhwoQ5E3377bf0dKgjyb5q4fv16vf9UoKnK8KJFi3SZU0GdMVhSQZkKbqdNm6ZvMqjpVWCvtpXaTyoA/eijj3Tt56ZNm3SQDwDhEEgBgEOpC05VQ6IuDNXFrrrrry5wL7zwQunUqZNp2quuukpfgCoqOFAX5eoCWF2gK7fddpuexkvVPhgvtlUgpmpJVIARSZO0jz/+WF8Mq2DDe3GuLnhVjYqqOTjqqKNk165dOoho06Z4mAtVY+OlghBvTVM4c+fO1dMbqQt+tU28TjjhBB3AeKkLfBWgvPbaa3pdFW8ApYIAVbPiDWhUvxsVrJ533nn6Nf/PBbJkyRK9XoGa16kaDRXwqGmUSy65RC677DIdoKnASQUHKqBV2zuSbama6imqNkdNE2iexuVSNTBGZ511li/4UuVGBS2qCaCqnVKBrCorigq8VW2Wqt00BlKqbKgaS7VOqrbnlFNO0WMpqWBF1T6p16644grfd6jpVe2PMZBSgb2x/Cnqs16qZkoFOJ988okux2p/q5oqtZyhyojaT2r/qSaW3r5zKthSvwEVWHubZKr19i6PChrV9CowVYGUqnFU8zjxxBN1MK1uJKgaLgAIh0AKQEqqUiFD1wzZNW8rfaTUhasKDFRzMtVE7Nlnn9UX1OquupcxsFLN9ZTDDjvM9Jq6+PUOMqhqpB599FF9Qa8676umUHv37g3YjC0QdcdeNR1UzdOM1DxUrYS6+FfLpwI5dbGqLlLVBbKqcbBKNftSAZCR/+DpKnDzpwIO43ZRNRwqeDv66KN9r6naEPX96r1gnwsm0iaaqsZEXaCrdVBBsKoBUcuvtkkk29JL7c9QQVSoYCMnJ0fXGqlaJ0XNTwV2at8YqZpD/9os47bw7r8tW7bogEMtuwpMVc2ml2q2p5bdGzgG2z8jRoyQt956S5c5VfbUvCNJQGKk9psKBI01baqmSZXvdevW+WpX/fenWg+1DooKoFVNmQoCVS2g2l+qdjZUPzwAUDhKAEhJ6sIrkuZ1TqDu/KsLXvVP1TJdc801+u66MZAydt73XlQGek3VfKi79EOHDtW1EapJnOrfo+7+n3vuufpiNhLqQlX1v1E1Ov5U0zBvrYrqa6Ka8KlaF5U1TtWMqGaKVqjgQS1jKKq2xp9aJ+MFdqQi+Vz79u117U2gpA+qRkQFrGoa7/Krbaua96lASv1VzRu9F+qRbMtg6+hP1bb492HyBj+qaZyXmqeiAukDDzSP3ejfhDRYOfJ+j6pZOvvsswOW22DLrprRqeZ7qp+ZCoRUEKn6m6mmd/Hgn9xCrYd3HVSNltpm6vegyqdqvqiWRfW9csLAoACci2QTAOAyKgmCqmEoD9W8SwViqtmXqulQTZuMyQ3CUc0MVVM51X9IBTnGf8b+Tqp2Q/XTUvNTKatVEOENLlTNRSKppoWq5s14sa6yH6qLaLVNrVABkQoiVDM4fyo4VRfgqjbRSzXvUwHl/PnzdSII9dzqtoyE6gOmgoF///035HTGRBr+81SBRaTUsqvt5/8d6l+oDIne5pUqaFFlRE1vrH2LtIx4E6sYawfVd6vATPXBshI8q1ooVWunmlOq71RNSgEgFAIpAHAodZGv+v6ohAAq6YBKYa0656umfSqrW3moC9cvvvhCZz9TzbNUHxbvHfpIqEBAZc5Ty6GaHaplUxegqgZKNalSz1UApS5IVaa+CRMm6GDB209KZdpT06j5Z2Vl6b4wwajgRyUAMP5TaeCtUrU1anlVnzNVm6TWW/W1UjUyVrenqkVR/c5UzZ6qVVFBgEp0oGrdVPY59ZoxIOnTp48OVtV2U/2BjM0Lw21LK+644w69bH379tXLMWvWLP19P/30k24WqtKHKyrQUDVCanqViEEtv5pWpUdXzyOl+iGpJA+qVkoFiaqpnaptUtsh3L74559/9HKpfl2qpnXGjBmmaVQZUeVeBWqqjKi+a/5UIKYyHt5yyy16+6ssf6q2ViWWiDTVveqH+Oabb+oEKCtWrNC/NxVYqeQaABAKgRQAOJTqcK8uuFVGMXUhrmp01AWnCgRUZ/nyUBf6KgW4qhVQd+JVXyZVuxAp1fdF9bdRfVBUsy4VIKl01qpvjOr/o95XF7aqVkY1cVMZ2W666SZfljv1uuqPotJqq+ZrKttcMOoCXTVPM/6L9iJXNTdUzehUEgwVcKiajPHjx0fVhEv1q1H9j9Syq32j+gGpbaISV6gLe/+mZKq2SAVvxtqoSLalFao5nUqioJItqHXt1auX/j6VPVD1HVLL5qWSQqjypLL3qWnU/lBN/VSgFylVbtQAxCpQVgkpVLNNVV7D7R9VDtS6qiaOqoyrmwYqKDJS5Vz1X1PbVZWRQINQqyBY7T+VEVIlB7n++uv1tgsXyBmppB6jR4/W20f1pVJN/L799lvdfw4AQkmb/8eSyHrLJrHsnGw5euCROsOU1ZNWLKm7beqE4O2YDFBmYkddlKo78+oi0dh3I9WoWifVf0cd66wOTovURJkx41gSHtczcHuZUedJ1bR62vhZUr2aOWusEWdRAAAAALCIQAoAACABFm/aI9tzIsuMCcD53JH7FwAAwMWWbt4jA4YXj+O16ulT7F4cADFAjRQAAECcTV25nW0MJBkCKQS0KitHnv5hkWRlB09JDLiRcbwZAOAYAiBaNO1DQGeM+Ft27c2Xeet3yfvXlI53AriVd/ycvLw8PUYMAEQjNzdX/3VCZjEA9iKQQkAqiFJmrt7BFkJSyMzM1OP1bN26VV8ApWrqb5XKWgWTKoVzqm4DWEOZKa3NVkHUli1b9NhT3pszAFIXgRSAlKAGRFUDuaqxpFavXi2pfDG4d+9eXSuntglAmbFGBVGNGzd2RMEpKCySzAxuiAB2IZCCI2zYuVfu/uw/uapnS+nboZHdi4MkVbFiRWnXrp2ukUnlQQ///PNP6dOnD02TQJmxSNVmO6Um6u9lWXLJmGnyyOkd5YoeLe1eHCAlEUjBEe7/ap5MWpal/5EWFvGkmrNVrlw5ZTeyuggsKCjQ24A+HqDMuNctH/6r/z70zXwCKcAm1AfDEcgOCAAAADchkAIAAAAAiwikAAAAAMAiAikAAAAAsIhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAAACwikAIAAAAAiwikAAAAAMAiAikAAAAAsIhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAAACwikAIAAAAAiwikAAAAAMAiAikAAAAAsIhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAAACwikAIAAAAAiwikAAAAAMAiAikAAAAAsIhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAAACwikAIAAAAAiwikAAAAAMAiAikAAAAAsIhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAAC3BVKbt26SYY/fJT1O6yZH9jtMzrzyVJm3aK7vfY/HI6+8+ZIce1ZP/f6gO6+Q1etWmb5j5+6dcvdjQ6TbyUdI91O6yAPP3Cc5uTk2rA0AAACAVGBrILVrzy659OaLJDMjU15/drR88+54GXrTPVKzRi3fNG9+OFrGffGuPDTkEfnw9U+lSuWqMviuq2X//v2+aYY9NkSWrVoqY14YKyOeGiX/zJkhDz//gE1rBQAAACDZ2RpIvfnBG9K4QWN54t6npVOHztL0gGbSs2svaX5gc19t1HufviPXXXajnNDrRDmozcHy1H3PypZtW+SXSRP1NMtXLZNJ0/+SR4c+IZ0O6SxdOh0l9932gPzw6/eyJWuznasHAAAAIEll2jnz3/7+VXp26y13PHir/DNnujSs30guPPNiOe+0C/T76zaulaztW6V7l2N8n6lRvYYOuubMny0D+56q/9asXlMOPfgw3zTHdOkh6enp8t+COXJin/5l5puXlyd5+Xm+59m52fpvfn6+/mcX77ztXIayPAlZHhU0ezlr/Z3NmWUGTkaZAWXGHoWFhWV+h8l63uQ4A7eXmUiXw9ZASgVKH3/9gVxx3lUy+NLrZe6i/+Splx+XChUqyJknnS1Z27P0dPXr1jd9rl6d+jrAUtTfunXqmd7PzMyUWjVq+T7vb/S41+W1sa+WeX3ChAlStWpVsdvEicW1bfbK9B34x48fH/e57dyZISJp+nEi5pdsnFFm4CaUGVBmEmv+JnWOy4jZeS4vz/nnTY4zcGuZyc3NdX4gVVTkkUMPOlRuHzxEP+/Q/hBZtnKpfPL1RzqQipdrL7lerjj/alONVN9z+0j//v2lZs2aYmf0qwpQv379dDBpp9umTNB/MzIyZODAAXGf35g1U2Vtzm79eODAgXGfX7JwUpmBO1BmQJmxx47pa+XTlQtjdp57eM5vklOQ78jzJscZuL3M7N5dfE3q6ECqQb0G0qZlG9NrrVu0kYl//mSqiVI1Sw3qNfRNs21HlhzctkPJNA1k+45tpu8oKCjQiSz8a7K8KlasqP/5UzvOCTvPKctRLC0hy5KWVnxXTXHOuruHs8oM3IAyA8pMYqkbk8bfXyqcNznOwK1lJtJlsDXZxBGHHikr16w0vbZq3Spp0uhA/Vgln1CB0rRZU3zvZ+dky38L50jnjofr5+rv7uzdMn/xPN800/6dKkVFRTr5BAAAAADEmq2B1OXnXakTQrzx3khZvW61fDfxW/ns24/lorMu8d1tuey8K2TUuyPl179/kSXLF8u9Tw6VhvUaSt9e/fQ0bVq2lV7destDz92vA6xZc2fKE8MflZNPOEUnrwAAAACAWLO1ad9hHTrJS4+PkOFvvCAj3x0hTRs3lWE33yen9jvdN82gi66VvXv36nGh9mTvliMP6yKjnntTKlWq5JvmmQde0MHToDuulPT0NOnXZ4Dce+v9Nq0VAAAAgGRnayClHNfjeP0vGFUrdcug2/S/YGrXrC3PPfhinJYQAAAAABzUtA8AAAAA3IhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAAACwikAIAAAAAiwikAAAAAMAiAikAAAAAsIhACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAASyOPxsL2BJEAgBQAAAAAWEUgBAAAAgEUEUgAAAAlEyz4gORBIAQAAAIBFBFIAAAAAYBGBFAAAQAKRsw9IDgRSAAAAAGARgRQAAAAAWEQgBQAAkEAMyAskBwIpAAAAALCIQAoAAAAALCKQAgAASCCy9gHJgUAKAAAAACwikAIAAAAAiwikAAAAEshD2z4gKRBIAQAAAIBFBFIAAAAAYBGBFAAAQAJ5yNsHJAUCKQAAAACwiEAKAAAAACwikAIAAEggsvYByYFACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAASCCy9gHJgUAKAAAAACwikAIAAAAAiwikAAAAEsgjHrY3kAQIpAAAAADAIgIpAAAAALCIQAoAACCByNoHJAcCKQAAAACwiEAKAAAAACwikAIAAEggcvYByYFACgAAAAAsIpACAAAAAIsIpAAAAADAIgIpAACABPKQ/xxICgRSAAAAAGARgRQAAAAAWEQgBQAAkECkPweSA4EUAAAAAFhEIAUAAAAAFhFIAQAAJBBJ+4DkQCAFAAAAABYRSAEAAACARQRSAAAAiUTaPiApEEgBAAAAgEUEUgAAAABgEYEUAABAAnlo2wckBQIpAAAAALCIQAoAAAAALCKQAgAASCAG5AWSA4EUAAAAAFhEIAUAAAAAFmWKjUa8/bK8NvZV02utmreS7977ST++8rZLZcbs6ab3zz/9QnloyKO+5xs2b5DHXnxIpv87TapWqSpnnHSW3H7tEMnMtHXVAAAAAmI8XiA52B5ttG3VTsa8MNb3PDMjw/T+uaeeLzdffZvveZXKVXyPCwsL5cZhg6V+3fry/oiPJGvbVrn3ybslMyNTbh88JEFrAAAAACDV2B5IZWRkSIN6DYK+X7lylaDvT54xSZavXiZjXhyrgylpJ3LLoNvlxVHPyY1X3SIVK1SM45IDAAAASFW2B1Jr1q2W487uJZUqVpTOHY/QNUlNGjXxvf/9xG/ku4lfS/26DeS4HsfL9Zff5KuVmj1/trRr3b44iCrRs1svefTFh2T5ymXSof0hAeeZl5cnefl5vufZudn6b35+vv5nF++87VyGsjwJWR6PIYWRs9bf2ZxZZuBklBlQZuyhWtF45enrjbSkPW9ynIHby0yky2FrINWpQ2d54p6npWXzVrJ121YZOfZVufyWi+Xrsd9JtarVZWDfU6VJ4wOlYb2GsmTFYl3TtGrNSnnp8RH681nbt0q9OqVBlOJ9rt4LZvS418v0zVImTJggVatWFbtNnDjR7kXwFQ114B8/fnzc57Zzp2rSWXxSScT8ko0zygzchDIDykxizd+kznHF3Rd++flnqV6hfN+Xl+f88ybHGbi1zOTm5jo/kOrd/Vjf44PaHKwDq34XHCc//vaDnHPKeTqxhFf7NgdJ/XoNZNAdV8ia9Wuk+YHNo57vtZdcL1ecf7WpRqrvuX2kf//+UrNmTbEz+lUFqF+/flKhQjmPsOV025QJvqaXAwcOiPv8xqyZKmtzduvHAwcOjPv8koWTygzcgTIDyow9dkxfK5+uXKgf9z3xRKlXrXzdDx6e85vkFOQ78rzJcQZuLzO7dxdfkzq+aZ9RzRo1pUXTlrJm/eqA76tAS1Hvq0BKNfebu+g/0zTbdmTpv+q9YCpWrKj/+VM7zgk7zynLUSwtIcuSllbaxME56+4eziozcAPKDCgziaVuTHqpzMLlPWa74bzJcQZuLTORLoOjxpHKyc2RtRvWSoO6DQO+v2hZ8Z0cb/KJwzseLktXLJFtO7b5ppk842+pXq26tGnZNkFLDQAAACDV2Foj9dxrT8txPU7QySW2bNsiI956WTLS02Xgiafq5nvf//yt9Ol+rNSuWVsWr1gsz776pBzVuatuBqj06NpL2rRoK/c8MVSGXD9UsrZnyStvDpeLzrwkYI0TAAAAALg+kNq8dZMMffRO2bl7h9StXVeOPKyLfDDyU/14f95+mTpzsrz32Tuyd1+uNG5wgJzYZ4Bcf/mNpmry154epbP0XXLjBTqbnxqQ1zjuFAAAgO0MWfYMDwG4mK2B1PMPDQ/63gEND5B3Xh4X9jtUVr/Xnx0T4yUDAAAAAJf0kQIAAAAANyCQAgAAiDNjaz6P6RkAtyKQAgAAAACLCKQAAAAAwCICKQAAgDgzZeqjZR+QFAikAAAAAMAiAikAAAAAsIhACgAAIM48xgF52dpAUiCQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAgzkzZz+kkBSQFAikAAAAAsIhACgAAAAAsIpACAACIM2NzPg8J0IGkQCAFAAAAABYRSAEAAACARQRSAAAAcUbWPiD5EEgBAAAAgEUEUgAAAABgEYEUAABAnHkMafsYjxdIDgRSAAAAAGARgRQAAAAAWEQgBQAAYFMzPwDuRSAFAAAAABYRSAEAAACARQRSAAAAcWZszUfLPiA5EEgBAAAAgEUEUgAAAABgEYEUAABAnHkYhhdIOgRSDrJme668ODdDrhw70+5FAQAAABBCZqg3kVj7C4pkdXaa7C7azaYHAAAAHIwaKQdJT0vTf4tI5wMAQFIhax+QfAikHCS9OI6SIgY8BwAAAByNQMpBqJECAAAA3IFAykFKWvYxUB8AAEnG2NiEDH5AciCQcmCNVCFt+wAAAABHI5BykIySTlIkmwAAAACcjUDKQWjaBwBAciJrH5B8CKQchGQTAAAAgDsQSDkI6c8BAAAAdyCQcpA0b9s+3QSAwaQAAEgWxkx9nOGB5EAg5cAaKYXEfQAAAIBzEUg5sI+UQuY+AAAAwLkIpBxbI0XFPwAAyZm1j3M8kAwIpBzbR8rWRQEAAAAQAoGUg1AjBQAAALgDgZRj+0jZuigAACBOOMUDyYFAyqFN++gjBQAAADgXgZRDm/Z5iuxcEgAAAAChEEg5COnPAQBITsZMfSSUApIDgZSDkGwCAAAAcAcCKYf1kUor6YJKsgkAAADAuQikHMbbTYpkEwAAAIBzEUg5jDdxH4EUAADJw9wvigToQDIgkHJsjZTNCwIAAAAgKAIpp9ZIEUkBAAAAjkUg5dAaKVKjAgCQPIwNTTjHA8mBQMph6CMFAAAAOB+BlEN3CMkmAAAAAOcikHIYkk0AAJB8jM35yCcFJAcCKYc27fPQgBoAAABwLAIph6FGCgAAAHA+AimHIdkEAADJx2No0EejEyA5EEg5tkaKFtQAAACAUxFIOUy6r4+U3UsCAAAAIBgCKYehRgoAgGTP2sfdUiAZEEg5to+U3UsCAAAAIBgCKYehRgoAAABwPgIph2EcKQAAko+xoQn9oIHkQCDl0B1SWGTzggAAAMe3YAHgokAqvyBfrr7jclm9blV8lijFMY4UAAAIh67UgAsDqQqZFWTJ8sXxWRrQRwoAgGRkaM9H0z4ghZv2ndr/DPn8+09jvzTwBVIcZAEAQDA07QPslxnNhwoLCuTj8Z/J1JlT5JD2HaVK5Sqm94fdfF+sli/l0LRPBZEeSfNuCAAAUAZN+wCXBlJLVy6VDu066ser1pr7SnEBHKv05+X8IgAA4MysfYRBQOoGUmNfei/2SwKNGikAABAO7TYAlwZSRpu2bNJ/GzdsbPmzI95+WV4b+6rptVbNW8l37/2kH+/fv1+efe0p+eHX8ZKXnyc9u/aSB+54WOrXre+bfsPmDfLYiw/J9H+nSdUqVeWMk86S268dIpmZ5V41m/tIpW6VlFp1WvYBABDiXMnGAWwXVbRRVFQkr7/7mrzzyVuSuzdXv1atajW54vyr5brLbpD09MhzWLRt1U7GvDC2dIEyMnyPn3n1Sflj6u/y4iMvSY1qNeSJ4Y/KbQ/cLONGfKTfLywslBuHDdaB1fsjPpKsbVvl3ifvlsyMTLl98BBxo/SSSKqIcaQAAEgaxvujKXyvFEgqUQVSL415Ub74/jO5Y/BdcsRhR+rXZv03U14b+4rk5e2X2669M+LvysjIkAb1GpR5fU/2Hvl8/Gfy7AMvSPcjj9GvPX7PU3La5SfLnPmzpXPHw2XyjEmyfPUyGfPi2OJaqnYitwy6XV4c9ZzceNUtUrFCRXFvHymOsgAAIPT1AgCXBVJf//iVPHL3E3JCz76+1w5qc7A0atBIHvvfw5YCqTXrVstxZ/eSShUrSueOR+iapCaNmsj8JfOkoCBfjunSwzdt6xZt5IBGTWT2/H91IDV7/mxp17q9qalfz2695NEXH5LlK5dJh/aHBJxnXl6ebirolZ2brf/m5+frf3ZR8/Y2acsvKLB1WUp5ErIcxqaMefn5kuGtmkNI3n3jjLICN6DMgDJjj8KiQt/jghic4403XJ12DuA4A7eXmUiXI6pAateendK6eesyr7dq3lp27d4V8fd06tBZnrjnaWnZvJVs3bZVRo59VS6/5WL5eux3krUtSypUqCA1a9Q0faZenXqStT1LP87avlXq1anv935933vBjB73epm+WcqECROkatWqYqd0KW7a+M/MWVKwys5aqUxf88nx48fHfW47d6r1Lg6efvjhB18TR0Rm4sSJbCpYQpmBVZSZ8lm2RnV7KO76MGnSJFldvXzfl5dXet5MxHk6GpQZuLXM5OYWd12KSyClap8++OJ9ue+2B0yvq9cOantwxN/Tu/uxpu9UgVW/C46TH3/7QSpVrCzxcu0l1+v+XMYaqb7n9pH+/ftLzZrmwC3R0e8r83/Vj4844gg5+VDrCTxi5bYpE3xNLwcOHBD3+Y1ZM1XW5uzWj08++WRqpCyUGXXQ6devn77xAFBmEGscZ2Jj0cSlMnH9Sv24V69e0rFJ+a43HvnvN8kpKL5rPnDgQHESygzcXmZ27y6+Jo1LIDXk+rvlhnsGy5SZk+Xwjkfo11RzO5XB7/Vn3pBoqdqnFk1bypr1q+WYo3rqjbp7z25TrdS2Hdt8Tfnq120gcxf9Z/qObTuyfO8FU7FiRf3Pn9pxdu+8NJ2HJ03S0jNsXxbvEiViOYzjj6mMi5kZkScsgTPKLtyFMgPKTGKlG85r6jxX/mN26XnTqcd/jjNwa5mJdBmiulrteng3Gf/+T3Ji736yO3u3/ndin/7y/fs/SpfOXSVaObk5snbDWmlQt6F0bH+oZGZWkKmzpvjeX7lmhWzcvMEXvB3e8XBZumKJDq68Js/4W6pXqy5tWrYVN2IcKQAAkg9Z+4DkY7lGKr8gX64bOkgeGvKopaQSgTz32tNyXI8TdHKJLdu2yIi3XpaM9HQZeOKpUqN6DTln4Lny7IinpFaNWjo4evKlx3QQpRJNKD269pI2LdrKPU8MlSHXD9V9p155c7hcdOYlAWuc3DWOlM0LAgAAHIuuxIALA6kKmRVkyfLFMZn55q2bZOijd8rO3Tukbu26cuRhXeSDkZ/qx8qwm++TtPQ0uf3BWyS/ZEDe++942Pd51X/ntadH6Sx9l9x4gVSpXEUPyHvz1beJ2w+MhUWpG0ml7poDABAZzpWA/aLqI3Vq/zPk8+8/lTuvG1qumT//0PCQ71eqVEkeuONh/S+YJo0PlNefHSPJgqZ9AAAkd+DjIQwCUjeQKiwokI/HfyZTZ06RQ9p31DVBRqomCdGhaR8AAIj0egGAywKppSuXSod2HfXjVWtXBc2+Buu84ycZB9pLNSm86gAARIRTJeDCQEoN0Hrz1bdKu9btdRIIxJY3DE3hLlIAACQdsvYBycdy+nOV4OHau66SPdmRDVQFa+gjRdtxAAAAOF9U40i1a9Ve1m1YG/ulgaGPFFVSAAAAQFIFUrcMul2ee+0Z+X3yb7J12xbJzsk2/UMsaqTYigAAAEBSJZu4Ydi1+u/N911vSi6halHU87m/LYrdEqZoZEuyCQAAkocx5Tn3SoEUDqTeHv5e7JcEGjVSAAAAQJIGUl0P7xb7JYFGHykAAAAgyfpIvfnBaNm3f5/v+ay5MyUvL8/3PCc3Wx598eHYLmGKIWsfAABJyNCej4RSQAoGUsNHvyA5uTm+59fffY1sztrke7533z759NuPYruEKYZxpAAAAIAkC6T876CQoTuegRRdUQEAAICkSn+O+EkviaRSOY5K5XUHACQn46mN0xyQHAiknFojxUBSAAAAQPJk7fv8u0+kapVq+nFhYYF89cOXUqdWHf08Z29p/ylEh/TnAAAAQJIFUgc0aiKfffep73n9ug3k2wlfm6dp2CR2S5fCNVKFKdy+zThoIQAAycDYzzyFT/FA6gZSEz/+LX5LAlONFKlRAQAAgCTrI/X1j1+axo/yysvP0+8hemTtAwAAAJI0kLr/mXtlT86eMq/n5ubo9xA9+kjR5AEAkHzMzflo2wekbCClmp2lea/4DTZt3STVq9WIxXKlLGqkAAAAgCTrI3XOoDN0AKX+DbrjCsnIyPC9V1hUKOs3rpNe3frEYzlTBuNIAQAAAEkWSPXtfaL+u2jZQunZrZdUrVLV916FzArSpHFT6Xds/9gvZQphHCkaPAAAknxAXlr2AakXSN145S36b5PGB8rAE06VihUrxmu5UhZ9pAAAAIAk7SN19JHHyPad233P/1s4R5565Qn55JuPYrlsKb1DilL4dhWp3wEAAJCUgdTdj94p0/+dqh9v3bZVrrnzSpm38D95ecz/5LWxr8Z6GVOyaR/BBAAAycN4fzR1b5UCySWqQGrZyqVyWIdO+vFPv/0g7VofJONe+1ieeeAF+frHL2K9jCmFpn0AAABAkgZS+YUFUrFCcf+oKTMny/E9T9CPWzVvrWuoEL20kvtUKd20z+4FAAAAAOIRSLVt2VY+/uZDmTlnhkz552/p1a23fn1r1hapXat2NF+JEtRIAQCQfDyG24QpfK8USCpRBVJ3XjdUPv3mY7ny9stkYN9T5eC2HfTrv/39ixx2cHGTP0SHPlIAAABAkqU/9+p2xNEy6Ztpkp2bLbVq1PK9ft5pF0jlylViuXwpWyO1aNMeeXfKKhnQsbE0qllZUgl36gAAAJCUNVLerHILFs/TKc9zcrP1axUqVJDKlVLroj/WKpbskdlrd8qDX8+XR76db/ciAQCAWGbt444hkLo1Uhs2rZfrhg6SjVs2Sl5+nhxzVA+pVrW6vPnBaP38oSGPxn5JU0SX+h6p2qiZLNyULTNX75DtOXl2LxIAAACAWNRIPfXK49LxoENl8nczpFLF0hqovr37ydSZU6L5SpSoXkHkoVM7yNU9W+nnRanYITUV1xkAAADJXyM187+ZMm7ER74U6F4HNj5QtmRtjtWypTRvXymCCgAAkgv3C4EUrpHyFBVJYVFRmdc3bd0k1apWi8VypTxvHJXK40kBAAAASRVI9ejaU977dKzveVpamuTk5siIt1+W3kcfG8vlS1lqmyqeFB9rAwAAAEiaQGrojffKv/NmyWmXnyx5efvl7keHSP8Lj9fN+u68fmjslzKlB+YlqAAAwO2Mmfo4tQMp3EeqccPG8sWb38gPv34vi5cvlty9uXL2KefKqf1OJ/15jKR7a6SIowAAQJAuAABcFkjpD2Zmymn9z5DTYrs8KOHLNZGCkVQKrjIAAJZwqgRcFEj9+vcvEX/pCT37Rrs8KJFe0uiSAyUAAO5nPJ/TFxhIsUDq1v+7MeIkCXN/W1SeZYKukaJpHwAACHK9xYYB3BNIzft9cXyXBCapnGwi9dYYAABrOFcCLs3ahwSmP+dICQCA65nO55zbgdSqkXr/s3cj/tJLz7082uVBifQUrpECAACh0bQPcFEg9a5hAN5QVEUKgVTs+kilolTMVAgAgBWcKQEXBVITPv41vksCE2qkAAAAAOeij5RTlVRI2VE5Y3eNEHfZAADJxpjyPBbnuTQHnbeBVBX1gLybtmyS3/7+RTZu2SD5+fmm94bdfF8sli2lpZckm0iVPlIpspoAAMQEp03ApYHU1JmT5eb7bpCmBzSTlWtWSNtW7WTDpvX6bkuHdh1jv5QpyHuniQMlAAAAkCRN+/73xgty5QVXy1djv5OKFSvJ8MdekZ8//UOO6txNBhx3UuyXMgWll3SSsqdpX+Ln6aT5AwAQz3NbLM5z5qZ95f8+AAkKpFasXiGnDzhLP87MyJD9+/dLtarV5Oarb5U3PxwdzVciWI1UihwdjW3HAQBAuPMmAFcGUlWqVJH8/Dz9uH69BrJmwxrfezt27Yjd0qUw74C8RTYfKUsWAwAAAEB5+0h1PqSzzJo7U9q0bCt9uh8rz414WpauWCI//zlBv4fYBTB21NSYBl+3o2kh99kAAEnGE+PznKlpX7m/DUDCAqm7b7pXcvfm6sc3XXWrfvzjr+OledMWMuyme6NaEATJ2leUGlsmRVowAgAQE5w2AZcGUs2aNPc9rlqlqjw05NFYLhP87jQlWqr0ywIAAAASPo6UV05uTpkL7+rVqpf3a1Neqo0jZZKCqwwASG7xzdqnvpBOzYArAql1G9fKE8MflRmzp8v+vP2mH7JKkjD3t0WxXMbU7iOVIkFFqqwnAACxwGkTcGkgNezxofrK97FhT0q9OvV9GeYQO95NakeNFAdnAAAAIA6B1OLli+TTN76QVs1bR/NxRCCtpIo+FYOaVFxnAEDqnN1icZ4jax/g0nGkDj3oMNm0ZWPslwY+6emplfghNdYSAIDY4LwJuLRG6tG7n5BHX3hQNmdtlnat2ktmpvlrDmpzcKyWL2X5aqTsGMeJozMAAAAQ+0Bqx87tsnbDGrn/6dIxo1Q/KZJNxE66jX2k7JaCqwwASKmsfTEekJfzJuCeQOr+Z+6Vg9sdIs89+D+pV6ceySbimbVPUkOqNGEEACAWOGsCLg2kNm7eIK8++bq0aNoi9ksEzZsJsajIjqx9HJ4BAACAmCeb6HZEd525D/HjrbJPxZCGQA4AkNRN+2KetS8VrxYAl9ZIHdfjeHnm1Sdl6YrF0q71QWWSTZzQs2+sli9lpZfUSNHiDQAA+CN0Atyate/Fh/Tfke+MCNgkbe5v1FbFrI+UHQPycnQGAAAAYh9Izft9cTQfQxQ1UjZ0kbIdgRwAINmYmt/F4NxO1j7AZX2kZs/7V36f/Jvpta9//FL6X3CC9D6juzz03P2Sl5cX62VMaanS7pngCQAAC+dNNhbgrkBq5DuvyrJVS33PlyxfLA8++3/SvcsxMujiwTrIGj3u9XgsZ8pJT0/hGim7FwAAAACIZSC1aNki6X7kMb7nP/z6vRx2SCd59O4n5MoLrpb7br1ffvztBytfiXBV9kQVAAAkWda+2A7IC8AFgdTu7F1Sr2593/MZc6ZL76OP9T0/9ODDZNOWTbFdQkn1PlKpEUmlShNGAABigbMm4LJAql6d+rJ+4zr9OC8/TxYuWSCdD+nsez8nN6dMKnSUM2ufDRvQ7tjNjkyFAAAAQNwCqT5HHyv/G/W8zJwzQ4a/8YJUrlxZjux0lO/9JSsWS7MmzSwtAEIHUilTI5UaqwkASFGeGJ/zaNoHuCyQumXQbZKRkSFX3HapfPbdJ/LIXY9LxQoVfe9/Mf4z6dm1VzyWM+WklRwiCTAAAEC8AzMA1llqh1endl1595UPZE/2HqlapaoOqoxefORl/TrKryRpn6+pmxroOFX6K3FCAAAAgNNF1aGpRvUaAV+vXbN2eZcHJYyBkwosEhhH2YKbaQCAlMnaR9M+IPWa9sXT6HGjpOOx7eWpV57wvXblbZfq14z/HnnhQdPnNmzeIDcMu1a69O+kBwV+fuQzUlBQIMlUI5Uq/aQAAEAUTfu4HQnYwhEp9uYu/E8+/eZjad/moDLvnXvq+XLz1bf5nlepXMX3uLCwUG4cNljq160v74/4SLK2bZV7n7xbMjMy5fbBQyQZ+kgpiQ6jiNsAAAAAh9dIqZTpwx6/Sx4Z+pjUqlGrzPuVK1eRBvUa+P5Vr1bd997kGZNk+epl8vT9z0uHdodI7+7Hyi2DbpcPvxqn07O7WorVSJHyHACQzIy1RrE4qyd5i3/AFWyvkXp8+CPS55jj5Jijesqo90aWef/7id/IdxO/lvp1G8hxPY6X6y+/yVcrNXv+bGnXur2ukfLq2a2XPPriQ7J85TLp0P6QgPPMy8szBVrZudn6b35+vv5nF++81d/CwtJDZH5evqR7ihK4HMamkZ6EbBNjrJhfYO9+cBNjmQEoM+A441yeotLzeGFBQbmP28abrHl5+VIhzTk3XTk3we1lJtLlsDWQGv/Ld3pQ349HfR7w/YF9T5UmjQ+UhvUa6jGqXhz1nKxas1JeenyEfj9r+1Y9SLCR97l6L5jR416X18a+Wub1CRMmSNWq9mcdnDhxouwrLN09P/z4k1Q0J0iMK+O8VfPJ8ePHx32eOTlqBYuDx99++13qV477LJOKKjMAZQYcZ5xr3fp0X0Ogf2bOlP0ryxf45OWVnjfV9UulBF4nRIpzE9xaZnJzc50dSG3cslGefuUJGf3C21KpUqWA05x/+oW+x6r/VP16DWTQHVfImvVrpPmBzaOe97WXXC9XnH+1qUaq77l9pH///lKzZk2xM/pVBahfv36S70mTYdN/1a/3H9BfqlZM3K7K3l/gm7dKcT9w4IC4z/OlpZNE9hYX2uOOO06a17U/oHUDY5mpUKGC3YsDF6DMgDJjj98+mysztm7Uj7t06SIndmhYru97eM5vklNQfNdcXb9Uq2R7IyMfjjNwe5nZvXt3RNPZ9qtbsHiebNuxTc679izfa6r24585M+TDL9+XfyfOKzNOVacOnfXfNetX60BKNfebu+g/0zTbdmTpv+q9YCpWrKj/+VM7zgk7Ty1Dmqe0+1pGplquxO2qCkXGltdpidkmhvzuFTKdsR/cxCllF+5BmQFlJrHS0w3n9YyMch+zjcOkZOpzgHMCKS+OM3BrmYl0GWz71XXvcox89fZ3ptf+7+l7pHXz1jLo4sFlgihl0bKF+q9KOqEc3vFweeP9kTogq1ennn5t8oy/dUKKNi3bipsZx41KdCIGuxM/kMYVAAAATmdbIFWtanWdKMKoapWqUqtWHf26ar73/c/fSp/ux+qBfhevWCzPvvqkHNW5qxzU5mA9fY+uvaRNi7ZyzxNDZcj1QyVre5a88uZwuejMSwLWOLk1kCpyTv/R+EmFdQQApCzzuE8AkoHz6oENVWpTZ06W9z57R/buy5XGDQ6QE/sMkOsvv9E3jaq1eu3pUTpL3yU3XqCz+Z1x0lmmcafcKt1UJWXfchgXAwAAOI/dLUmAVOWoQGrsS+/7Hh/Q8AB55+VxYT+jsvq9/uwYSTZpNjZ1M901s+HYzPkAAAAATmf7gLwIXyOVCk37UmAVAQAAkEQIpBzKzmQTwZYjUQiqAADJxnguj/VpnfMmYA8CKYdKs7FGyniAp5kdAAAAUBaBlIN5Y6lUSAdOR1kAAAC4CYGUC/pJ2VkrZEvTPqrBAABJxnwqj+2JndMmYA8CKQdLs+sASdM+AAAAICQCKRfUSBWlwK2m5F9DAAAAJBMCKSfz9ZFKLLv7ZBFUAQCSTawTOZla3nPiBGxBIOVg6SVHyaIUGEgqBSrdAACIGU6bgP0IpBwszXy/CQAAAIBDEEi5oUYqwdU1dtcO2T1/AABizXhqi8VpLs1BTfKBVEUg5YJBeVMhsOAkAACAlfMmALsRSDmYdwynVMjaBwAAALgJgZQbxpFK8HztD9vsXwIAAOI12Hyss/ZxvxWwB4GUg6WXdJIyHnyTVQqsIgAAMcNpE7AfgZQbaqQ4WgIAAACOQiDlYOklnaQSPYyU3TVgBI4AgOTO2ueJcdY+AHYgkHJBsolUyGhH8AQAgIXzJhsLsB2BlAvSnxcV2b0kAAAAAIwIpFyRtS/BA/ImdG7Omz8AAPE8ucU+ax9nTsAOBFIu6CPF8REAABgROgH2I5ByQx8pjpYAAACAoxBIuSJrX4Kb9hmbH9hwz4vAEQCQbIzn01icWcnaB9iPQMoFUqFCivbdAABYOG+ysQDbEUg5WHrJ3kl0jZTdUiHdOwAAANyNQMrB0koq7hMdR5maHxDTAABQ/nOrKWtfbE+unKsBexBIOVi6L9lE8kczyb+GAADETipcGwBORyDlggF5U+1QybkBAAAATkcg5YL050VFCW/bF+ghAACI9tQaxxMqfYsBexBIOVhaCgUz1EIBAGDhvMnGAmxHIOVgdo0jZbcUW10AAAC4EIGUC5r2Jfq2k2l2CZo3zRIAAMksrhlxuQEJ2IJAyhU1UnYvCQAAcBJabwD2I5BygVSrrUm19QUAAID7EEg5mF01UqZBAxMU1HBnDQCQzOJ5buX2I2APAikX9JFi0D0AAGDEtQFgPwIpF9RIpVptTaqtLwAAANyHQMoNNVIJrrSPa2ahoPMEACB5Gc9zsT63cgMSsAeBlIN5s58XFdm8IAAAwFG4AQnYj0DKwdK8TfvsXhAAAAAAJgRSLmjaV5TgOntzZqHEzxMAAFg4h3LLFbAFgZSDpWqyCQAAko3pJmUszutcGwC2I5ByQR+pVEtxmmKrCwAAABcikHJDjVSC52vOLJSwxn0Jmg8AAMmFG5CAPQiknMw3IK/dCwIAAMrHMLRIDDYllwaA/QikHCzdpmQTdqPTLAAAAJyOQMrB0sSmpn2GwI2sfQAAAEBZBFIOll6yd0g2AQBAMmXt87jypicAMwIpN9RIeVyeohUAAABIMgRSDmbXgLx2SP41BAAAQDIhkHKwtBQdkDfFVhcAkAJMQ4vE+vtS7UIBcAgCKQdL1ax9AAAAgNMRSDlYWgrV0HA3DQAAAG5CIOVg6b6mfakQSpVKtfUFAKTYuS0GpzkSQwH2I5ByQbIJO7P2FT8nsAEAAACMCKRckGyiKAXimBRYRQAAACQRAilX9JFKrTAjtdYWAJB6WftiMCAvZ0vAdgRSLugjlegaKf+DcyJa9tF6EAAAAG5CIOWCPlJEGQAAAICzEEg5mF01UnajdgoAkGxinWWPrH2A/QiknMyXtc9jb9a+hMwzxaJFAAAAuBqBlIOlao0UAAAA4HQEUq7I2pdqUm+NAQCplLUv1t/NeROwA4GUg6Xb1bTP/3kC5s8pAAAAzpuAmxBIuWBAXroPAQAAAM5CIOWC9OdFKRZJpdjqAgBSgLF1R6zPc5w3AXsQSDlYWkkvqUTHFf5N+RIyf4InAAA4bwIuQiDlgj5SKVcjZfcCAAAAAGEQSLmgaV+KxVEAACS1WGfZ4zIBsAeBlAvGkbI/a1/i5wkAAEKdNzlzAnYjkHJRjdTKrBzZtGufJDtq4AAAAOB0mXYvAMKnPy/yiGzPyZPjn/9dP1/19ClsNgAAXHqTMPZZ+6idAuxAjZSDpRmq71VtVKL4H48T0XyAkwAAAFbOm2wtwG4EUi7oI6VqpFIJQRUAAACcjkDKBX2kuO0EAIC7GVt3xPr+aIrdbwUcg0DKweyrkfIbkJesfQAAOArBE2A/AikX8O+jlOxN35J77QAAAJAMCKRcWCOV5HEUAABJx3TujvGJnOsCIMUDqdHjRknHY9vLU6884Xtt//798tj/HpYep3WTo046XG574GbJ2p5l+tyGzRvkhmHXSpf+naT3Gd3l+ZHPSEFBgSTjOFJeRXE+YtpxQOYkAACAlfMmd1UBuzkikJq78D/59JuPpX2bg0yvP/Pqk/L75N/kxUdekndeel+2Zm3RwZRXYWGh3DhssOTn58v7Iz6SJ+99Rr764Qt59a2XJBmk+wIpv6Z9ktw4NwAAAMDpbA+kcnJzZNjjd8kjQx+TWjVq+V7fk71HPh//mdx9073S/chjpONBh8rj9zwls+fNkjnzZ+tpJs+YJMtXL5On739eOrQ7RHp3P1ZuGXS7fPjVOMnLz5NkGZDXP3CKd42UHRIxVhUAAMmJcyhgh0yx2ePDH5E+xxwnxxzVU0a9N9L3+vwl86SgIF+O6dLD91rrFm3kgEZNZPb8f6Vzx8Nl9vzZ0q51e6lft75vmp7desmjLz4ky1cukw7tDwk4z7y8PFOglZ2brf+qmi31zy7eeXv/eoqK9N+CgkIpNDRXzM/Ll3RPURyXw9w0Mi8vXzIkfvPzV1hYYOt+cBP/MgNQZsBxxpmKSs7pSkFhYbmP2x6/87aTzgOcm+D2MhPpctgaSI3/5TtZuGSBfDzq8zLvZW3LkgoVKkjNGjVNr9erU8/XTypr+1apV6e+3/v1fe8FM3rc6/La2FfLvD5hwgSpWrWq2G3ixIn674rVqsIwXVasXCmTdy/37a4ffvxJKmbEb/4bc81F46ef4js/pbBQzaC4Bm7q1GmybSF316IpMwBlBvHCcaZ8tm0rPqcr8+fPl/Hb5pXvCz2l580///xTlth/+VIGZQZuLTO5ufpi2LmB1MYtG+XpV56Q0S+8LZUqVUrovK+95Hq54vyrTTVSfc/tI/3795eaNc2BW6KjX1WA+vXrp4PIBROWyi8bVkqLli2lx6GNZfi86Xq6/gP6S9WK8dt1Szdny9NzJvueDxgwQKrEOZK6e8bP6nadfnz00UdL99Z14zq/ZOFfZgDKDDjOONOHm2bI0t079OOOHTvKwKObl+v7bp86wVct1btPH2nXsLo4BecmuL3M7N6929mB1ILF82Tbjm1y3rVnmZJH/DNnhnz45fvyxnNv6o26e89uU62U+oy3KV/9ug1k7qL/TN+7bUeW771gKlasqP/5UzvOCTvPuxyZmcV3rtLS0iUjs3RXZWSq9+O364zzUjIrZMZ1fmXnn+GI/eAmTim7cA/KDCgzNqXiVfVS6eU/zxm7S1fIzHTkOYDjDNxaZiJdBtsCqe5djpGv3v7O9Nr/PX2PtG7eWgZdPFgaNzxAMjMryNRZU6T/sQP0+yvXrJCNmzfI4R2P0M8P73i4vPH+SB1cqSZ/yuQZf0v1atWlTcu24nZpJVX2/ln7kjHZhEmSrx4AAADcz7ZAqlrV6jpRhFHVKlWlVq06vtfPGXiuPDviKZ3NTwVHT770mA6iVKIJpUfXXtKmRVu554mhMuT6obrv1CtvDpeLzrwkYI2Ta9Of+72ejHFUEq4SAAAJwTkUSNGsfaEMu/k+SUtPk9sfvEXy8/OkZ9decv8dD/vez8jIkNeeHqWz9F1y4wVSpXIVOeOks+Tmq2+TZGoG4F8DFe9B+PxTkSdj4AYAQCIZz6UMpgskB0cFUmNfet/0XCWheOCOh/W/YJo0PlBef3aMJKPSAXklpQKbJF89AAAAJAHbB+RFBH2kDP9PRB+pMoFbXOeWyJkAAJB8kv0GK+BUBFKuqJHymA6SRUl+wOSEAABINsZTd3lP4zQNBJyBQMoFmVJVYGE+ACd5JAUAAAA4HIGUg6UZkk0UGaqh4l1jU7ZPVvwDN4JDAAA4hwJuQiDlxhqpJK+QIqgCACQdU9a+cn5Vkl8HAG5BIOVg6b4aKXOCiaQfkBcAAABwOAIpB0sz1tCYkk0keBypuM6tZB7EhgAAcA4FXIRAygU1UirIMGbqS/agI9nXDwCQeow3Kcudta/cSwMgFgikXNFHymOqhSLQAAAAAOxFIOWKrH2RpT+fumKbXPPOP7JuR26Ms/aV6+sim2f8ZwEAQFLiBitgj0yb5gtLfaT8k00Env7CN6bqv7v35csn1x3j2m1MUAUASOZgp7zDijAgL+AM1Eg5WHpJJKWDKAvJJjbs3BvnJQMAAABSG4GUC5r2qSDK1j5SiWjaR7sEAACiO4fSlgOwBYGUS2qkzFn7krvxW7KvHwAg9cTyzMZZEnAGAiknM6Q/NwYXyXgATcZ1AgAAQPIikHJhjVTcB+T1z9pHmAMAgGPRkAOwB4GUg6WV5O0rjmtSZxypJF89AECSm75yu9z/1Vz5bdEW32umliXlPNEl+3UA4BYEUi6okSoekDdxNVJ2SMJVAgCkqDs+ni3vT10jV42dYfeiAIgjAikH8yXtS3DWPv+mfAQ5AABEbuMuhiEBUgGBlAvSn6sgyjyQnyS3ZF8/AEDK8cSw7zF9lwFnIJBysJIKKX24NdZIJWPTPgAAAMBNCKQcLN1XI2V+Pd5hVNmsfQAAAACMCKRc0UfKk1I1UjRZAAAkm1g20S9zwzO5LwsAxyKQckGNlE42USQBU6gmg2RbHwAAACQ/Aik31Ej51dHEP2ufvYEOcRUAAACcjkDKDVn7ivyTTdi4UAAAoJxZ+2KLJvGAPQikXJG1z2M66iZbU7gkWx0AAJL2nA2gFIGUS7L2JbJGyv+gn+hTAOccAECyoBUJkLwIpFzQR8qvQoq7WwAAuITv5qThLmGsbxhyAxKwB4GUg6WXBFKqNspYI5VsjQSSbX0AAIhnjRSBE+AMBFKOVpL+3O9AHO9xpMpm7Yvr7MLO3wn2FxTKRW9MleE/Lynz3sjfl8t5r0+WvXmF+vlvi7fIKS//JQs37rZhSQEATpLsYz8CqYxAyiU1UvFsEoDwvv9vo0xZsU2G/7y0zHvP/LhIZqzaIR9OX6OfX/X2DJm/YbcMfu8fNi0AIEDWvtieyLksAOxBIOWC9Od6QN4E1kglmhsyGu0vKLI8za7c/DguEYBEWbE1WyYtzbL8uY279sovCzfrY9ziTXvkn1Xbfe9t2bNPfl6wWYrinIlgR06e/DB3o65VDyU3r0AvoxUFhUUyfu5G2bJ7X5n3Zq/dKTNXb5elm/fIH0u2yrodufLjvE2uON7HWjzO2aQ7B5wh0+4FQPgaKXXiMZ584j4gr8feA3YqnmgBONcJL/yh/35/ay/p2KRWxJ875qlf9d+RlxwpN4ybpR9Pu6+vNKpZWU584Q/Zva9Anjr7MLmoW/M4LbnIJWOmyYKNu+X6Y9vIPScfHHS6U1+eJCuycuSdq7vJse0bRPTdYyevkse/Xyh1qlaQfx/s73s9r6BIzhzxd8DPvHrxEXJqpyaSSsja537vTV0tbepXkx5t6wd8f9fefFmwYbd0blZLqlTI8N0It4u6jsrNK5Rqley5zH/up0WyN69IHjztkIimVy179ucVSGdxH2qkHMz7O/TvI8WdKABIvIUbrdXYeE1evs33eN2OvfqvCqKUXxZukXhSQZTy7ZwNIadTQVQk0xn9vHCz/rvDr/Y9rzB4Df60FaW1cqkiETcHuQEZP9NWbJMHvponF4+ZFnSaM16dJBeNniqHPPiTDPl0jtjtrk//k44P/STz1u9K+Lz3FxTKiN+Wy1t/r5RNu8rWVvvL2V+g+5q/NXm17M4T1yGQcjDvHQ3/rH1F4VuZuUqq1T/NXL1D3puyKuyJb8gnc+SyN6fFvekPACB+vIdw4yG/vLEVDTcSZ8323LDTrNpWOs0Xs9aL3T6ftU7/HfnH8oTP22Mo2/khbqp4FRo+UOjCyx2a9jlYWtCmdvGW8BnaObuEO2fkZP23Se0q0rdDo7AHwkWb9sghTWombPkAALFDbZG72d1Mz208yX4R54caKQdL99VImTurJluyiVS1YmtxU5pwJ14372+VCv7UV/6SRZtilwpepZU/afifcvMHs+Tr2eHv/L3990r5c8lWsYNq4vDjvI2yM7e0vYJKk686/+/ZZy0ZiSoTqg1+uKQBcB43NceOxeGGwCH22zTsNo//LICkO97FAoGUG/pI6WQTyXuSSrLViYlk2SYqFfy89bvluvdmxuw71XepWrrv/tsot300W2cbC2b6yu3yyLcL5PK3posd/jdxqVz//iy5eHRp2/oHv56nEw/cWJJ8QHXKVc04w/2uP/1nnQx8+S+5euwM/Xz3vnxZVdKvBc6WLL/nSNEa2X97eGJ+gZliRQpJ/Pv3uLwwE0i5oEbKP/154rP2JZbbf1Sx4OZaqGAZjWJFBRBG+QXBt9WGncUd++3irTHzdvhXPp1Z3GTzr5J02qpTrmrGGS6RwbtTV+m/fy8rTlxw9BO/yHHP/y7LtmSLmxQWmbOQukm0y+2mwCIWrZjcun9jyWPT/t+8e5/sy6fWGvbxWP39u/xwQSDlhj5S4pdswuWFLtWrgSORbPsY4UXSKddob8nF0uTl1sc3sssbfy6Xdv83Xi54Y2pKXWyn0roqKba6AZlakSSiZ7NHZGVWjhz95C/StyRdP+CG65cilx8wCKRcEEn5F8p4H5T9v93lZdzR1ICWn81cJ2sMGX+S4cACBPLT/M36eKaaXO7ZH7xJZrJ1Ok+1n3OqH7/8A2fv01g20Q/0eTXAs7Le5pp4pDaPxbLt9uMFgZQrmvZ5TCmwk7+2IulX0OeD6Wvkrk/nSJ/nfjO9Hq/jyi7DeC+qXKnajKzs/eJmbq7RTLWaCiNPkg3jkMwXClYl/znK2vqn2v5HaiuyXCNV+tiNvxQCKZekP/ck8cVXkq2OJVMMA3XG+8T77pRV0vnRCfLmpJX6+YQFm3UShGOfNQdxbuPm8pPKF5ypdHGZavs52c5RsaqRivNcEzETwNKx3eOx9ntx46GDQMrB0tNLaqSiKJixTTbhwpLtEsFaCsXjIvPBr+frv499t0D//W3RFv03J8/dHZPdfEHu5mUvr1Ra91QLLFItcIy0RiqmA/KW7+Pw7QePzF67U7INTY3X7ciV7TmlQ1bAmiKLw7dQI4W4STMURGNZTPYLELeuXiwDzqIUavaUyhdtphskkvzM46NJynDTMTsWi+qm9Y0H//VP8c3haN/+t1HOHPG3nP3a3/q5CqB6PfObHPnYRLsXzbU8Fq9XE1lREA/USLmgY7Nu2ufygpZMEnF3OdkuROK5Om6+2x+bwU/FNczDOLhowcup0LSqyb/eyXb8csWAvLoLgMfyUBIqXXqgvrRq6AY1eHgwfy/LkvkbdpV5XfW7ffy7BfLv2p3y+cp02WEYjNwO701ZJRe9MdVU42T05azi4SiWbC4eRmLJ5tDDUCC8Ios3zExdV1y4gQmk3DAgr05/nriTlD3tuw3zE+cxtsCL9E56tBm+iufhxK3gTG6u2VBjKqUSN9ZImW9iRTuOlCfFxpGSlOa/v0sH5C3lccA27vTwBJ0u3T/h0DXvztCDnavBwwNZuz1XLhkzTU55eVKZ91S/2zGTVsr5b0yXPzelyyPfLRI7PfD1fJmyYpu8VdI3GPHnsXjjyJhMzY2HDgIpF2TtU828jHea3FjQkkksL4qCfVUiB2B2mzQXX6RKEi17NBJ5QyhWYrKY7ljVmHHJrk26rH1pZY6OkZm73lyzNGPVDv33y3+LBxT3t2a7ebiOUBZvckYNT05eZMMthNtVqVSTnrAaKU/gx25BIOVgwWpBku2H7IbV8URxUizPfjLfuXfBBrKRm7eP8XedZmMNglM7ITuBJxbjSKVYJOWWfZuwVh2JmGcC5gF3XK+4uo+UuA+BlBtqpMokm4jvfP2/PtEF2+kHqkQsnxvv3CdKmfIZ4eZx4g0IK8sU7d1mJ3FjTWssfn/G5DFuadJYHql+zPLfx97feUz7Oqf2Jo4b472SQMfnVC/blm+YFVmb3o2bl0DKDX2kSDbhKMFGp4/mQjeS9OepcOFVHpHXEIrjpNq+dWNNa0wCKdOFhTvWuzxSYBVD8r8AT/Xt4VaB9hv70tp2KyL9OezkvchWBdGNTWKStdlLIoIc8x0ad22fRIt0Hzjxd2OlSYPbfifJcoMgNunAAz9OVql+zCozFmMCNkeKb/K4CHTOcOJ5JKkG5BX3oUbKwbw1HB7/WpCEnwQSW7SdfsEYy6A22McT2ZTT7SLdB07cjsl8gyRclkK3rG8sljMWmf/cGjimomBZ+2J5nnPKedLp5bk83HKMcnvLgyIXNvk2IpBysPT00kKZzMkm3KY8/Zci3XfGC85US5FtVaTb1IknxWDNRJOVG9c3Fj8/N9bElYcTf2upkLXPilj9/pK5PAfaRE7cl26/RvK4PCs1gZQbaqT8BtpLtjb2bjsulafDsClLW1rqNO2LZ2a5SH8OTtyM0V5gu7VMGNe3MIJOyE4Qi99iqAsLN2Vd9BesX6hLi2fM+NcWJaZpn5pr5DOK1WVEMgcWgW5iJtnlV1x4LLagMiakcGNxIpBysHRDH6mENu2z4SRg5/wSWSMVTTM0Dtyx2qbOK1im/WxhRztwVZI2G6WnKL5NOJ22GawEdsEu3N2ybxPdR8pcIxvbeVhVnn1k/Ggyt5igj1Ribj4V0UcKcc/al4JNQ5zG3Gcp+n0R6d1oN2Y3s0sk6VWduh2NwZOVsuTEdUnWPmGxXk53rHX5uGTX2tpHym6xWiYHrlrMBDomx+LGSrIrsniNlMiKgnigRsrBvIM/qostN/YtiJTHxReAlmuk/Kqwg33cjXfu7eLmZBPR/q6duC6RiOUd+USJxYC8ydZUN5xUP2aVqZFKxDzLLIMnJjegAjH+DJJ5XzOOVLTbTSy1tHB7RQGBlIN5j1X+NVJxP27ZXJA9LuoXZfXuVDTN0JL4PBUTkW4fJ17ARnsCcevFi9trpKLuI2UckDcF7mi7Zd8mukYqlp3qPWH6rIU7nsRqHyXzvg48jlTyrq9d57UimvYhXtLTjMkmAhc6JEawGqL49ZGK/wVnshQjN9dIRbuf3Rpou/HOYyy2tZsCyFgsnlv2beL6SCV+g4QrZ7ELpCTpm/Gbm9rbsVTuUmSxpYV5evdl36FGyg19pHSyidjdyXIaJ9YURHoBaL6TYu2AEfnBPNKlTE2hLgiM+8SJnaKjbcLpwFWJiBubrAbrHxn9d0jSc8Mx3Y5kE4mcZ/hAKjbzTb4swmVvepj78CTX+jrjhplH3IxAygU1UqogxuJkHn1b67jOzhUn4WDZ1awOJOekcaTcnHbZKNTmMfdJ8yRNKn23BCFuHpg2linqU6+PlKS0sk37iv+aXi5nOQhXjsJ9fXkCoERej9h7rnd3CwAlzWUD8haJ+xBIuYC6A+LmH3I4blidoH2kLF4gRXruSrULr/IItX2c3pTMuExWAma3Zo5yY1r/WNQahiqHyTmOlEt2bhJn7Ytn0z7TuJZJtqsDNcN1Y026nTymGrzwjEG9GzcvgZSDpZcMJKXKWLg2p6l+4rIva59xGmvfE6qJgBsvOO0Savs4/c5pLPpIuYlpfV1SsM0n+ej2UagLseQcR8r6Z5KJ//on4vdaPBxv5AGO6X1PfPvAuD+Qcm/gaMfiFlk875rKk7gPgZSD+c5nZZr2lZ02lj/uMu27XVm0E59sIrIDRmR3XhIxjlSynP9CbR+nd/JPtax9xlo3t1yQRNu/KdhdWSeudqwvhpPt4tq6MifRMq+Wdwt5ynmMKM85xnhzoTDJ9rW5xYn3r/tuANmpyGKLmmibuDsFgZQr+kj5JZsIGEi5sPSVcMOiR3J32erAcyH79ji8JsU9gVTpYyduxmDNRJP1jnC062snU8rqKGuknN43LNbXhqG+L1hzwOSukUr8MoRr/msMgKz2w3VzDU15m/ZF+vN14u/cnpYHEsH0pY/duNUIpNyQtc+vcAW6cEym36wT1yV4v6jArwcbvDPSGpJE9Ilzc98Mo1DbJ1lrpNyajteNF2DRNrMN1qzU6eXQid/nNrb0kfKbRbiaovI0Hw+WfMmN/M/TgbZLNLV3Lt8s5eKxeCPY7TkACKQczPv7VoUs3IBlsTxQ+zflc2PBjrVgF67Wm/aZvzPYR5x+4eWa9OcOv3CPdSIDp3NjuY5FPzanD8gb6wsZt+zbeCnbPN77euy2c6DPW7mAjbbvn/93O3lXW21WFixjbjTHWzt/A3bXhhVZbakT5LFbEEg5mLcJhCeiZBPiXi5Y9kia80WUbCLCPiLR9stw64G3PJzcRDJcpV/qDcgrrutbEYsU9f6PnfZ7i/XiOGz1UiZrX7iWK6Zpy1Mj5ZI+UtEsWqBmuOFuZDstkLL7pmFRmOvVstNb375OQiDlYCVJ+yK6cEqmhBBOXJdIEkxEcuCM9I6hqVN+nI6KiQ7W4iXyJpLOW8lo0766sWbHDX2FElFrqB4av8YJTWxjvSvcVCYTUiOVgO3hsXiDpbA8ySZcUiMeTZAXuGmf9X1p52YxLqMdh5ciqzVSLr0x6EUg5ZK2u+aaDE+Cs/YhWGdTqwcA/wNMsIuoRF8oO/lkGE6oQNPpWeKsXJAYO+m7NQgONNil08WiH5v/DRSn/d6iXZ7g40hJSisbSJX8Nb5WzjNroM9batpXrkDKHTdEoinXsUp/nso1UuKxVs6N5wK7Fz0aBFIuqZEKd0HotBOz22ug/AXrbGq1022oE1DwCy+JC2MQ5+bykzzjSEX7OeetVzItt6l/UwxqpPzHBXQC4/LFoobMLfs2cU37EjRfCzeOypX+PMhxy2lBVTSLEy5rX+BkX/G9ue2231+RxfOaW5uqexFIOZjxbp+5DWmAH7JL7u5Gwok/pFilP/f/bLCAKdEXnE7c5rFu2ufEGpBo93Mimn7Gmn9yFZcsdtR334NdHJRJHuSA7RDtvgiWGp4Bef0DqcQcw03N9cLs1PLUDge7QeW033Qk290/a5+5xUnZ7wm0rQLNxt5kE4bHrugjJaXTi/sQSDlYWnrgC6dA5TKWtTr+3+S0u0x2CBrkWLxbHepiw867fHbfwSqPUNvH6W35o2l7X97UxanUbyQWog3+jJ8zXuCqR05b9Vjsi0iPWU5b93goew5NwEzK7IPQHy/PzbpAme2i+Z54i2R8rFBZ+wJ3o4jsNTs3hd37ochi2XJ6dt1wCKQczHifpDDMScqNhc/LYcdeS3elI22qVzp94O/0f56Iu3ymC72iZM3a56w7//Hof2P3SdPpzZ3Ky1zrYuFzhokLDAdw9brT9lks9kWkZdkt+708gp4LLAQ65Z1vuDJWngAoVG2rk0RT1ozr4Et/HuZGdqD52HmjyO794LHYEoQaKcRNetBkE4EKorMOYOXhxFUJdlfa6t3qUBf2wWonEpNsQlwr1PZxesARi4xwbqnZCVUD62Sx2Ef+TTGdtu6xuOEQ6V1ot5TX2Jb1xAT8VmoCytPMNljw5LRdG01ZC7RdwvaREqf1kbJv3tEcM93QTz6UTDtn/tFXH8jHX38o6zet08/btmwnN1xxk/Tufqx+fuVtl8qM2dNNnzn/9AvloSGP+p5v2LxBHnvxIZn+7zSpWqWqnHHSWXL7tUMkM9PWVYsJY9PdcHePArXrjdndtPJ9XVIIdkfKdLc6kuw0fm3YgyV8SETTLeO83Xxx4+RxpDwJSTYhrhCqBjbZ+0gV+N0Ic9o+i7aJabDviLTfYrIqc5MsDmfRQN9o5bwRq6x9wW4YJEONVKBxpCJtxmdvHyl790ORxeDaSpNUJ7I12mjUoLHccd0QadG0pd7xX//4pdz8fzfK52O+krat2ulpzj31fLn56tt8n6lSuYrvcWFhodw4bLDUr1tf3h/xkWRt2yr3Pnm3ZGZkyu2Dh0gy1UiZ2tiHqUZ26olq+srtUq96RWnToLrpdWcurVkkCSYiGy/BPH2wu4KJTibgsPNfDPtIOTvgiHZcJSf3SwjGfzldsthRt98P3lRX/fCdNo5UdOUpWEr+UJz4O0xUM1bjq/HYDFaCo/LcrDNejzj5WGRankgu6HVtsfHzAY7T4eYT4rVEcV2NlMdQhsR9bA2kju95gun5bdfeKR99/aHMWTDbF0hVrlxFGtRrEPDzk2dMkuWrl8mYF8fqYEraidwy6HZ5cdRzcuNVt0jFChUlOWtEQlcjO+xYpq3KypHzR00pfvz0KSGndeDih74oinIEb/8DTLATUrwOyE5u225F6LvfzrlLF4gpWYmFs5/TA8RAUq1pn3HSgsIi0+tOW/dY1IBHesxy2ron5qZB/NdZx+cWymp5btYFO6467VgUbvzNMuN1+vVf9D4Odx4J3EdKbGP3uc4TJvD05+TmoZFwTPs3Vbv00+8/yN59udK54xG+17+f+I18N/FrqV+3gRzX43i5/vKbfLVSs+fPlnat2xcHUSV6duslj774kCxfuUw6tD8k4Lzy8vIkLz/P9zw7N1v/zc/P1//s4p23929hQenJN99wIi4sKiqznHmG56pQlmc9CgoK/JaroNzbZcH6nYbvM3+X//PCwvLPLx7lM9D2UI+NF0vh1i3PML36ziJD3vq8vHzJL/lF5hcY5ldQGHR7+JeZSOmyblin/Wre+elxPbDGbJ/6HWjz84NvnwLDdlTbPtHlynhCCTTvfMNvTe2PUMtn/K59eaXTFYT5XKzKTHmp8m1ajgLn/c4DMR5brWzr/YbpjBet+YUFpveKAhzP4yP4b9C4joUhjmP+r3sMxy91DKmY7vH9JoMJ9P3Jpsw5tOQYbgo6yrkdjPtMz7OwwHy8ywv9+zK+F+q3GOh14/oZj6v+v3Fvs0a79rfxdxbo/Oz9/ZnOg8Zjcsm6qd99qPNIoPUuPqdmiB1M14M2/N7yTdcv4Y/zxuOF+oU45fgQ6XLYHkgtWb5YLr7pAsnL26/7OL38+Ahp27Ktfm9g31OlSeMDpWG9hrJkxWJd07RqzUp56fER+v2s7VulXp3SIErxPlfvBTN63Ovy2thXy7w+YcIEqVq1qtht4sSJ+m9x7FS8i7bvUIFI8Z2TlatWy/jxK02f2b6/dNq9+/bJ+PHjo57/gh1qPqUHgD///EMWl7aojMp/20u/8/vvx5uas2Tnm4vif3P+k8ob54iTrFylgoziQGPqtOmyc3HxCXH57tJlX7NmrXz//Wrfc/Uj9N8PK/eUTr9i1WrZo9e9+HsnTJwo1SsUTzd7a+n2WrR4kYzPXhhRmQmtdBur5Vq7rnSdfv75F6ldSeKgZFvkld0W0crTJ6fSAjR7zhyptHF2wGmXrildxylTpsjWBZJQ+/aVLmvp+pfuhxn/zPTt5/nzF8j4HfODfteuXaXfNenvyb7vWb58hYwfv8zyskVWZmInt8C87rMd+DsPZOmu0t/iMgvbesve0vUtvoFSvO8WLlwkE7MW+t7bvHlzzH4bgRXPJzd3b9D5GM8fGzZukPHji/sthyszWdtKf18/qfNnye6dvcV8DjFat369jB+/VpLZ4p3m9Z89e7ZUWP+vZOeU/oaXLV8u48cvjXoeOw37TJkxfYYs1/Mt3h9/TfpLVlYL/nnjuWv+Av9jT/Hr6kZfoDIzb2Pp+hmPq/7nciUnOyfO5Tu4HYZttHzFShk/fnmZabZsKS3DP/z4o8zbbFi3qdNk+yKPLF1dOs3kKVNky/zQxzbll19/lbpxOaeGtyvP+HveKOPHr0/o/Gcbrl/mzp0ntbbODT294XjhseHcFExubq47AqmWzVvJ52O+luycPTLhjx/lvieHydiXx+lgSiWW8Grf5iCpX6+BDLrjClmzfo00P7B51PO89pLr5YrzrzbVSPU9t4/0799fatasKXZRF96qAPXr108qVKig72LeOa24QNVQy5Wjr8KlWfPmMnCgubZt3Y698sisv/TjChUrysCBx0e9HFWXbJVRi/71Pe/du4+0bWju12RVhQVb5M3FxRe7ffsPkMoVSk8y23Ly5P/++d33vONhh0mbA2tJu4bVJT09zVcjtzIrR7/mP4BeIkz9ZoHI5uKLi65du0rvdsUB+/RV20Xm/6MfN2naVE46uaPI1OJ9pvbhwIEDTN8zc/UOkXkzfPtxW3aeyPYt+nnfvn2lXvXiI+++f9eLLCs+Wrdrd5AMPK51RGUmlNumTPA9HjhwoPz62VyRrRv18+NPOEEOqFU5qm0TyTwrVCy7LaL10OzfJLeg9E7RoYd1koFdDgw47YIJS0XWF9906HZ0d+neuq4k0lPz/xDJ2+/b5v774YgjjxRZUhxMHHRwBxnYq2XQ73pj9RTfMUCti7fctWzVSgaefFDEy2SlzMTSjtw8kRmlv/PDDjtMBnZpKk43ZcU2kQUq4BVppbb1SZFta3W8emL238VP0tN9Y1gcdNDBcsIRTeSBmX/o5/UbNJSBA4+My7KrGpDbphQfj6pUqSIDB/YJON3aHbkisybpx40aHyADB3aOqMx8sGmGLNu9Qz8+8cR+Urtq8Xs5M9eLLA98U+CAA5rIwIGdJJnVWJYlsnCW7/lhnTrJwCMOlBcXT5KsfcUXZ23atJaB/dtHPY9Nu/fJQ7P+9D3v2q2r5CzOEtm4Rj/v0aOXHHpg8OuZaStLz10Hq2NPz9Jjj/cYlZ6WHvC4vXnyapFVi4vne/TRckzrevpxVvZ+kX+Ky7VXterVZODAXmKH9Tv3ipRcF7Vo0UIGDuxQZpovts0S2ZmlH/fvP0B2zFznW7ejunWV3m3ry7yflsgvG1bp144+ursc3apuyGObctxxx0vTOuW8A12OsiEzi8tGw0aNZeDAwxM6/32G65dDOnaUgUeHvl43Hi9UpW2iz03B7N6t7zY4P5BS/ZhaNG2hH3c86FCZt2iuvP/ZO/LwXY+VmbZTh+KD+5r1q3UgpZr7zV30n2mabTuKfxDqvaDzrFhR//OndpwTdp53OTKCpDxPS0svs5yZmcYqyLRyrYdK1mFensxyb5f0jNLAqcBjXv4Kmebuhc/8tFR27c2Xu/q3l5tPKO4rN+yL2fLFv+vl8TMPlUu7F5cXu0ZHTsvI8C1/enqmqeN1hl+2SP/tlm7YtjogNASF6Zml2zktvXR7paWX3d/lLbt6WuO8DesUH+Urk6Zv8ouj00Ntn5JAPDHrWJYx6FeZRP1vAqRb2M+mz6YZ7vanRbdtE328y8jw60aclvj9EY0M0282/G8x0G/dmLVP7Wfjd0a7/6z2EUkLMR/T8oT4rfqXGWOZVMe+0uNiiGbCcVxfpzBvz+LfuVpn40/Y+1q0MjMLys7TwjHdeOxR57dQ+7zsZ0v3rzoHeqfJyCjbpFOdF+3a3+np+WHXMd2vDKvfeJn9Zlrfstu1zLGt5Hhv13pnZBTY+ntLM57XIjhmZmSUbl+Pw67FXTkgrzrwG/svGS1aVty8yZt84vCOh8vSFUtk245tvmkmz/hbqlerLm1Kmge6WbDU2IE7Ozo3c45SYGiHvM/QflbxX1oVRCnPT1jie00FUcorv0bfFCIe2dX8s12Fz5Rk7ODr/12B5xev/en0BCWRCtXB2UkJNcKlyLWyfMbfk1v2nXuTTUTXmT7Yb9t/HKl4dtCPdBvHIqFGpOvkkt0elwyVnjinRE9U1r5EDx4fLavHV5WN0HwejnQcz9DzdsuQDbHisVgm3H4tYmuN1P/eeF56H32sHNDwAMnJzZHvf/lWZsyeJm8895Zuvvf9z99Kn+7HSu2atWXxisXy7KtPylGdu8pBbQ7Wn+/RtZe0adFW7nliqAy5fqhkbc+SV94cLhedeUnAGie3Md7tM3ZWDnxBZnhczqOZ/wE+FgV7X37phd/evOAdkZ3KPwAqfd38ONy28j+wB015HoMsWrHIaOQGITOEhTkBJno50w19u7yveVnZBU5OORzx+HQpNCCv/+uJutCJtMxHm3nNOClZ+wJvT/9tE0+JG0cq8u+xM71/JOtovM5SuVPCZe1zw4C85hsczj9mFlnM8uc0tgZS23ds1+M+bd22RWpUq6H7QakgqkfXnrJxy0aZOnOyvPfZOzqTX+MGB8iJfQbI9Zff6Pt8RkaGvPb0KJ2l75IbL9DZ/NSAvMZxp9xO/caL0+VKmAHhnF0Q9xqyshiDKsUN11ORnDgiqZHyP8DFKq16NIzziMdAiom6UA49jpTxsb0FLVyKXCs3QMwDvHpcWiMlKTMgr//rge56O6VGytLv1lzFEvDlaJfJzfxXMRFrrPablXHpynOOCXZjN9B5xM7dbb7BHMn0gceRCnehH661QaLZ3UKpyOINQtPxR9zH1kDqsWFPBn1P1VK98/K4sN+hsvq9/uwYSVaq/a6qbg5WWxHoNSeep/YbAym/pn1uEOwOj/8BwzhdoKQY/ievYPvNeFEdr9Hi490kI1EXyqHmY65JtDuQ8sRsHxSUJC5QDCMjuGyQUgceqOI0IK//9yWqyWmkXx1tU+Jg5TfSmxvJKlgzVtNmKed2CHjxHqS1RPgbaVbnHfj85LSfdCQBRajm+aX7LfT3hLu5nWiJulET0fzFE3UzaLdwXB8pmKUFOlgFKJix7FMTj7tpxuZ8+1zYtC94Hymx2EfKb/qgg/AG/kwsxXuw2sQ1Zwk+HycNFhm2SW7UfaQ8Lg2kxBXMF6eRL3SwSUPVRDujj1R03x/pTQu3lFc3lnUrrQyiLdfF0wf+rNNujkRyo8q/7JvOyUXRD8hr5/HN7n3isXjeNW5zZ5UgcUfWPoRWnFHGfIGuHqrAZM++fHO6S8P7oahU4lv37JcmtROXmtNYC1U22YTzfzrBgxz/u1nWvieS743XhUe8gww7+gWEes/uk3y4O5nh9rPxbWONlN3rFSn/pjVuuaCOttYwVNM+5yWbiO5CJvixMLLPJKsymz0BZd1j8eZYec4xkZy3nHbMCbaOoVqJBGraF6iJYKAbB/Y27St97HFFHynxcVgRigiBlEuqpIx9IlZvy5Euj0+U3CA1O+EK7tVjZ8hfS7Pkixt7yJHN60gi7M0rPfpcPfYfmfF/J0qDGpGNVhevpm0S4wuG4mQT4S6G/QOvSGqk4rP+8Z5Hog6IoZsRxT8gjVS45h/hg/DSx+Ga+jqRW5v2RVtzG2xSqxe85RHpN0f7OwlVOx/JZ1In2UTJ64Y9Ut6tEOjz0R5Pgk0bLFFEsPLrtN+0qYYuaNM+MU0TsGlfkO8M9B2+6Wxscu1xXR8pT+ljcR+a9jmcdxgc4w9j1pqdviAqIz3NOFROybShv1MFUcq4qcUD94Vt2heD36F/LdTzPxUPeBeJ7P2lYyLYdZyONClE2JOX6Q6Z38nM1NY8/hfKxovxeASrTmjal4gmkpEKNH9j34TwiUo8Lk82Efji0uli0X/I//VENTlVWcgimi7Kmyqh+otG8plU6yMV//lGXlatBhmmzxrPHQkqy9Ewl+vA0/jfwAz0ew/3e42031Si2F1LWBTkuiZZEUg5nBrMLtiF7kXdmsnyJwfKksdPjqqpXCLTku4zJJtQVm/PKX0SYnHVAcwYSOXZ1LPeeCwyPzYfYMPd2fU/0QU7QCeijbPVu0bWvz/232l1PnbfmQu2LF6FhsjaWudwxpFKFONusVKEgu3PMjdQ4lguo2naZ+VOejS16Hb/DhMhHkOIRDBTMbT4tRRIWe8jZX2/28Ec5AW/sWEe4630Pe9b4ZoIOm29E9V0OHbjSHkMnxXXIZByOG9tU6BAqmmdqr5aKSMn3gDwD6QiHUtKpU3P3lcaSKnHdjQNibQJXrgDgv/0wcYDSkSb4WjvQrurRso5d0sDzd9YsxS+T0PgzzntJB75OFLiCtGWoaB9MorUZXZiLhwiLRvRHguC3VQK9Q1u2e/xztpX3vNYuAv6cF9fnnNM0KZ9DmuXFUmTVf/tECj4CncMCPTN9tZIRX5eiff8iyKYv2kfiPsQSDmcN4V2oB9vs7pVA6bZDlVw9xua2AWbzP/lWCSD8B87Klj/Ln8qcMren2+6gNxfUOTQZBPhL0jK1mAF+67E1kjFpY9UgnZTpM2I7O6bEWgbWxlY11wj5ZwAMeqLS5cseLRlKFSWsEg6wSdy2aO9I0yNlLNuGlipgTcHQIEvvIO1Wgk2H6cNyBssTbuRf+bcQMfZcLVukWbyc1KTRicdM4scdMMzGiSbcDjvQcg4oK1XszqBs+6FKre79ubbcsfEvwZq6ZZseXfKKp2VMMfQdM/fpzPXyY6cPNNr6nN9OzSSWlUqSP3qkSWsSESyieI+UqEPCP7TB+2sHef+S4k4eCWsRirEwiciIA0l3PzNNUuhv8vj8hop+kiVbodEXThE2sSmKNY1UiHn5Y7yWh7+q5iIzLRqHtGOIxUskU2wXWXuXxv4OyNdjniKpI9s2eb2pe95H4erbY2031SixLLms9zzF6vT2xh5R4lAyuHqVqsoe/YVlLmYrpCRJq3qVwv6OfXjCTQg7G5DIGVMnx5vgQbhffDr+WE/91yApBRPjl+k/yl39W8vN5/QTuwbR8r/blbkHVBD10gZX5eY00FfOcYRiUS8TiQeS32kxNZmJ+Gaz8SiRsrumrboAyn3LbeV36ITBuSNNECK9YC8oW9uSNILdtMg0EV6tAJ93sqNo2ABUGR9WiL/rJ3Hp0iOk2Wa5wc4Jlvp+xxufolgdw1PkdWmfREE705GIOVwoy7rIpNKsuw1rFlZ8guKZHtOnhx6YC2pXbVi0M+pcpmRFrpGyvjYSrMEVegXbNyta8kC3fnwvmR8z79W6bwuTSUnr0Bf3OqUo0UeqVe9ol4mdfBTAeS27Dzfd6l+YLWrVJBtOXl6DKyFG3fru/J/LslKSCAVLLDx76Qd7oDgf4CJpGlMfAbL9V/2+MwjHutQpjxG2GTJjgv3cBeoxvGgwi2e8fOmcaQc1i8hFvsttQbklbiJdD7R3riJpO+oP7cE/u7M2hfdOFLW+7QEv5kYapkSLZIyGbq5fdnPBlzHQGNLOWS97amR8hiWRZK+jxSBlMMd3Lim/hfNDykjQBVpJIFUOG/9vVIe/36hlMdz53Uu1+dnrt4h54ycLOt37pVECHrS8bvICnen2b/tcrAmFbHOhFR2XJP4Ny+KtKmPVf7bI3TfD4nLMkQq3AWqMfteuCacxrJmzvbnjlNPMtRIWVnkYNPqJlgJutAxfnPEfaSsfH/QmonIfpPJquwxKv7zVPMw9wkKN33g/RXJsga7UHZaRrtIan5DJYzyJQkJGzQFCq7sW+94t2ixNn9P2OljOb6aHQikklSwshuLQGrigs36b6OalaRaxZIiZIjZvA+9TQtLn4vUqVpRbu1b/hokb/+wjbv2Sn5hkVTISLe9Rko9DHcx4X8BFayTb6zHmfG/SPdvhhivWi/j/OJXsyGOrZEKV+tnpa9T8HGkxKV36cUVom2GF+rCLV6/jTLzirB8RRvYBbsAD/UdqVAj5X81GGid47EVrBzvgp3Tgo0pFUla8UDBm70BhXHZgv0ezTdGA52Hw23XwK1PxDZ2D/tRZDEwt/uGZ3kRSCWpYD+eXbmlwVNWdp4MGjtDJ2xoUb+qHrNKBTtLN2ebPvP5zHXSqGZl/Vi9P23ldv143DXdpW3D6mIHtcwVM9Mlr6BINu3a58tgmPg+UtZqeULf/TK/F+hxtPyXxf/uZbyTTcTy+63UbCRq4NNgwjX1tJJ9z/i+lbTpTuHWGqloU5WH7iOVmHLpXwMefELjdFYCqfI1I0y1PlLxn2/wZQh57LGYvCZYfzjnNe0rfRxstfz7qQYaYDhc6wrn9ZEK/NiW+ReFX4BIh05wKgKpJBXsN6wSVxgPIL8s2hL2u8ZMWlnmtXrVKkqbBsGTXcRbenqaNK1dRVZk5cjbf6+S5nVLMxj6J9kwPjW+06ZBdenRtn65ahbK9nkKnfSjTJ+qYE0GY3yHxv9Ar/a98YQRyV3I8swzlhfNVi5S4p1QIxz/QNv/5GptHKnAfaTise/iwf+E6pLFjklGO9P3qbvepunityEirWkyX/iUP8gMlaXOLYG/2/pI+beICDfLoE37Itj/kQwkH+q1RIkkQCx7M1TKpj8Pm5kwQCAl9nFfHymP4bPiOgRSSSrYiSyvpO69W8u6cm6XproAL9uSLTv35hfXUqjPeYoDlcY1K8uGnXtN36SaA2Zl75eLuzUPmBUwkZrXq6oDKdVnK1qThh3vG9g4lGBpZf0fl+2LZE764X/yCtZsKNZBSNnmcPHP2mdu6hO77w0UrDr1hBIu0LaWtS9YHylx58WlSxY82prV4E37/Jv0lm/5Il2GUPOJ9ngT7BiS6jVS/udf76aJZb/RcDUj0Tbti6hGKsh+D/RROy+MI9ke/uU2UIAZrmwH+mZbA0gHNe0rsljD6cbDA4FUkkhPi6w6VzWFUzo3qyXnd20mbnZnv/a6z5XqI6WUWeUQd0h/X7xVDwq8eff+yAKpIFXP/p0kA92JNCb98K9pKkrQOFL+tRb+mcPi00cqPgfzQIkzgi9D4MeJ4j9//2UtsBAQBesj5ZY7/HY1dyqvaPsSBlu/hPaRinA+0d4RtlIzEcl7ycLKzZ6YzjfqcaSsXvgG3u+BzlV21phHcvwvc7PLNFh22c8GuknNgLxmVnc5NVJwhMyM4v5C4Q6G+0umiXdyhkTo1LS2/O+Cw6P6bL8X/9CDAu8PML5V+NqVyAbkLX7fE3wgQ7/22MH6y8SjaV+ZsWzikEI7XheLVpIWOKvTbdnyEYtxpNxyYeraPlLR1tY4oo9UZPMJVjMebZAZuvZLkl6glglxn6fFgChYH6lgiSRMnzVOE7aPlENqZoLsBP9ze6BtGO53FPg1+9Y72PVKohRZPJ6Yllfcx/1X09Aq+gVGwcqut2mfStSQyipXyNB/9+dHFkEEO0GVHZDXP2Axf0+Z6YM2jYnthbJ/u3f/2pF4HPSjbRIVTQbCRC9DpMwXqGXLg7VxpAyfM10EiSuE+i04mflCMfLPBR8ANPzA3bESTS1RtMFipB3M3bLfY1rWSy4PjS+H6kcW0TwC1owYH3ssHZvCvR78sw5u2hfBDSf/MhyoFsu03wIFi0XOHZDXjsXwWDyexGuolERJ7avpJJLpN/pusB+xt9aKQKq46O/LL4zZBUNx6tTgnyvzWb/22OaTYPDviEeyifj0kfIPKmMQEAb4jkjvfjujj1TZ/RBo2tSokRJXiDboCd20z2l9pEofe2KwbUL3kXLJjndhWbdy4yjSG3fhxoZydLKJCIJC//N5oG0RrmYrYI2UjTe4nNVHSiKYvvSxG48OBFJJIjPdvCuDFV5vfyL/GqxUUymzuEZqX4RN+yK5YAh0oRwqe1NxM4Jg3xvdXfCom/bF4ehlpQletN8Zrzvt8QukzO9bGQ/KuPjmPlLiCqF+C04Wff+hIK8nsFx6EtpHKrJ1cslud11ZD9YsLZhIa6HCNWULd66y84ZJJP2A/ZvbBwqawgVkzh6IOPHzL7J4A9O0n8R9UvtqOolUsFgjVSnlm/alW2ra54mwWUO45kuhAq9gJ6dY1KSESzYR76Z9sZqH1TuezjqhBK4ZjPzCx3DCNyWpcMepx601UtHWDodqShTseBJr0QQ30a6juUY9sqAtWZVZf0/oVgfRzaN8yXWC1nD7fTBQAglzS4zA3xPs+xLJtI5BNrh/q5CATfvC/I6cHEDafgOxKPz0sfxd2IFAKkmb9gX7EdO0r1ilkj5SkTbtMx1Ig9QGRFIjZf6eyGqhYnFA9j84hQriYsX/ABqvQKqkkjUgO5vABcou6N9XzZi1z8o4UvmGvlVuuTCNR1PP5BiQN37bwXSBEnK66JbHE9W8JOkFOt4mZr7BAyJ/ocZDtHLzKlztg71N+8Jf0Ie+uemJqMbWyQPy2rEYHos3ZqiRgiNU8GvaF7RGimQTWmVf075okk2IpQF5g3+PX9M+U4AW2wutQCdHc5rX2B9tyzYnLP93BvqOyMeRkoTyvzvr3y/NP9lEuDt3xq8LluHRyfzXLxZp/Z3cRyrYpPoGioU01eVRtsY88MzMxyEL3x/hxXiwZUpWdtS+qllY6fca7GZd2XNYoM+GvwEY6rVEiWSsRP/lN9dQuX9AXttrpDyxn95pqJFKEv7pzIOVxWRKf14elSw27Ytk4MlAFyqh2pv7N/UKdjETj75F/rVh8biojcdd2XI17UvwETrQvvdfVnMfqcjvIDOOVOJEG4yHunCLddPdSJchaHAX5fJ4oko2IUnPfxW9m6a8mfpCzaM840iFunEXrilb+GZvzqiRCl72zdMH2obhynbAfWHjets97IfHWD4iKPMkm4BDm/aFydqX4oFUaY2U9ax9oTpYh+tkXGb6IBfSwcbpiJb/dySkaV8c7spaveNp552ushex4bL2SZR9pMQVwtXWOpXVZiql0wZ/PdparnglPYj2d0IfqfDbJdR2jzUrAXGk55tAfYuCDQkQaf8hJ/12/WugAtXqeVw8IK8dh9kiizefTPtA3Ce1r6aTSGZ6mrWsfSSbsNhHKvC29R+oL9wJ1P/AHvyObgKa9sX5Ys7/BByLWi+rdzztbOJQtkau7GvW+kgZPufCPlJ2XVwmYiyagJ9z2IC8oeYVi2Axkv4o/vNKVh4bbhoUt4gQC037ggVDft9bZCEIc3CNVLBkE+YbVGXP6ZH0kwxU3p3SR8ruJEtFEWwH0yQuPD4QSCUJ/6Z6wWoxSDZhTn/ubeoYTiRtwv1reQJeTPslqjAe3IPdRYpNIFX2eaC24I4fRyrgCSv49Im68x958BqqRir48vlvO2PTPvf0NYrsot5por0ocdqAvPGukYp0ndwSQMf02JeAeVjdj5E2zwtXyxQugC4O8OzZ58HOr6GOwYHWLVhfZv/pwr2WKIlqOhyMx+LxxFSGxH0IpJK0aV8w3mQTpD+P0YC8fgfqsrUQwS8eyzavCx+sxbaZWeDlipVQ/cOi/05rJyw724qHqwUsm30v1HeZnzOOVOLEov+Q6fUomr7E7mZGJOUrunWM9JiVAnFU0ADWfLMs9hvCSg18pC0gwh1zI7npZ9c+jyS4998n5sDQE1HZDvgaA/JGfN61O8tgeWXavQCIT43U498vkGqVSndvRlqaXNC1maGPVHGNTKqqXJL+POJxpILevQtd4xDqjrB/X4lg/WViUpPjXxOi2oLHuVYjUL+scn9nmLujod5LfB8p83O16KHGaLFyF9+N40hFelHvNNE2wwvVR8r4VnyTTZifB+v4He06BmvyFKqDuVvKq/tqX81NxcNt5mDjP5XJrhngi0zBk18ri4Dz8ngkXSK72RtLkdxIC3VOLk02EXj6QPMJN79EMAe3iZ9/kcXrF1NgLu5DIJUkTji4ofy1NMv3/Kf5m8tMsyIrxxdIVchM/EHNSSqXq0Yq8B3qgMkm/F7wn95/MMDA84toEcMsv/9yxb/6P9JmReX5Tmf3kSobSPovQqRZ+/zfo49U4piCHgun+VTrIxXsWBbqM8nKf/3jcegJ9JWBalOs3mQK1zxdMd2Ei6AfknrdjotN88DBQabxW/dAwVe4ZvCBNrWd9wvsPO+Ve0BecR8CqSRx+TEtpX71SlK9UqYs3bLH9N6Gnftk7ORVkpW9n6x9UfeRCvbY/25W6JOQfxV28GaCsT0Q+tc4lU10Ia6ogQh0Egs9jlRs51/epo0ha5YsdNA37k+33OCPxwDNTrwoiKyPlMe5faQsHAyi6SPllmyN8egfag7KY89KDXyk5xs3N+0ztJy2UCNVdruEb9rnrBopu5vKeSzemElUU+d4IZBKEhnpaXJa5yb68fEHNzS9t2zLHh1I7czNl/1k7YtpjZT/HdlQA/AG+p5gzfliP45U2aAm3hdzCauRChmARHeBmKhkE9HWSEXaJNBJkiPZRHS1NaEG5I3n7ivzu4/grnyky1N8vAs8r9DNbV2y4x127AvH/5gerHbIOH0kAVi42pZI9rtd+zySG5KmdSkKfE4ONyBvoG8uSuEaKY/Fpnqm44+4D4FUCqhZpYL+u3tfvu+gkerpzytVsDaOlKnqOcRJJNxd92B9oozT5uYVyI/zN/len7N2p0xdsU0a1qik96WqebSqbM2YW8eRsnbnL97rWJ4EH1ZqlkIlm3BLQFK2uZM7Fjzau6VFjqiRCl+74P96pMsT6JgSyXe4pbyWh38T0Hjs4kDfae6v5Cl3c3X/77T62dJpxBaRHP/907ebztEBaqQ8kbaUsDMkiOLGiJ19pDzUSMHpapUEUsbyXCnFk014sxZGmmwi+InDPE244MF4wPAPpLzvfT17Q5n5X/jGVF/N47hrjpburetFtNyBllnP238E93gkm4jghFze7yx+LdT0oT8bT2UuNP3GKLHS14kaKftYbaZSOm3w74v2O60KFeyYp7MemIdqxhy6uW3yR1LBanWC3ZCLx3zDbWdzgong56VA32PqF2U4jwRLWmTXEA2RXND7B1vh+0NF+pqkbI1UkcXjiel3Ie5DjVSK9AeqUiFD9hqasaV6jZQ3a9+GnXvl9o/+lfS0NFFJhdKCZBbakZvnezxpWZbc9ekcX22RV87+Ann771Wmzz374yJdi3R0q7py3lHNTAcYY62C8WSzdHO2/lujcqbcc/LBMmH+Zvl3zQ6dun5ffpEMfvcfaVCjkqSlqaX1SHZ2hoxcMVnS09MlPb14HdT4zPr9NJEK6elSs4r5p14mzWscDrZlayBi/51WLtoSXQMSuGmfRDeOVFGogXzFFexo7mTngLyR95GSuIm0VjiaGw6h9meqD8gbqDY63tQcrJSr4P1+g08X6LVIgnDbxpEKESAGa54aqIl9uFppJw/Ia8dieCweT8LV+DkdgVQK1UoRSJVSzeSUnLxC+SpADVAoK7bm6H/+1JhAU1ZsM702YUFx9sTPZq6TbTl5smDjbt97xv1hPPitzCoOpFQQdcnRLfQ/RSULOfHFP3Rft937CgyfTJONucWfiZR/p9rENO2LV41UiAAkxAWCE5JNRNpEjxopcd1FSbBpiy94/af16BsfdjWnjOZCJlSQlurJJrwbUe1SXQMZZLK3/14pL05YUuamWqA+vU+dfZicdOgBZWcSZUAcrP9QuH6+Vj4baLkSKVyQF+j3EegGozm4irBGysYqKbfVSBXRtA9uULtqBdm0e59+rGorVBOxVNa0TlV59+pusmxLtv4Rq99xuANOk9pVdD+zPaYgRvR4XXWrVpS1O3L188z0NGlVv5os3VIc3ExamqVrsZ7+YZHpc95U9F7e+a/aVvw9repVM72v+kZNuKOPrMrK9fW3yS/Il6nTpkvXrt0kPSPDd4fNuE5Z2Xly35dzTd81bcU20wFu+dZs+WPJVv24faPqckCtKuKaZBMeZ55QAqW+LxsQFVmuWbOSpMJJIul7EYkPp6+RdyavChioqJr2YScdLL3a1ZdYibYMheqPFKgPTRziqLI1I0Gmi7RZXiw6mLu1j5Q6Xl/77j8yf0PpzTB/ah8eWLuKbC4516rxGws8HvlzyVa5YNQU2bW3tGXDT/M3yc69+WXOA4Gom27P/bRYalYubqavLNuaXa6+JsEudkM1Rw//2fCBeiKFW7ZANwMCbYtwgUHAWioby7mdA9FH0xKEZBNwVcIJJdWb9Xn1ad9A/4uXvh0a6b/nH9VMnhy/UPbsy9fPq1eqIGr85F17801jfo2btkYHM2u2FwdSLeubAymlYY3K+p9Xfn6+7FjkkV5t60mFCqX72N+SzcWZG72en7DE9L6qMVP/lDpVK8jvdx0vtaoG/75IpHqyCf/FKm5/L1HWSJmfF5r6VokrRNL3IhLDf14im3fvD/r+K78ujWkgFY8BeQPdZIjHgKWR3syI5neSan2k/lm93XezKZSte0rL5uHNass/q3fo1gjbVm43TbelZLoeberJM+d0Cvp9+wsK5fRX/5blW3Pk4jHTgk43Z91O03AeqtXExJIWEeq+aZcWdaR21YoB+zaZ+kiVudlTdl6mz0ZQduwLpEIHloGaWgdK2BGuqZrV81K8hQqMEz9/T9jpSTYBV6htDKTUVTwSpm61ivL8eZ2Dvv/UDwtl1B8rZGVWjv7nrX1qXLM0YCov1UxQUSf0rXv26WaIBYVFumZS/cvNK25muG7HXtmRmy8nvPC7rx9ZOA1rVpI6JSfoNMOdWe+FgtcDX83TtXflofqh+Zu7fpdc/tb0gNNvMVxw/7Zoi1z2ZuALkTJNPAz31EP19QqVnWuf353m//28xJd2P9D6qNrRc0ZOLrMdFbW/jPYYPqeafB71+ETDu2mmz/p/l1rkffsz5Mn5fxT3DTRNU/yoU9NaMnTAQb79ZbrENzwx9ik01qgYp/d+p2pG651ObSd1J35HTvEd+ioVMyIqb5t27dNBlLowHHtVN13766Vqi69/f5bMWLVdXpy4RM44vIm0aVBdyiveA/IWTyuuG5A3ZB+pEF9h1/Wl2hb6QtlQY6+T7qjHJQOUe5+riuL8kmghPb24z+nvi4uDqBM7NJK7BrQPOI/8Ao+s25Gr51GtUoYc06aezFy1Qx9TvVrUqyrbc/J0ywZ1Ku7Rtr6ppimQ+wZ2kPenrjZtY9Xiwbgt1TnE6OeFm/U/ryOb15bPb+jh+z0Gq7WIpEl2sGElgiWVsDL+mp01UsGb9oWuYYm0lipRAq1XPJoOB+MxlQ+L+0nchz5SKcJ7oatEeoGMxLitbzs5vGltUyr2I5rV0SfwWFH7/OHTO4ad7ucFm+Wad//RAVek1u/cG9F0/v3HYkX1GVNNZ8JRTVu9zVvtoAKlUCcQ1Xxn5uodlr9XnbNU801r0mRX3v6Q+/SHeaUp+GNJBT8qMFR36o94rDgAVAFmn3YNpHrlTJ0cJTMjTSpkpOsgXz8ueW39juKy1r5RjYC1yYccUFP3Q3z5l6X6bvz4W3uV+wIi0iQK/kImm4hR7ZzlYCfYha7fnftILryi7SNlx516FRSd9/oUmW1IDhStvh0aysGNawZ9/7CmtUzPVaBUXpd2b6H/GakxEB/5doFs3LVXsvcV+Gq3VYsTlZXW2AR9wYbdMmvNTjlzxN/6d6XOLSsMTQPVDTTvTSZjYiXlwa/nSQ2/QG9tSasJZcaqHXLHx7P140Wb9gRc/oe/mS9VK2b4EjqV5HbS0oyv+Ypc6TTe90unLb05U+bzhhs66iaROsZ4qdq6FycsLl2otDRdGxiqtth7/yrcTQIn10h5n2cksDeHJ8TNxkDoIwVXOL9rM92mWh18z+3S1O7FgUHViply8mHGTsT2OfGQRvLH0ON8zQ6NAjeN8MjaHXtLBzb2mA+e6mTWtmF1WRzkBBut9o1r6AsFFWsaa50CaV6vquzSCTpK18n/GjFYzYo/0wm8zHuBv+/gA2roIMqb6lxNd3DjGnqbqIsfVWOp7khv2LXXbzubm5V4SpoKqe/JKyzU5UYFHht37jNtc9/0AU5m6rWCggKZNGmS9OrVSzIzMw3fX/xgyeZsHYiojJalS+H9Tk+A1yRiqjb81r5t5Z0pq01NoFQ2Sm9ilkgc0bx2wNefPbeTfDFrvXwwfbUs3Lhbjn/+dx2MFWe4LN72Genp0rtdfeneum7QLJ1G6iLTGOz+tnhLyOlVf0m1T421EEZqXVWNhP+FaTxucKna50huemz3u3hWzYu9tZWK6ou5bZ/ofqAVMosvqv37im7P2e+7wPbWcAeian3WlPQDLVNmIypnwT5jmNZvGnWDwkoQpYJ9Fbir/4z9TQ+oXVn6HVLcZNtuqryoBBSReOTb+Tqj7Jx1uwK+r8r1X0uzAr6nAqVQVJn68t/1IacxjotoF1Vb9vKvy0yvqeOc0bhpq2VDyfFU+XbOBnn09I7mAbQDBAaBDoFO6SOlqLKbEYemw/FIf+5GafP/WOLyVSi/7JxsOXrgkbJr1y6pWTP4naZ4U/1dxo8fLwMHDgzZ3wWgzMDpx5lA/QsUdTfcODixevzXsixZviVbX+yo4FLVIKjH3iao6jWV8l0FkeqO+uA+raVZ3apB5/3Q1/N0sAYYXd2zldx2YjtfwiUVLKp4Uf0t/me+WZIsVD+rqSu26xte3t+euthV2XxV810VAHuDRfWe2gLtGlUvrmEKcoXYqFZlydqzv0wNlgrwVLP01duyZevy+VKnZQdJT8/QAYiu7TQFwaXpx72JkoyBsfEz6kGw97zPS760ODNmSTNOVdutatQ2ldyo8lIJoFS/s3BO7NBQB5Pem4vdWtaVM4840NR0WrW28B//UX2uVwxqI6Mxefk2042pB089pOTGQPHdpOA1fSWvmWoMDdP7fabM+1L84ojflvkyFLdpUE03Ew/lpV+W6RtfXj/d2lMOahL4Zlki7d69W2rVqiXTxs+S6tWCNxOnaR8AIOa8F6SBrkvVe6VNTdLk+IMa6n+xcv+ph+iLHdUPy3Sh5SlOvPL93I2mjvnhVKuYqe/ahxtYVM1D1RB6a2RU3y9V+6TG8VMXMqpPnBrXT6lUIV03S1XvxZP/fINRF5w7gi6PRwoLCyVDD+Ru3qFqu6gL8kDZ51Ttk2rSpdZTJbFR36+beJXw75tnKBIB3y9+HPqzxuZhxs/Uq1ZRrundyjdAfSpR+/7YEImVjmxeJ+DrnZpGfzGbn19Xxm+fJwN7tnTkjWF1s0b1m1Vls22j6jqY3J9fXIZVzeO0Fdvl1d+Wyc8LzTXQ01dt1//CUZ/z/6xdHv1ugW3zXr41R/ddtWLiwi2OCKQiRSAFAEgqqtbqiCAXh6rPypU9WyV8mZKjFnOAIy+KgWiOEf07Ng4aTPZu10A30Z22srhvr6rVUqG5qpkyNyctrhlTfdOqV8rUNZuqZs9/nEg7gucalTNla/b+klq6khq8ALV5JWtjeK24fs/43DdViO/SSl5TTc/1dgjRzNeoaqVMKSoqkvWbs6RxzeJxPt2CQAoAAAAwuLpXK/0PCb5hc3gTV21y8mADAAAAgEUEUgAAAABgEYEUAAAAAFhEIAUAAAAAFhFIAQAAAIBFBFIAAAAAYBGBFAAAAABYRCAFAAAAABYRSAEAAACARQRSAAAAAGARgRQAAAAAWEQgBQAAAAAWEUgBAAAAgEUEUgAAAABgEYEUAAAAAFhEIAUAAAAAFhFIAQAAAIBFBFIAAAAAYBGBFAAAAABYRCAFAAAAABYRSAEAAACARQRSAAAAAGARgRQAAAAAWEQgBQAAAAAWEUgBAAAAgEWZVj+QjDwej/67e/duW5cjPz9fcnNz9XJUqFDB1mWBO1BmQJkBxxk4DecmuL3MeGMCb4wQDIGUiOTszdEbo1mzZonYNwAAAABcECPUqF4j6Ptp8/9YEjrUSgFFRUWyZdsWqValmqSlpdm2HNm52dL33D7yy2d/SvWq1W1bDrgHZQaUGXCcgdNwboLby4yqiVJBVMN6DSU9PXhPKGqkVEex9HRp3KCxOIUqQNWr2V+I4B6UGVBmwHEGTsO5CW4uM6FqorxINgEAAAAAFhFIAQAAAIBFBFIOUrFCRbnxypv1X4AyA44zcALOTaDMgONMYCSbAAAAAACLqJECAAAAAIsIpAAAAADAIgIpAAAAALCIQAoAAAAALCKQcpAPvnxf+l1wvBzR71C58Ppz5b+Fc+xeJNhg9Puvy/mDz5auJx0hvc/oLrf83w2ycs0K0zT79++Xx/73sPQ4rZscddLhctsDN0vW9izTNBs2b5Abhl0rXfp30t/z/MhnpKCgIMFrAzuMHjdKOh7bXp565Qnfa5QZ+Nu8dZMMe/wufRw5st9hcuaVp8q8RXN973s8HnnlzZfk2LN66vcH3XmFrF63yvQdO3fvlLsfGyLdTj5Cup/SRR545j7Jyc1hYyehwsJCefnN4dL/ghN0eTjpor4y8p0Rupx4UWZS2z9zZsiN91wnx53dS5+Dfvlroun9WJWPxcsXyWU3X6Svl/ue20fe/GC02IVAyiF++PV7eXbEU3LjFTfLp6O/koPaHCzX3TVItu3YZveiIcFmzJkhF511qXw48hMZ/cLbOvi59q6rJXdvrm+aZ159Un6f/Ju8+MhL8s5L78vWrC06mDKe8G4cNljy8/Pl/REfyZP3PiNf/fCFvPrWS+zPJDd34X/y6TcfS/s2B5lep8zAaNeeXXLpzRdJZkamvP7saPnm3fEy9KZ7pGaNWr5p3vxwtIz74l15aMgj8uHrn0qVylVl8F1X66Dca9hjQ2TZqqUy5oWxMuKpUfpC6uHnH2BjJ6E3P3hDPv76A/m/2x+Qb9/9Qe64bqi89eEYGff5e6XTUGZS2t69uXJQ24Pl/tsfDPh+LMpHdk62viZq0vhA+fSNL2XIDXfLa2NfkU+++UjsQCDlEO988race+r5ctbAc6Rty7by0JBHpXLlyvLF+M/sXjQk2BvPvSlnnXy2tG3VTg5u20GeuPcZ2bh5gyxYMl+/vyd7j3w+/jO5+6Z7pfuRx0jHgw6Vx+95SmbPmyVz5s/W00yeMUmWr14mT9//vHRod4j07n6s3DLodvnwq3GSl5/HPk1S6q6dqmF4ZOhjUstwQUyZQaCL4sYNGssT9z4tnTp0lqYHNJOeXXtJ8wOb++4cv/fpO3LdZTfKCb1O1Df3nrrvWdmybYv8Mqn4LvPyVctk0vS/5NGhT0inQzpLl05HyX23PaBvDG7J2sxGTzKz5/8rJ/Q8UY495ng58ICmMuC4k6RH154yd9F/+n3KDHp3P1Zuu+YOObFP/zIbI1bl47uJ3+ibxI8Ne1JfJw3se6pccs5l8u6nb9uyAwikHEBd2KqL5GO69PC9lp6eLt279PBdGCN1qYtgxXthPH/JPCkoyDeVl9Yt2sgBjZroE50ye/5sade6vdSvW983Tc9uvfSdnOUrlyV8HZAYjw9/RPocc5wcc1RP0+uUGfj77e9fpePBh8kdD96qm/6eM+gM+fTbj33vr9u4VrK2b5XuXY7xvVajeg0ddHnPS+pvzeo15dCDD/NNo45L6vz13wKapiebwzseIVNnTZFVa1fq54uWLZR/586U3kf30c8pMwglVuVjzvx/5ajOR+mBwr16du0tK9es1DXtiZaZ8DmijJ27duimWPXqlF70Kuq5f98YpJaioiJ55tUn5IjDjtSBkZK1LUsqVKggNWvUNE1br049Xz8pdbAKVJ687yH5jP/lO1m4ZIF8POrzMu9RZhDookY107rivKtk8KXX61qFp15+XB9bzjzpbN+xxHgzxnsc8R5D1N+6deqZ3s/MzNQ3ffz7bML9rrnkOsnOzZZTLztJMtIzpLCoUNc+nNrvdP0+ZQahxKp8qL+qRtT0HSXfmbVtq6k1RiIQSAEO9vj/HpGlK5fKe698aPeiwME2btkoT7/yhO5TV6lSJbsXBy5QVOSRQw86VG4fPEQ/79D+EFm2cql88vVHOpAC/P3423j5fuK38uwDL0jblu10jdTTrz4pDeo3pMwgZdG0zwFq16ojGRkZsm2H+Q6eel6/bgPblgv2N9P6Y8pv8vbwd6Vxw8a+1+vXq6/bB+/es9s0vUpM4r3To8pNoPLkfQ/JZcHieXr/n3ftWdLphA7634zZ02Xc5+/qx/Xr1qPMwKRBvQbSpmUb02uqifDGLRv0Y++xxL9myXheUn+3+yVEUslxVPMa/7vOcL8XRj4rgy4ZrPukqGQ2pw84Uy4/70oZM26Ufp8yg1BiVT7U3zLXN97arnqJv74hkHIA1c7zkPYdZerMKaYmXdNmTZHOHQ+3ddmQeKpDpgqiVNrQt4a/qzuBG3Vsf6hkZlbQbdW9VBNQlZBCtWFXDu94uCxdscSU9XHyjL+lerXq0qZl2wSuDRJBtTn/6u3v5PMxX/v+qSQkp554WsnjwygzMDni0CN1nwKjVetWSZNGB+rH6rijLmrUechL9bFUw3J4z0vq7+7s3TJ/8TzfNNP+narPX6qjOJLL3v37JD3NfNmYkZ6uazcVygxCiVX56NzxCPlnzj+SX5Dvm2bKP39Lq+atEt6sT6Fpn0Nccf5Vct9Tw6TjwYfKYQd3kvc+e0f27t0rZ518jt2LhgR77H+PyPhfvpVXnhgpVatUk63btvo6ZVauVFn/PWfguTpdvjpoqODoyZce00GU92DUo2svadOirdzzxFAZcv1QfQfolTeHy0VnXiIVK5Z20ERyqFa1uq8PnVfVKlWlVq06vtcpMzBSNQmX3nShvPHeSBlw/ECdNv+zbz+Wh+96TL+flpYml513hYx6d6Q0b9pSmjZuKq+8NVwa1msofXv109OomzK9uvWWh567Xx4c8oi+c/zE8Efl5BNOkYb1G7HBk8xxPY6XN94fKQc0OkA37Vu4dIHOOHzWwHP1+5QZ5OTmyJr1q30bYt3Gdbqc1KpZW5o0ahKTY8opJ54mr73zqjz4zH0y6OLBsnTlEnn/83d1JmM7pM3/Y0npSGqw1bgv3pO3P3pTd7ZTaa/vu/UB7uqlIDWIXSCP3/O0TouuqDEXnn3tKRn/y/eSn5+n0xbff8fDurmO14ZN6+XRFx/STbyqVK4iZ5x0ltwx+C7dcRPJ78rbLpWD2naQe2/5P/2cMgN/aiy64W+8IKvXr9IXNZeff5Wcd9oFptrxV996WT797mPZk71bjjysizxwx8PSslkr0+CZ6kJHfVd6epr06zNA7r31fqlWtRobPMnk5GbLy2++pFtLqOZXDes3lJP7nio3XHGTL4MaZSa1Tf93mlx1+2VlXlfXH2o8y1iVDzUgr+pDPm/xXKlTq45cfPZlcs3Fg8UOBFIAAAAAYBF9pAAAAADAIgIpAAAAALCIQAoAAAAALCKQAgAAAACLCKQAAAAAwCICKQAAAACwiEAKAAAAACwikAIAAAAAiwikAACIsS9/+EK6n9KF7QoASSxt/h9LPHYvBAAgNW3dtlXGjBslf0z9XTZv3SQ1qtWQZge2kNP6nS5nnHSWVKlcRZyu3wXHy2XnXiGXn3el77V9+/dJTm6O1KtTz9ZlAwDET2YcvxsAgKDWblgjl950kdSsXkNuv+ZOade6vVSsWFGWrlgin377sTRs0EhO6NnXli3o8XiksLBQMjOjO01WrlRZ/wMAJC9qpAAAthg8dJAsX7lUvn3vR6lapWrAYCYtLU1279ktz498Wn6d9Ivk5edJx4MOk2E33ysHt+2gpxvx9svyy6Sf5crzr5ZX3npJdu/ZJb2P7iOPDH1cqlWtrqcpKiqSNz94Qz799hPJ2r5VWjRrKddffpMMOO4k/f70f6fJVbdfJq8/M1pefnO4LFmxREY//5Y0bniAPDviKflvwWzJ3bdXWjdvLXcMHiLHHNVTf+7K2y6VGbOnm5Z7/h9LdNO+Z159QqZ+P9P3+kdffSBjP35TNm7ZJE0PaCrXXXaDnD7gTN/7HY9tr5f5zym/y98zJknD+o1k6E332BZMAgBCo48UACDhdu7aIZNnTJILz7okYBClqCBKufOhW2Xbju3y+rNj5NPRX8oh7Q+RQXdeITt37/RNu3b9Wh1MvfbUKP1vxuwZMmbcG773R48bJd/89JU8OOQR+fqd7+Xy866Se564q0wQ9OIbz8sdg++Sb9/9Qdq3OUhy9+ZIn+7HypsvviOfj/lKeh3dR26693rZsHmDnn74Y69K4waN5earb5Pfv/hb/wvk5z8nyFOvPCFXnH+1fD32OznvtAvk/mfulWmzppqmGzn2VRlw/ED54q1v9XyHPXaXaT0BAM5B0z4AQMKtWb9G1zi1atbK9HrP07vJ/rw8/fiiMy+R43ocL3MX/Sd/fTVVN/tTht54jw6aJvz+o5x/+oX6NY+nSJ6892lfDdRp/c+QqbOmyG0ikpeXJ6Pff13GvDBWDj/0CP1+sybN5d+5/8gn33wkXQ/v5pv/LVffJj26Ftc2KbVr1vbVfCm3Drpdfvlrovz29y9yydmX6ffTMzKkWtVq0qBeg6Dr+/bHb8mZJ50lF511iX5+5QWt5L8Fc3QN1dFHdvdNd8bJZ8spJ56qH9927Z3y/ufvytyF/+kaNgCAsxBIAQAc46PXP5OiIo8Me3yIbsa3ePkiyd2bKz1OLw12lP3798naDWt9z5s0PtAXRCkqqNm+Y5t+vGb9atm7b69cc9dVpu/Iz8+XDu1KgySl40GHmp6rhBGvjX1FN7fbun2rFBQW6nlv3LzR0nqtWL1czjvtfNNrRxx2pLz/2Tum1w5qfZDvsaqpq16tum89AADOQiAFAEi45gc21033Vq5daXpd1RQp3kQNKohSQdHbw98v8x0qSYWXf1II9d1FHo/vO5SRT7+h+x0ZeWu5vKpUNjczfH7kMzLln7/lrhuGSfMDW0ilSpXljodukfyCfImHUOsBAHAWAikAQMLVrlVHJ2z48Mv3dRO5YP2kDmnXUbK2Z0lmRoYceEDTqObVpmUbHTBt3LzB1IwvEv/OnSVnnHS2nNinv6+GasOm9aZpKmRWkKKiwpDf07pFG/1dZ550tum727Rsa2l5AADOQbIJAIAtHrjjYd1U7oLBZ8sPv34vy1ctk5VrVsi3E76WFWtWSEZ6uhxzVA/pfMjhcsv9N+pMdus3rpN/582Sl0a/KPMWzY1oPqrJ35UXDJJnRjwpX/34he6ftWDJfBn3+bv6eSgtmrbQiSIWLl0gi5YtlLsfG6IzABod2PhA+WfOP3ocrB07twf8nqsvHCRf/filzty3et0qGfvxW/LzXxP0cgEA3IkaKQCAbc37VCa8N957XYa/8YJs2rpZKlaoqGuQrrpgkFx45sW6advrz46Wl0b/T+5/+h7ZvnOH1K9bX47q3FXq1a0f8bxUkoi6tevqwX/XbnhANwvs0L6jDL70+pCfu/ume+X+Z+6TS2+6UNeiDbr4WsnJzTZNozL2PfzCA3LSxSfqxBYq/bm/vr37yb23/J9OLqGy96n0548Pe0q6HXG0hS0GAHASxpECAAAAAIto2gcAAAAAFhFIAQAAAIBFBFIAAAAAYBGBFAAAAABYRCAFAAAAABYRSAEAAACARQRSAAAAAGARgRQAAAAAWEQgBQAAAAAWEUgBAAAAgEUEUgAAAAAg1vw/XU4Vv+AIv5UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The final best chromosome is 0526958995019879299707315930420015797974\n",
      " Encoded values: lambda_k 0.526958995, lambda_T 0.198792997, lambda_s 0.731593042, lambda_r 0.015797974\n",
      " Total cost =  50000 chromosomes\n",
      " Final error =  321.64337752915134 \n",
      "Execution time: 4 hours, 35 minutes, 42 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\")"
   ]
  },
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   "cell_type": "markdown",
   "id": "9878b205",
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   "source": [
    "*Our observations.* \n",
    "\n",
    "For 100 generations of the Master GA, our observations are the following:    \n",
    "\n",
    "1. The initial errors between predicted and observed were in the order of decimals. These were obtained by rudimentary Evolution, made by the Author by making changes by hand.  \n",
    "2. For the small experiment,    \n",
    ">>> - The optimized values of the four parameters were:lambda_k 0.67422946, lambda_T 0.187559769, lambda_s 0.640827007, lambda_r 0.023058801. The fitting was very good.      \n",
    ">  - For another run, the fitting was not bad. The optimized values of the four parameters were    \n",
    ">>> lambda_k 0.201150048, lambda_T 1.072661032, lambda_s 0.782895876, lambda_r 0.016005215.    \n",
    "> - Third run with a good fitting:     \n",
    ">>> lambda_k 0.005948459, lambda_T 0.580856665, lambda_s 0.71, lambda_r 0.014.     \n",
    "> - We see that the parameter lambda_T observes a high variability. This parameter quantifies the participation of touched sites over the overall performance. Also, the parameter lambda_k is not strongly restricted because it determines the initial condition. So, it can change a lot if only it remains small. The other two parameters are important for the recursive process, so they variability is very small.     \n",
    "3. For the large experiment :    \n",
    ">>> The values of parameters were k 0.17135506, lambda_T 0.735381264, lambda_s 0.787101097, lambda_r 0.014983946. The fitting in one run was good.      \n",
    "> - In another run, the fitting was also good and the values of the parameters were    \n",
    ">>> lambda_k 0.62533042, lambda_T 0.111140669, lambda_s 0.705993696, lambda_r 0.015598979.    \n",
    "> - In a third run, the fitting was also good and the values of the parameters were    \n",
    ">>> lambda_k 0.305317511, lambda_T 0.535010903, lambda_s 0.780780999, lambda_r 0.015380011    \n",
    "> - We confirm the previous generalization: parameters lambda_k and Lambda_T  are more flexible and admit more change than the others two, because the first deals with the initial condition and nothing else, the second with touched sites by mutation, but lambda_s deals with the effect of solved sites among touched, while lambda_r represents the effect of inheritance of solved sites from the previous generation.  Its critical importance is revealed by its low variability.    \n",
    "4. Evolution is effective: Evolution improves the quality of predictions from an initial error of the order of decimals to another in the order of millesimals. Thus, for this problem, Evolution lifts quality near to perfection. It is astonishing that a model with constant parameters could produce so good a fitting.     \n",
    "\n",
    "**Exercise.** The results reported in the previous observation say that Evolution gives to some parameters very diverse values. This might be an operative error. So, answer to the following \n",
    ">\n",
    "> *Objection.* This conclusion is premature because only 100 generations for the Evolutionary Process is not sufficient.\n",
    ">\n",
    "> *Answer.* The objection is correct. We have no right to claim that 100 generations are enough to achieve a final estimation of the parameters. To remedy this, the program was run during 1000 generations for the Master Evolutionary Process of the long experiment. \n",
    ">>\n",
    ">> - The values of the parameters for one run were:\n",
    ">>\n",
    ">>> lambda_k 0.407185299, lambda_T 0.295248246, lambda_s 0.760910943, lambda_r 0.012388185\n",
    ">>\n",
    ">> - For another run, the values were:\n",
    ">>\n",
    ">>> lambda_k 0.426610715, lambda_T 0.401010011, lambda_s 0.780849601, lambda_r 0.010760077\n",
    ">>\n",
    ">> - For a third run the values were:\n",
    ">>\n",
    ">>> lambda_k 0.388449127, lambda_T 0.380907956, lambda_s 0.79101312, lambda_r 0.010200201\n",
    ">>\n",
    ">>> Fourth run:\n",
    ">>\n",
    ">> lambda_k 0.526958995, lambda_T 0.198792997, lambda_s 0.731593042, lambda_r 0.015797974\n",
    "\n",
    ">> - We see that for 1000 generations, the variability in the values of the first two parameters seems to be smaller than with 100 generations alone. \n",
    "\n",
    "**Exercise.** Compare the values of the four parameters with the signature of the four types of fluctuations that are present in an Evolutionary Process.\n",
    ">\n",
    "> *Answer.* Let us adopt the following values for the four parameters and the corresponding micro-observable:\n",
    ">>\n",
    ">> $\\lambda_k \\in [0.38, 0.53]$: initial condition.\n",
    ">> \n",
    ">> $\\lambda_T \\in [0.19, 0.40]$: number of touched sites\n",
    ">>\n",
    ">> $\\lambda_s = 0.7$: number of solved sites among those touched by mutation.\n",
    ">>\n",
    ">> $\\lambda_r = 0.01$: number of untouched by mutation sites that were solved at the start of this generation\n",
    "\n",
    "On the other hand, the signature of the four types of micro-fluctuations is:\n",
    "\n",
    " 1. The fluctuation in the number of touched by mutation sites are very negative. \n",
    " 2. The fluctuations in the number of touched and solved sites are equilibrated, positive and negative fluctuations play a similar role in the plateau. In the ramp are positive. \n",
    " 3. The fluctuation in the number of untouched by mutation sites are positive except at the beginning.\n",
    " 4. The fluctuations in the number of untouched and solved sites are very positive. We have super powerful positive fluctuations. \n",
    "\n",
    " From this, we infer that \n",
    " 1. The creation of a population always produces positive fluctuations that are an important pivot for Evolution with a weight around 0.4 that is given by $\\lambda_k \\in [0.38, 0.43]$.\n",
    " 2. Evolution gives to very positive fluctuations in the number of untouched and solved sites a low weight that is given by $\\lambda_r = 0.01$.  We consider that this means that very positive fluctuations  in this observable are very important but rarely happen. \n",
    " 3. Fluctuations in the number of touched sites have varying weight given by $\\lambda_T \\in [0.29, 0.40]$. We consider that its weight is a round 0.35, which is relatively high. \n",
    " 4. Fluctuations in the number of solved sites among those touched by mutation are commonly used, with a weight of $\\lambda_s = 0.7$. \n",
    "\n",
    "**Challenge.** The previous program needs too much memory. Even when all other apps are turned off, some 25 GB are needed on a desktop. Do something to keep memory needs into usual bounds, say, less than 16 GB for the overall system. "
   ]
  },
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   "cell_type": "markdown",
   "id": "0b4ed96d",
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   "source": [
    "# GRADUATION\n",
    "\n",
    "Choose one among the following projects:\n",
    "\n",
    "1. Use the Underdog Model to make extrapolations about the needed time to solve a task with a complexity of the order of the simplest microorganism, half a million of DNA basis. \n",
    "2. Our Underdog Model is based on the mathematical theory of MAX. This model needs the external specification of four parameters. A preliminary work was made by hand and its output given as a hint to a GA, which successfully improved high quality. That is why we claim that the Underdog Model is the natural way of understanding Evolution. Prove that Evolution can also successfully specify those parameters if no hint is given, i.e., if the initial population is synthesized by randomness alone. \n",
    "3. Make an incursion into the world of variable parameters for the Underdog Model. So, pay attention to the dependency of parameters on the population size because for some of its values, the fitting is much better than for others.\n",
    "4. Carry out a severe statistical analysis to disentangle the role of recombination. Up to this moment, we consider that in regard with the acquisition of function by simple steps, the role of recombination is insignificant. More exactly, its role is plainly obscured by the uncertainty in the estimation of the optimal values of the parameters of the Underdog Model. We know this because the simulations in the present contribution include recombination while the Underdog Model relies on mutation without recombination and predictions agree very well with observations. Recombination of complete genes is nevertheless a very important phenomenon in actual living beings beginning with bacteria and every kind of infection by viruses. \n",
    "5. We have proved that perfection has an exponential cost in terms of population size. This is a plain rejection of the Evolutionary Theory. This assertion is nevertheless very abstract. Work in the development of simulations that allow lay people to grasp what is an imperfection and why these must be necessarily found everywhere in the fossil record as well as in our anatomy and physiology. \n",
    "6. Perfection has an exponential cost that must be paid in population size. But Evolution is a dynamical phenomenon that gathers small progress over generations, over time. We have not specified the relation of parallel processing, given by population size, and serial processing, given by the generations of the Evolutionary Process. We abusively have used an Ergodic Postulate that says that parallel and serial processing can be interchanged one with the other without affecting the complexity of Evolution, which belongs in the exponential class. Challenge this, i.e., find the correct relation and make the corresponding amendments, if any (we think, none).\n",
    "7. We observed that good fitting of the simulations by the Underdog Model could be reached with diverse assignation to the values of its parameters. We thought that this was due to a small sample size for the tests. So, we took in the last programs a large sample with diverse values of population size and complexity. Now, we tend to belief that the optimal values are given by one single assignation. Dare this belief. For instance, set $\\lambda_T = 0$ and try to prove that the other three parameters can be adjusted to get a good fitting. Do you consider that your results attack the Underdog Model because it is loosely restricted? \n",
    "8. We tend to firmly believe that the relationship between resources and complexity determines the silhouette of the Evolutionary Process and all its observables. In other words, there must be a scalable relationship between resources and complexity. Turn this diffuse appreciation into a scientific, quantitative, and systematically reproducible assertion.\n",
    "9. We have said that high fluctuations in the number of sites that were touched and solved by mutation have a low participation in the overall fitness of the best of each generation. This is contra intuitive because the Evolutionary Theory says precisely that the selection of the best is the fuel of Evolution, thus, we all expect that the corresponding fluctuations would have a powerful participation over the fitness. To reencounter this assertion, ignore the populational variability of touched sites and compute anew the participation of the number of solved sites over the fitness.\n",
    "10. The Underdog ideology says that evolutionary success does not come from high fluctuations. The reason is that a high fluctuation occurs as a result of two high fluctuations in tandem. The first is in the number of sites that are affected by the mutation. The second is in the number of sites that have been touched by a mutation and are resolved by it. Such a match is, of course, low. This statement says that below our model that pays attention to mean values and expected deviation, a counting procedure could yield exact results whose average values might match those of our model. Develop such counting, combinatorial procedures, and try to reproduce and correct the Underdog model. \n",
    "11. Use the Underdog Model to find the optimal value of mutation to increase the Evolutionary rate (a constant parameter that measures the velocity of Evolution to achieve a task). Test your prediction. \n",
    "12. Quantify the variation of fluctuations around the plateau. Demonstrate that when complexity grows, the magnitude of the fluctuations decreases and thus becomes more difficult for them to achieve perfection, which can always be found. Model fluctuations and make predictions about the cost of finding perfection. Does your result challenge our rejection of the Evolutionary Theory? "
   ]
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   "id": "d46f9053",
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    "\n",
    "\n",
    "# CONCLUSION\n",
    "\n",
    "We have studied the battle between Evolution and complexity.\n",
    "\n",
    "**Exercise.** Redact your conclusions of this study.\n",
    ">\n",
    "> *Answer.* Our conclusions of the present work are:\n",
    "\n",
    "1. The Evolutionary Theory says that every species on Earth is a product of Evolution. We have found that this theory is plainly scientific because Evolution has been abstracted as a Genetic Algorithm (GA), whose properties can be studied, and its mandatory conclusions can be extracted. A GA is composed of iterations of assessment of fitness, selection of the fittest ensued by its cloning, mutation and recombination. \n",
    "2. The GAs are effective. In fact, rudimentary Evolution, made by hand, can produce not bad approximations, and its automatic implementation in a computer can sensibly improve high quality in appropriate examples. This is the proof, in the view of Modern Science, that the natural explanation of our existence is Evolution. But, Science is wrong because Evolution also has mandatory predictions that lethally contradict the aforementioned theory. \n",
    "3. The most important mandatory prediction of the Evolutionary Theory is that to fabricate approximate solutions to posed problems, Evolution must pay a cost. To quantify this, we have tested Evolution against the task of matching a binary string of predefined length. Complexity is represented by the length of the input or target string. Evolution uses the population as resource that allows parallel recursive trials to gain quality step by step, serially, until a plateau is reached, usually below perfection. If population size is high enough, perfection can be achieved. The corresponding cost is exponential in the length of the input or length of the target binary string. In practice, this means that Evolution is real and not magic and that it must produce tones of imperfect organisms before reaching any trace of perfection. So, the museums must be filled in every kind of crooked skeletons, and also, malfunctions and malformations must be counted in our bodies by millions. Therefore, each person on Earth can test the Evolutionary Theory by just looking into his or her body and physiology. \n",
    "4. Evolution can be almost perfectly understood thanks to the Underdog Model. This model assumes that the Selection of the Fittest is correctly abstracted by MAX, which takes the best output of random trials. The best is above the mean, so it represents a positive fluctuation. Our mathematical theory of MAX needs the external specification of four parameters corresponding to four processes, in which MAX captures the corresponding fluctuations. We have used Evolution, both rudimentary and automatic-and-full-fledged, to find good values for those parameters. It is astonishing how good is the resultant fitting. Our estimation of parameters confirm our previous insight that say that the Underdog, below the mean value as predicted by MAX, is an important fuel for Evolution.  But since the fitting is not perfect for small population sizes, parameters that depend on the population size and complexity might be considered. \n",
    "\n",
    "\n",
    "## A Parable\n",
    "\n",
    "The Evolutionary 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 Evolutionary 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
}