José Rodríguez

Bogotá, Colombia, 13/XII/2019


The explicit order of the Lord Jesus Christ regarding Evolution is very clear: Render to Evolution the things that are Evolution's and to God the things that are God's. To do this, we can start with common sense that tells us that building a living being should not be easy. That is, complexity is the natural judge of the Evolutionary Theory. So, we start explaining Evolution to next face complexity. Specifically, we will give to simulations of Evolution the task of resolving passwords. To move from our simulations to implications on the Evolutionary Theory we will help ourselves with Molecular Biology.



Evolution is a method that we use at each moment to acquire wisdom, to engineer crafts, to improve services of all types. The biblical version can be read in Prov 11:14. It has various components:

  1. If we dislike something, we change it. This is called mutation.
    Your browser does not support the canvas element.

  2. If we want to improve something, we ask, read, research and make a final product with part of what we knew and part of what we learned. This is called recombination.

    Your browser does not support the canvas element.

  3. Products are evaluated, either in practice or in the mind, to know how well the mutants and recombinants work. The best is selected for further improvement. We call this evaluation, assessment, selection.
  4. When a problem is important, one repeats and repeats this process. We call this recursion or repetition.


To automate Evolution all that is needed is to add to the previous procedure a source of creativity to propose solutions and another one of wisdom to offer advice. But proposing good solutions and giving good advice is very complicated and so, we do not aspire to automate that.

What will we do then?

We will use two qualities that computers have to spare: persistence and the ability to generate random numbers.

The property of random numbers that interests us is that one cannot predict which number will come after which. Therefore, we consider random numbers to be supremely creative. When we add such creativity to persistence we hope to get great things.

Is that true?

The smart way to know it is to test the proposed automation. So, we give Evolution the task of solving a game.

2.1 Game with dice

In the GUESS GUESSER game one player thinks a number and the other tries to guess it. To understand what it means to automate Evolution, let's play a variant of this game in which the one who guesses is a dice that has neither common sense nor intuition. So that the game could be played as a family with a pair of dice, we restrict ourselves to 2-digit numbers, each digit with values ​​from 1 to 6.

But attention, let's note that the objective of the game is to guess a gradualist key. In this type of key, any match adds points. For example, if the key is 1234 and one has 3245, one already carries a point because one has guessed 2. And if one has 5214, one won 2 points. That way one can accumulate point after point and improve the solution.

Attached to this document are some JavaScript programs that can be run on the cell phone just by clicking on them. The JavaScript program that simulates the Guess-Guesser game is:


The output of the program is very clear, but some question may arise. In that case, we recommend reading the


It can eventually help.

2.2 Genetic algorithms

The previous program is the simplest example of what is called genetic algorithms or simulations of Evolution to solve specific problems. In these algorithms, a universe is constructed within the memory of a computer, which gets populated by organisms that can evolve, with recombination, mutation, selection, and repetition. Reproduction is due to merits according to how well the proposed problem is solved. In modern science and technology, genetic algorithms are routine.

Well, together with thousands and thousands of scientists and engineers, we have also witnessed that Evolution is a useful tool for solving gradual problems, which allow improvements to be added to improvements. The Evolutionary method is so sure that it is not right to call guessing the work that Evolution does. It would be better to say the Evolution finds the solution infallibly.

Our success forces us to face a claim that is based on something we all know: with enough attempts, one can also guess a key by chance, without memorizing what one did or did not do, without mutation and recombination, and without worrying about evaluating to select the best one. For keys of few numbers, chance is so infallible that it is an endless source of big businesses, such as raffles, lotteries, and their innumerable variants. All this means that we have the task of contrasting the performance of Evolution with that of chance to see who wins.

2.3 Is evolution better than randomness?

Evolution has to be much better than chance and not only better because programming Evolution is much more difficult than programming chance. We hope that Evolution is much better than chance because the former can build the solution little by little but, by contrast, chance can only guess the whole key else nothing. To elucidate if what is expected of Evolution is correct, we run a program that finds increasingly long gradualist keys by two methods: by Evolution and by chance. So we can compare them. The program is:

ChanceEvoP.html. Run this program with the option GRADUALISTIC.


The previous program has shown us that chance is better than Evolution for short gradualist keys but that Evolution is ordinarily hundreds of times more efficient than chance for long gradualist keys. In general, chance cannot compete with Evolution to solve gradual problems because Evolution can improve the solution to this type of problems by gathering small chance (this expression was patented by Richard Dawkins in honor to Darwin).

Now let's move to the biological implications.


To move from our simulations to Biology we need to argue that Biology has the necessary elements for Evolution. Indeed:

Genetics and Molecular Biology have shown that the genetic information we carry in our DNA suffers mutations and recombinations, both at random. This represents a line of work inaugurated by Gregor Mendel around 1850 who was the first who allowed us to think that inheritance is due to discrete elements that we now call genes and that we later discovered were texts in DNA strands.

We usually see the mutation in action when someone is born with a defect that did not exist in the family clan. For example, with a viperine lip, or with crooked feet. We can infer that the mutation is unstoppable by noticing that the spots of the calf that was cloned are different from those of the mother cow. More familiarly, paternity tests show that the father's DNA is not exactly copied to the child and so, mutation works non-stop.

As for recombination, let us consider that the genetic information is distributed in chromosomes just like an encyclopedia comes in several volumes and different versions. We could think of recombining the versions. But not only between volumes but also among chapters, pages and parts of pages. That biological recombination is something so sophisticated was demonstrated on bacteria that had a single chromosome and that is why it is considered that recombination within the same chromosome is the essential healing mechanism against deleterious mutations. The proof was as follows: natural bacteria could digest both substances A and B. Bacteria were irradiated with X-rays and from there came out defective mutants. Some were filtered that could digest substance A but not B and others that could digest substance B but not A. The two types of mutants were placed on the same culture agar. And from there came bacteria that could digest both substances. How is it possible? It could be because of recombination but, what a problem: How would you exclude mutation as a cause of genetic healing? Because it could be that a semi-functional bacterium mutated ending up digesting both A and B.

Since living beings have mutation and recombination, a question immediately arises: what about selection and recursion to get a full-fledged evolutionary mechanism? In regard to recursion, it is simply a derivative of our ability to reproduce and reproduce again. And, selection? What is the matter with selection?


When one looks into each other's eyes with a dog, one realizes that we are both brothers. Of course, that is not free because the dog loves you unconditionally. But all that would not be possible if we did not have many, many biological things in common. Therefore, and knowing that mutation, recombination, and recursion work both in the dog and in us, we have a sacred duty to reinvent the wheel, that is, to propose the Evolutionary Theory:

The man and the dog are biological brothers, that is to say, they have arisen by Evolution from a common ancestor.

Of course, we have a gap because we are missing assessment and selection of the best. Well, that gap is filled with the ideas of Charles Darwin who published them in the mid-nineteenth century. These were supported by fieldwork, particularly in observations of the Evolution of the bird's beak on the Galapagos Island, in Ecuador.

The mechanism that assesses and chooses the best was called by Darwin natural selection, which represents a very simple phenomenon. In an updated version it says: the members of a population are all different and to such an extent that some have better ability to survive and leave more children than others. When such ability is associated with specific genes or groups of genes, these genes tend to fill the world. And so the species are perfected, for which recombination is very appropriate.


We have a very sensible theory that challenges the personal beliefs of many people as well as mine. Indeed, according to my religion, we were created from the dust of the earth. Having been created gives the Creator the authority to judge us and to put in the lake of fire everyone who enjoys oppressing his wife or his employee. But if it is demonstrated that Evolution is a sufficient explanation of our existence, I adhere to the opinion that God is redundant. Therefore, it is worth spending time studying the theory. Well, there are two major fronts of work: The first front seems safe to us: so many human beings that we are and all so physically different is something that we would like to be explained by Evolution. Therefore, we believe and teach that Evolutionary Theory has a non-zero validity range and that it is an essential part of us humans.

We have the problem of establishing whether or not the Evolutionary Theory has an absolute validity range.

If what we like is an affirmative answer, we would favor the belief that all species and races come from a common ancestor. Every scientist believes not only in that but also in that the common ancestor appeared partly by chance and partly by self-organization. If that is true, I am in favor of believing that it implies that God has nothing to do with us.


To understand what self-organization means, do the following experiment: in a pot, firstly pour beans (or peas or chickpeas) and then toss some rice gently on top of it. Notice that the rice remains on top of the beans. Now with your fingers randomly stir the beans with the rice. When curiosity dominates you, stop and see where the rice grains are: Are they perfectly mixed with the beans? Repeat your exercise until reaching the following conclusion: chance has managed to change the original order and imposes a natural order that will always be repeated. We say that the final order was obtained by self-organization. That is nothing new to anyone because we all know sayings like the following: God raises them and they come together . It is a social example of self-organization.

On the other hand, to say that the Evolutionary Theory is false is to pretend that at least two species are not connected to a common ancestor. In general, everyone who likes the idea that the Evolutionary Theory is false also suffers because of the assertion that man and monkey have a common ancestor connected by Evolutionary changes. What we dislike is to despise the difference of intelligence of the monkey with that of the man: the change is explained by Evolution when we humans have had so much work doing the computers and much more putting them to imitate the human thought. However, it is necessary to try to understand Evolutionists.

5.1 An illustration of the evolutionary thought

The Evolution of the monkey or one of its ancestors, into the man, is for an Evolutionist something that does not even have to be argued because for him or her that is something obvious, as perhaps it is insinuated by the attached program that embodies the gradualist vision of the Evolution. The program presents two hands, that of the chimpanzee and that of the human, and demonstrates how easy and inspiring it is to transform the first into the second:


With stories like the one presented by the previous simulation, the Evolutionary Theory becomes something so obvious that questioning it may seem stupid. Actually, questioning it is an obligation. In particular, the previous program is a misinterpretation of the Evolutionary Theory, because the program is reduced to showing changes in appearances, the phenotype. This is wrong because the modern Evolutionary Theory aims to show that the Evolution of appearances can be explained by the Evolution of the corresponding genetic information, the genotype. What happens is that the genotype has the instructions to build the phenotype. So the genotype is a recipe for building the phenotype, and it is therefore an algorithm. Hence, the appropriate theoretical environment for the study of Evolutionary Theory is that of Genetic Programming, a part of Computer Science that studies how to use Evolution for the development of computer programs. This subject is so difficult that it is not even a profession but is a matter of purely academic research. For now, let's listen to someone else.


To elucidate whether or not the Evolutionary Theory is a plausible or maybe solid explanation of our existence, we have a very simple directive: less wordiness and more simulations.

But attention, we cannot make simulations of the first idea that comes to mind. We need a guide, an orientation. Where do we take it from? Of our instinct. Of our intuition. And what does our instinct tell us?

Our instinct tells us that Evolutionary Theory is obviously false.

And what arguments does our instinct present?


We have a serious problem that we can attack if we wonder where intuition is born. Surely it has genetic components but also others learned. We are interested in those learned. Therefore we ask ourselves: Is there anything we have learned that flatly rejects the Evolutionary Theory?

Yes. Yes. There is something and it is the daily experience. Every day we verify that wonders, such as our body, are difficult to build. But, what does this mean? What does it mean to be difficult to do? A problem is operationally difficult when we get stuck in unsatisfactory, mediocre solutions. If we achieve an improvement, it is very partial and we don't see how to progress. We call all that complexity.

We all experience great difficulty with complexity. Is that because of us, because we are not very intelligent nor do we value the experience of others? What will Evolution say about our struggles? To find it out, let's apply our directive: Less verbiage and more simulations.

But how?

We need to choose a simulation of something difficult. If Evolution suffers to get done, that is, if it stagnates in rudimentary solutions, we have an immediate and ubiquitous prediction: the fossil record must show rudimentary organisms and also in our own body there must be many things whose Evolution is stagnant in mediocre solutions.

Is that true?

We have already verified that Evolution is very good for solving gradual problems for which matching each part gives a good point. Let's look now how it behaves with a non-gradualist problem. The one that seems most at hand is the problem of guessing a password, which is guessed in its entirety else nothing is guessed. This problem is our epitome of complexity. It is so difficult for us that it is used to safeguard one of the things we love most: money. The program that we propose below gives Evolution a binary key, for example 1000111, and if Evolution finds it, the program proposes another one at random but not with 7 figures but with 8. And so on for a long time. Officially: we give Evolution a binary key with k bits, when it resolves it, we put one with k + 1. The program compares Evolution with chance to see if Evolution is better. It is the same we did with the gradualist keys when we concluded that Evolution was the best.

We use here for passwords the same simulation of Evolution that we used for gradualist keys because we must make sure that the changes are as minimal as possible. The program is:

ChanceEvoP.html. Run it with the ALL_ELSE_NOTHING option.

What conclusion do we draw?

Our conclusion is: Evolution is no better than chance to guess passwords. And both are useless to solve long passwords.

Our test has been purely academic. Can we extrapolate the conclusion to Biology? Can we say that Biological Evolution should have solved the problem of guessing a great password to reach us? To attack this important question, some theory would help us:

6.1 Passwords and Evolution

Genetic information consists of instructions to continue life. Some of them work together to do a great task. For example, some instructions allow you to digest food and others allow you to breathe. As there is not a single mammal that only digests food and does not breathe, nor does another one that breathes and does not digest food, these two functions work together to form a living being. By diving into this theme, what one finds is a set of instructions that work in a highly coordinated group but with the possibility of having variability within some sub-functions. For example, untrained humans have to breathe once every minute as a minimum but cachalot can last an hour and a half underwater. Our discussion allows us to postulate the following global picture:

Living beings contain a large number of passwords (group work, all or nothing) immersed in a large set of gradualist keys (sub-functions that may have too much variability).

We have now a theory about the usual complexity in biology. It seems reasonable. Let's show now that it is mandatory.

Molecular Biology has nothing special about predicting passwords everywhere: large and numerous passwords necessarily arise when the problem of coding information is involved. To prove this we present the following argument consisting of three points:

  1. A password is a sequence of symbols that triggers a function such that apart from it no other is useful because a password is completely matched else you have nothing. Let us reformulate this definition in Evolutionary terms: a password is a sequence that serves to execute a function such that any mutation makes it unusable. The way we like to say it is like this: a password is a sequence that makes sense, which is understood by the system to which it belongs, and which is surrounded by a sea of ​​nonsense as any other mutant sequence does not trigger the function, it is not understood by the system. In short: a password is a meaningful sequence surrounded by all sides by a sea of ​​nonsense.
  2. There is a very large number, too large, of possible sequences but a comparatively small of informative, with meaning. For example, a modern language is one million words maximum 20 letters long, not including compound words that can be very, very long, as in the Turkish language. But the number of possible sequences with 20 letters or less in length exceeds 27 20 which is larger than 10 20 . That is, the proportion of meaningful sequences is less than 1 x 10 -14 .
  3. The more informative a sequence is, the less autocorrelation it has. That is what information means: what we already know is useless to predict what they will tell us later. To say that the informative sequences have zero autocorrelation is the same as saying that they come at random. And since they come at random, they are automatically distanced one from each other almost everywhere.

The first point is a characterization of being a password: a sequence to be a password needs to make sense but be surrounded by a sea of ​​nonsense. The second point assures us that for information systems, with limited memory and computing capacity, the sequence set is a sea of ​​nonsense. And the third one dictates that in any language or information system each highly informative sequence is a password because it makes sense and because it is surrounded by a sea of ​​nonsense.

The third point says that highly packed information looks like a random sequence. It is difficult to accept this as it might seem contrary to common sense. The reason is that chance could be a synonym of not making sense and therefore of not having information. In reality, the concepts of chance and information only make sense within an information management system. Within such a system, a sequence is random when the system cannot interpret it, when it has low autocorrelation, and has no bias or tendency. On the other hand, a sequence is highly informative when it can be interpreted and executed, it does not have autocorrelation, so that it is not redundant, and it has biases locally but that do not extend on a large scale since they would end up being redundant.

The relation of information to chance is so important that we should attempt to familiarize ourselves with it. To do this, let's use a high-quality autocorrelation and bias detector with which almost everyone is born: the ear. Even babies can move the head coherently with a rhythm when they detect it. A rhythmical pattern repeats itself, that is, it has high autocorrelation. And we can all perceive changes in rhythm, expression, tone or altitude, which represent local biases or tendencies. So in the following program, we present two melodies: the first is the music of a natural DNA and the second corresponds to an artificial and random DNA. If you consider that the two musical pieces obey the same musical pattern, you would be detecting that the natural DNA sounds the same as randomness, with low autocorrelation and low bias. If you are so well gifted that you can detect fine differences, you have to strive to be able to decide if they are too big to force us to conclude that DNA and chance are different, or if, rather, natural DNA is quite similar to chance and therefore very informative. The program is:


Our assessment is as follows: for several moments the natural DNA seems more random than the artificial DNA synthesized at random. But often it is the other way around. Our general conclusion is that DNA texts are highly informative but definitely, they have not zero autocorrelation.

A local look at the natural DNA must be complemented by a global vision that produces observations such as the following: the genetic information of cotton comes by tetraplicate and that of some types of strawberries comes in ten copies. Its official term is polyploidy. Of course, this could not be an example of a text with highly compressed information. And in general, it would be very strange if any real system faithfully conforms to the extreme claim that every sequence is a password. We see that we need more experimentation.

To enrich our discussion, let us investigate the behavior of our mother language, which served us as a laboratory to learn and experiment with information management.

6.2 Our mother language as a lab

Does our mother language have a sea of ​​nonsense that fills everything? Does each informative sequence, each phrase, lose its meaning in the face of any mutation? To elucidate it, we can play with the following program starting with any phrase, subjecting it to mutation and watching whether or not it loses its meaning as it mutates:



We see that some random mutations convert any meaningful phrase into nonsense. We can repeat that in many ways and it is always the same. But beware, sometimes a mutant phrase appears with a perfect and even poetic sense. We summarize:

In our mother language, there is a sea of ​​nonsense that fills everything but passwords are not isolated phrases but islets. These contain the original phrase with meaning and some mutants around that have, in general, a somewhat impaired meaning that deteriorates more and more sideways until they finally become meaningless. This situation explains some well known facts:

Sequences with meaning require the creation of dictionaries and spell checkers. But since the informational sequences are somewhat tolerant to mutation, it is possible to create intelligent spell checkers that propose suggestions to restore meaning. On the other hand, every human language has a grammar that is an inexhaustible source of correlations and that allows the existence of automatic translators. It is impossible to formalize a grammar of a real language, but the Language Academy designs a more or less formal grammar and expects it to be obeyed. As that does not happen, there will always be human translators who will aspire to produce professional translations.

We have now a simple and realistic image of the sea of ​​nonsense in linguistics. Now, what language would we be talking about in the case of Molecular Biology? It is the language of life: a DNA sequence makes sense when being expressed and executed increases the chances of survival and reproduction but also compensates for the cost of its production. What would be a meaningless sequence? It is one that is of no use, representing a cost for survival, or that causes harm when expressed and executed, or that is lethal and causes death either because the lost function was essential for life or because the new one creates a killing poison.

And the passwords? Do we already have the prediction that there must be passwords in Molecular Biology, the language of life, and that they must also be extensive and ubiquitous, filling every place?

No, not yet.

To deduce the existence of passwords and to describe their geography we need to support two points. First: that the possible DNA sequences are too many compared to those present in living beings, which implies a sea of ​​nonsense. Second: estimation of the degree of global autocorrelation of the DNA sequences of living beings. This would allow us to decide whether meaningful sequences form large continents where Evolution is mandatory, or if they form archipelagos, where Evolution is plausible, or if they form very isolated islets where species are not Evolutionarily connected, or what.

The first point is straightforward: we have less than 1 billion species and instead, the possible sequences are counted by numbers that, because they are so large, do not even have names. Therefore, there is a sea of ​​nonsense in Molecular Biology.

Here we have a strong experimental objection to our reasoning: we are assuming that the only meaningful sequences are those that are present in today's living beings. Nothing is farther from the truth: there are too many extinct species and now there are already non-standard life projects that explore and produce new life forms that break the generic schemes of Biology. For example, thanks to Evolution, new types of bacteria have been created that have genetic codes of 4 letters instead of 3 which is the one that has the ordinary life, you and me.

How will we solve this objection? We will not solve it because it is a problem that is solved by itself: ​​we have assumed that there are less than 1000 million species, but we can change this number for a reasonable one, any. However large, it will remain much smaller than the number of possible DNA sequences and / or polypeptides. So, the sea of ​​nonsense in Biology is firm. Our disinterest in the objection is based on the fact that the number of sequences is exponential in the length of the sequence and that these functions grow extraordinarily fast. Nothing similar can be said about the number of different species. However, don't believe in me but cultivate your own opinions. But learn from me that I test many of my beliefs.

As for the geography of the archipelago of meaningful sequences we expect very isolated islets. This is because life is full of essential functions. To see it, let's refer to enzymes that are encoded within the bustling part of the DNA.

6.3 The enzymes

A cell is a chemical reactor in which at all times spontaneously run all kinds of biochemical reactions but at a very low speed. Enzymes accelerate those reactions, in their type, time, space, speed and regulation required for a wonderful living organism to appear, like you, dear reader, whom I thank very much for your interest in my work. Enzymes are molecules that belong to the class of proteins or polypeptides.

Regarding the relationship between enzymes and passwords, let's deny that connection and see how this assumption has been tested. If we deny that enzymes constitute passwords, that means that there is tolerance to mutation and that meaningful sequences form a continent. If that were true, we could use the Evolutionary Theory to design enzymes to catalyze reactions other than those that natively accelerate. As something to admire to the Academy, this has been put to the test. And, well, what do the experiments say?

For over 50 years, experiments say that with Evolution you can design enzymes, and that is repeated from time to time. But attention, every achievement has represented a great scientific success and the results are published in renowned journals and appear in big headlines everywhere. This is right because it's not easy at all. That is so difficult that the design of enzymes is becoming a profession not for biologists who believe in Evolution but for groups of physicists and systems engineers who do not use Evolution but instead are already maturing their methods of quantum mechanics ab initio to calculate enzymes on request. In clear words: Evolution is useless as a generic tool but geniuses can occasionally put it to work.

The developing picture that we have seen allows us to say that common sense is winning: it is not easy to make a living being, not even something that resembles it. But on the other hand, once one has a living being, there are quite a few ways to make variations that could result in incredible mutants. Rephrasing in terms of passwords, we are saying that the geography of the archipelago of meaningful DNA sequences should be very similar to that of meaningful phrases in any human language in the world. That is, the DNA shall be full of passwords everywhere but with a slight mutation tolerance, enough to allow the appearing of all kinds of races and possibly some species.

Now let's pay attention to some characteristics of enzymes:

Enzymes are specific to substrates. Enzymes are also specific to products. What does this mean? It means the following: A reaction is a process that transforms substrates into products. Being specific with the substrates means that the substrates are always the same for the same enzyme. However, some enzymes work on families of molecules because they all have the same chemical determinant that is the target of the enzyme. The same is valid for the products because the enzyme always transforms the same substrates into the same products (under the same environmental conditions). In our terms: substrates (or their chemical determinants) are passwords that ignite the corresponding enzymes.

Enzymes can be locked by inhibitors. The enzyme accepts them in its processing chamber, because they are very similar to substrates, but when trying to work, it cannot. If the enzyme is part of a critical chain of reactions, there is a problem. For example, for some plants, if their seeds are eaten and digested by the little birds, the plant becomes extinct. In prevention, these seeds can have a trypsin inhibitor, a digestive enzyme, the little bird does not die, but is deterred from eating seeds again.

In hindsight, we have seen that the substrates of an enzymatic reaction are passwords. So what are enzymes? They are password management systems. Indeed: each enzyme must have a representation not only of the substrates but of the reaction path that binds the substrates with the products. This is because the enzyme does not contradict thermodynamics but relies on it and because from the same reactants one can obtain several products, so the enzyme must select one and manufacture an expedited path for it to be synthesized as quickly as necessary. Some enzymes also have acceleration control that gives rise to other passwords.

Besides, all enzymes are one-dimensional chains that have to curl over themselves to form three-dimensional structures that carry the stereo-dynamic information of their functions. The bending happens spontaneously, by self-organization, which is induced by the Brownian motion but is stabilized by internal van der Waals forces among the different amino-acids of the enzyme. All this puts restrictions on restrictions.

Let's take a look at the problem of energy, which is born from the stability of the molecules. In effect, reactants or substrates are stable molecules that must be transformed into products that are also stable. Or, both the substrates and the products are resistant to deformation but the products are more resistant. Officially it is said: there must be an activation barrier that separates the products from the substrates. The enzyme must lower that barrier using, say, geometrical and physic-chemical changes to accelerate the reaction or / and must gather energy from the environment, in the form of local heat fluctuations, to overcome it, or / and must take free energy from specialized molecules, such as ATP, and / or must be recharged for the next reaction using the energy released by the reaction as an enzyme only accelerates thermodynamically viable reactions, that is, that produce more energy than they consume.

So many simultaneous requirements may be incompatible and some enzymes need extra help. For example, to help them in self-curling, some enzymes are assisted by other proteins called chaperones. There is also the problem that the 20 amino acids provided by the genetic code are not sufficient for so many functions and other enzymes are required to modify some of them, as happens in the myosin.

From our point of view, everything said about enzymes can be succinctly expressed as follows: enzymes are extremely informative structures. Or, geographically: the DNA sequences that code for enzymes make up very isolated islets in the infinite sea of ​​nonsense. Reformulating in Evolutionary terms we are saying: Evolution is a wonderful thing but it is not responsible for the origin of the enzymes that make the species exist.

Our delicious rhetoric must be tested, rigorously tested. But how? Formulating a prediction that must be mandatory, massively obligatory either in the world of protein sequences or in DNA.


Our prediction should combine the possibilities of Evolution with the specificity of the many restrictions. That Evolution creates enzymatic variability is something we should expect: amino acids, the elementary blocks that makeup enzymes and proteins, are different from each other although not entirely but have similarities to each other. Because of them, Evolution must be able to come and go at least to some degree. But how can we show that Evolution cannot do everything?

When there are no restrictions, the mutation produces new sequences that are viable, that is, that promote survival and reproduction of the carrier. When there are restrictions, the variability decreases. When restrictions stifle Evolution there is only one single sequence that does the job and everything else is deadly, either because they nullify an essential function or because they produce a lethal poison. How do you say this in Molecular Biology? It is done like this: When a sequence has variants, it is called polymorphic . And when you don't have them, it's called monomorphic . With this we can formulate our prediction to be fulfilled on all sides of any genome:

Mandatory, ubiquitous prediction of the Theory Of Complexity that denies the power of Evolution and claims the global, generalized falsification of the Evolutionary Theory:

Any polypeptide sequence of any species must have monomorphic sites. One is enough for each protein or enzyme. By adding them all together and assuming they work in group we have a great password. The other non-monomorphic sites, the polymorphic, correspond to gradualist keys.


Research on molecular polymorphism has a long history because variability is the terrain where Evolution can operate. If there is no polymorphism or variability, there is no Evolution. The good news is that polymorphisms are currently studied regularly on human genomes because they have medical connotations. In effect: Molecular Biology has already managed to make medicine strictly personal. The reason is that small genetic variations give us a great diversity: in personality, in ways of being predisposed to various diseases and disorders, in ways of responding to attacks of microbes, in ways to take advantage of medications. At the root of this whole mess, there is a term called: single nucleotide polymorphisms (SNPs), which indicate places in the DNA in which there are variations.

What do we expect regarding the frequency of SNPs or polymorphic DNA sites? We expect from our discussion that almost everything is polymorphic except for some monomorphic sites scattered everywhere.

What does reality say?

Well, Molecular biology tells us that we are blatantly wrong because the rule is not polymorphism but monomorphism, that is, we have underestimated the effect of so many restrictions. Quantitatively we have:

Current reports (2019) speak of 1 or 2 or 3 polymorphic sites per 1000 but as more population studies are done the number could grow and that is why we prefer to put 10 per 1000 as a possible maximum. Of course, this would be on average because there are areas of the genome where the function is tolerable to mutation. Moreover, it can even happen that the function of some region is precisely that of being highly variable, for example in the sequences that encode for the variable parts of immunoglobulins. These molecules are in charge of defending ourselves from whatever pathogen there may be and that is why our defences must be very variable because the pathogens are too surprising due to their very high variability created, naturally, by Evolution.

So, there are less than 10 SNPs for every thousand letters (officially it reads: 10 or less for every thousand base pairs since the DNA is double-stranded). In other words, it is not strange to find segments 100 base pairs long without finding a single site that allows variation. In short: the human genome is full of completely monomorphic sequences nearly 100 bases long.

Now let's reformulate the aforementioned result of Molecular Biology in our terms: Since our DNA has about 3000 million base pairs, our genome would consist of a series of 30 million passwords, each 100 bases long.

If one says that all those passwords form a large one, one would be in the most unfavorable scenario for Evolution: The Evolutionary Theory would be a utopia, something contrary to reality. But if one says that all of them form a gradualist key and that they can be guessed separately, we are giving Evolution the best possible scenario, which is a theory by itself.


To formulate our version of gradualism we take each monomorphic sub-sequence of 100 letters as if it were a letter of a larger alphabet. How many symbols does this alphabet have? Given that the DNA has 4 letters, such an alphabet would then have a number of symbols equal to 4 100 = 10 60 . With this we can make our formulation:

The Gradualistic Evolutionary Theory:

  1. The human genome consists of a large gradualist key of 30 million letters in length over an alphabet that has 10 60 symbols.
  2. Since Evolution is too good to solve gradualistic keys, the Evolution of complexity from the rudimentary to the excel was so rapid that it did not leave traces in the fossil record nor were left unresolved problems that we could observe in our own body. The same can be said of all other species.
  3. The diversity of species is due to chance and to the coupling of Evolution to the environment, which produces adaptations to ambient conditions plus other species.

The power of this theory is rooted on the fact that Evolution is much better than randomness to attack gradualistic problems. Another fold of this truth is shown by the following program:


This program shows that randomness is better than Evolution for simple problems but that Evolution is better when complexity increases. As a comparative descriptor, we use the percentage of the saved complexity. What is this? The saved complexity is the subtraction of the trials made by Evolution from those made by chance. If we divide this by the trials of chance and multiply by 100 we find the saved complexity percentage. For low complexity, this percentage is negative that shows how randomness outperforms Evolution. But Evolution becomes the excel winner as complexity increases. This is what matters. More details in the documentation:


And, ¿what about the falsification of this theory given that Gradualistic Evolution is so wonderful?

In our opinion, the clearest and sharpest falsification of this theory is shown by the following program :



Our interpretation of the output of this program is as follows:
  1. Randomness always generates incipient functions from which Evolution might begin.
  2. Rudimentary forms of life might be absent from the fossil record because Evolution runs very fast at that stage.
  3. It is true that Gradualistic Evolution builds perfection step by step but the more advanced is the Evolution the more distant get the steps.
  4. Therefore, there must be under Gradualism an observable and mandatory Evolution of perfection in advanced stages. For example, respiration is an energy extraction process. These processes have an efficiency that can range from 0 to a limit of around 50% for very sophisticated machinery that is only in living beings. This gradation assures us that if respiration appeared by Evolution, there must have been an Evolution of perfection, from random solutions, through very rudimentary solutions with 3% efficiency, to continue climbing to mediocre solutions (which make the half, 25%), then good, very good, excellent and wonderful (50%). But, also, as our DNA is so extensive, the Evolution of perfection would still be ongoing and should be seen in our own body and in that of every living being: we should all be full in malformations and dysfunctions.
I do not see in me, nor in the dogs of my city, the mandatory Evolution of high perfection. Therefore, I cannot find a single reason to think that Evolution may have been the cause of my existence. Not even one. Evolution is nevertheless a wonderful property of life that is always at work amidst us. On the other hand, my intuition tells me that I exist because I was created by God and my personal long experience has reinforced my conviction that Jesus is the lamb of God that takes away the sin of the world. I declare myself happy with this. Very happy.


We have shown extensively that Evolution is no longer a dogma but an incredible subject of study. In fact, Evolution is supremely good for solving gradual problems, in which the solution can be found by adding improvements to improvements. But Evolution is not better than chance to solve non-gradualist problems, such as guessing passwords, which are matched all or nothing, and both fall short to guess them when they are long and there is little time. That is why, in light of the complexity, the Gradualist Evolutionary Theory, which gives greater opportunities to Evolution, predicts that there is an Evolution of perfection, which should be evident in the fossil record: starting with rudimentary solutions, passing quickly for all kinds of mediocrities, moving towards acceptable solutions, following after long periods of stagnation towards quality implementations and continuing with very long stops towards perfection. But, besides, as our DNA is as extensive as the DNA of any living being, the Evolution of perfection should now be seen in our own body and in that of every living being: we should all be full of malformations and dysfunctions. None of that I see in me, nor in the dogs of the city. So we conclude: Evolution is a magnificent property of life but it does not serve at all to explain the general origin of the species. Therefore, whoever recognizes God the Father as Creator and has fear of Him, praying routinely for his enemies, has all my support and is my joy. With this, we have shown how to obey the command of the Lord Jesus Christ to rendering to Evolution the things that are Evolution's and to God the things that are God's.


  1. You might enjoy

    an understandable introduction to molecular biology

    where you can read about many things, in particular over


  2. To dive into the theme: Interview with EvolJava. This is necessary because, for example, this document is weak in the face of the following objection: everything that is said here is relevant in terms of applied Evolution, such as strain improvement, but has nothing to do with natural Evolution. The reason is that improvement poses an objective but according to Darwinism nature not.
  3. If you want to be an expert, someone who can turn his or her silly questions into great research programs, here you can find a good material to learn to program Evolution in Java, which is quite friendly and very good for research.
  4. You can read and examine the attached JavaScript programs by opening them with any text editor. They can even be edited, changing, for example, some parameter, saving it and running it again. Such a task could be easier if a specialized editor such as DroidEdit is installed. You can also run these programs in a desktop.
  5. The best way to know more is to work. So, let me offer you a good exercise: prove else refute the following assertion:
    As randomness is useless to match long gradualistic keys, so is Gradualistic Evolution.


We present here some explanations that eventually could help to understand the output of programs.

12.1 Simulation of the GUESS-GUESSER game

To understand what it means to automate Evolution, let's play the variant of the GUESS-GUESSER game in which the one who guesses is a dice that has neither common sense nor intuition. To make the game easy, instead of words, numbers with the digits 1 through 6 are used. This is a game that can be played as a family with a pair of dice. Let us begin.

Let's guess the number 56 by throwing a pair of dice and over a population of 6 individuals. The rule for the judge is:

Cold: if the first digit is not 5, nor is the second one 6. Ex: 43 (cold), 21 (cold). Hot: if the first digit is 5 or the second is 6 but not both. Ex: 46 (hot), 51 (hot). Perfect: if the first digit is 5 and the second is 6. Ex: 56 (perfect).

We now generate 6 two-digits numbers by rolling the two dice. The one that falls to my left gives the first figure, and the one on the right, the second.

First attempt, zero or initial generation: 23 (cold), 12 (cold), 34 (cold), 52 (hot), 43 (cold), 24 (cold)

We execute random reproduction by merits: We select the 52 (hot) and being the best, we reproduce it 6 times:

52, 52, 52, 52, 52, 52.

We subject the newly reproduced population to random mutation. We can produce a single mutation for each new individual. To decide which figure we mutate, we roll the dice. If the dice falls odd, we change the figure in the first place. Otherwise we change the second.

Let us start: Let us take the first 52. Roll the dice: fell 3. Since 3 is odd we can change the number on the first site, 5. To know the new value, we roll a dice, fell 2. Therefore, we change 5 to 2 and there is a new individual, 22.
Let's take the second 52. Let's roll a dice that fell 6, as it is even, we mutate the figure in second place. We roll a dice, fell 4. Therefore, there is a new individual, 54. We repeat the same about the other 4 numbers. Upon completion, the 6 new individuals are:

22, 54, 51, 32, 55, 12.

Now we submit the new population (reproduced and mutated) to random recombination. To decide with whom shall recombine the first individual we roll a dice: fell 1: the first individual recombines with itself and gives itself, 22. To find the couple of the second number we roll a dice. Fell 4. Therefore there is recombination between 54 and 32. Recombination gives us 52. And so on with the other 4 numbers of 2 digits. We finish with the following population:

22, 52, 55, 34, 52, 22 which is generation one.

We submit it to trial:

22 (cold), 52 (hot), 55 (hot), 34 (cold), 52 (hot), 22 (cold)

To obtain generation two we repeat the whole process once more:

The individuals to reproduce are the hot ones: 52, 55 and 52, in equal conditions. The 52 is repeated but they are not the same but they are different clones with the same information, they are like twins.

With the numbers 52, 55 and 52 we have to form a new population of 6 individuals. To know which shall be reproduced, we roll a dice. If 1 falls, the first individual becomes part of the new population. If 2 falls, the second. If 3 falls, the third. If neither 1 nor 2 nor 3 falls, we repeat rolling the dice until someone leaves favored. After rolling the dice 11 times, the numbers that served were 1, 2, 2, 2, 2, 3. Therefore, we reproduce the first one once, the second four times and the third once.

The new population is therefore:

52, 55, 55, 55, 55, 52.

Effect of mutation:
52, 54, 55, 65, 56, 12.

Effect of recombination:

55, 56, 55, 66, 52, 14.


55 (hot), 56 (perfect), 55 (hot),
66 (hot), 52 (hot), 14 (cold).
Conclusion: Happy ending, 56 was guessed.

The JavaScript program that simulates the Guess-Guess game is:


Return to main text

12.2 ChanceEvoP.html: Which is better: Evolution or chance? "

The ChanceEvoP.html program contrasts the performance of Evolution with that of chance faced in the task of guessing a key. The cost in trials needed to guess it is compared. When both have guessed the key they are given a longer task. There are two types of selection: gradualistic and all-or-nothing. In the first type, the objective can be divided into sub-objectives that can be found separately. The great objective is achieved by gathering small improvement. In the second type of Evolution, all-or-nothing, the objective is indivisible: either it is guessed in its entirety or not guessed. The output looks as follows:
Password = 011111101111011000000101
Guessed by randomness at trial   = 15367140

Evolution begins job
Type of selection : 
Gradualistic: target is subdivided
and reached by gathering small change.
Guessed by Evolution at trial   = 182420

Password = 011111101111011000000101
of length 24
guessed by chance at trial    15367140
guessed by Evolution at trial 182420
Evolution WAS BETTER 
Cumulated number of Trials by Chance    = 42945439
Cumulated number of Trials by Evolution = 307580

When selection is all or nothing, the only thing that changes is the information that says the type of Evolution.

Return to main text

12.3 NonsenseP.html: the sea of ​​nonsense

The program takes a meaningful phrase, mutates it repeatedly and shows the result. For example:

This is the original phrase
Thsskis the origjnua phrape
Tmsskie vhe keigjeuacphrabk
oqsrkqe vhe kezg euscdhrabk
oqivrqe vhe khzg eusvdhrabk
oqivrqe ude khzg eusvdhrabk

Return to main text

12.4 GradualEvoP.html:

The output of this program can be extended with a printing option in the program code. It comes line by line and is interpreted as follows, for example, for line 20:

  1. An experiment was performed with a 20-bit random binary string.
  2. Randomness was expected to guess the password after 1048576.0 tests.
  3. Evolution found the password with a cost of 12950 tests.
  4. Therefore, evolution was better than chance 69,5111037454425 times.
  5. In the experiments executed with passwords of all lengths from 1 to 20, Evolution won 13 times.
  6. Chance was the winner the other 7 times. It is necessary to note that those 7 times were all at the beginning, of length 1 to 7. So, for very simple problems, chance is better than Evolution, but when complexity increases, Evolution becomes better.
  7. Evolution saved 1035626.0 tests compared to chance. This is the saved complexity : the subtraction of the tests carried out by Evolution of those performed by chance.
  8. The percentage of complexity saved was 98.7649917602539. This is 100 times the complexity saved divided by the number of random tests.
  9. The victorious behavior of Evolution is all the more solid the larger the random key to be guessed.

Return to main text

12.5 EvoOfPerfectionP.html:

In this simulation, Evolution is given the task of guessing a password. The output shows the price paid by Gradualistic Evolution to gather perfection by small improvement. First number: the i-th bit that was guessed. Second number: the cost in trials or individuals that was necessary for guessing all i bits. Evolution is not orderly, the guessed bits for the i-th bit could be different than for the (i+1)-th.

When a problem is gradualistic it can be solved by gathering small improvement but improvements come in jumps separated by large gaps.
EXAMPLE with a password 25 bits long:
7 3 // This means that 7 bits of the password were matched at the third try.
8 183 // This means that 8 bits of the password were matched at the 183rd try.
9 279
10 515
11 1311
13 1722
14 5106
15 87888
16 189402
17 9449420
18 13628531
19 508746303
20 880635473 // Time is over. The password was not guessed.

Return to main text