Evo-Info 3: Evolution is not search

Introduction to Evolutionary Informatics, by Robert J. Marks II, the “Charles Darwin of Intelligent Design”; William A. Dembski, the “Isaac Newton of Information Theory”; and Winston Ewert, the “Charles Ingram of Active Information.” World Scientific, 332 pages.
Classification: Engineering mathematics. Engineering analysis. (TA347)
Subjects: Evolutionary computation. Information technology–Mathematics.

Marks, Dembski, and Ewert open Chapter 3 by stating the central fallacy of evolutionary informatics: “Evolution is often modeled by as [sic] a search process.” The long and the short of it is that they do not understand the models, and consequently mistake what a modeler does for what an engineer might do when searching for a solution to a given problem. What I hope to convey in this post, primarily by means of graphics, is that fine-tuning a model of evolution, and thereby obtaining an evolutionary process in which a maximally fit individual emerges rapidly, is nothing like informing evolution to search for the best solution to a problem. We consider, specifically, a simulation model presented by Christian apologist David Glass in a paper challenging evolutionary gradualism à la Dawkins. The behavior on exhibit below is qualitatively similar to that of various biological models of evolution.

Animation 1. Parental populations in the first 2000 generations of a run of the Glass model, with parameters (mutation rate .005, population size 500) tuned to speed the first occurrence of maximum fitness (1857 generations, on average), are shown in orange. Offspring are generated in pairs by recombination and mutation of heritable traits of randomly mated parents. The fitness of an individual in the parental population is, loosely, the number of pairs of offspring it is expected to leave. In each generation, the parental population is replaced by surviving offspring. Which of the offspring die is arbitrary. When the model is modified to begin with a maximally fit population, the long-term regime of the resulting process (blue) is the same as for the original process. Rather than seek out maximum fitness, the two evolutionary processes settle into statistical equilibrium.

Figure 1. The two bar charts, orange (Glass model) and blue (modified Glass model), are the mean frequencies of fitnesses in the parental populations of the 998,000 generations following the 2,000 shown in Animation 1. The mean frequency distributions approximate the equilibrium distribution to which the evolutionary processes converge. In both cases, the mean and standard deviation of the fitnesses are 39.5 and 2.84, respectively, and the average frequency of fitness 50 is 0.0034. Maximum fitness occurs in only 1 of 295 generations, on average.

I should explain immediately that an individual organism is characterized by 50 heritable traits. For each trait, there are several variants. Some variants contribute 1 to the average number offspring pairs left by individuals possessing them, and other variants contribute 0. The expected number of offspring pairs, or fitness, for an individual in the parental population is roughly the sum of the 0-1 contributions of its 50 traits. That is, fitness ranges from 0 to 50. It is irrelevant to the model what the traits and their variants actually are. In other words, there is no target type of organism specified independently of the evolutionary process. Note the circularity in saying that evolution searches for heritable traits that contribute to the propensity to leave offspring, whatever those traits might be.

The two evolutionary processes displayed above are identical, apart from their initial populations, and are statistically equivalent over the long term. Thus a general account of what occurs in one of them must apply to both of them. Surely you are not going to tell me that a search for the “target” of maximum fitness, when placed smack dab on the target, rushes away from the target, and subsequently finds it once in a blue moon. Hopefully you will allow that the occurrence of maximum fitness in an evolutionary process is an event of interest to us, not an event that evolution seeks to produce. Again, fitness is not the purpose of evolution, but instead the propensity of a type of organism to leave offspring. So why is it that, when the population is initially full of maximally fit individuals, the population does not stay that way indefinitely? In each generation, the parental population is replaced with surviving offspring, some of which are different in type (heritable traits) from their parents. The variety in offspring is due to recombination and mutation of parental traits. Even as the failure of parents to leave perfect copies of themselves contributes to the decrease of fitness in the blue process, it contributes also to the increase of fitness in the orange process.

Both of the evolutionary processes in Animation 1 settle into statistical equilibrium. That is, the effects of factors like differential reproduction and mutation on the frequencies of fitnesses in the population gradually come into balance. As the number of generations goes to infinity, the average frequencies of fitnesses cease to change (see “Wright, Fisher, and the Weasel,” by Joe Felsenstein). More precisely, the evolutionary processes converge to an equilibrium distribution, shown in Figure 1. This does not mean that the processes enter a state in which the frequencies of fitnesses in the population stay the same from one generation to the next. The equilibrium distribution is the underlying change­less­ness in a ceaselessly changing population. It is what your eyes would make of the flicker if I were to increase the frame rate of the animation, and show you a million generations in a minute.

Animation 2. As the mutation rate increases, the equilibrium distribution shifts from right to left, which is to say that the long-term mean fitness of the parental population decreases. The variance of the fitnesses (spread of the equilibrium distribution) increases until the mean reaches an intermediate value, and then decreases. Note that the fine-tuned mutation rate .005 ≈ 10–2.3 in Figure 1.

Let’s forget about the blue process now, and consider how the orange (randomly initialized) process settles into statistical equilibrium, moving from left to right in Animation 1. The mutation rate determines

  1. the location and the spread of the equilibrium distribution, and also
  2. the speed of convergence to the equilibrium distribution.

Animation 2 makes the first point clear. In visual terms, an effect of increasing the mutation rate is to move equilibrium distribution from right to left, placing it closer to the distribution of the initial population. The second point is intuitive: the closer the equilibrium distribution is to the frequency distribution of the initial population, the faster the evolutionary process “gets there.” Not only does the evolutionary process have “less far to go” to reach equilibrium, when the mutation rate is higher, but the frequency distribution of fitnesses changes faster. Animation 3 allows you to see the differences in rate of convergence to the equilibrium distribution for evolutionary processes with different mutation rates.

Animation 3. Shown are runs of the Glass model with mutation rate we have focused upon, .005, doubled and halved. That is,  = 2 ⨉ .005 = .01 for the blue process, and  = 1/2 ⨉ .005 = .0025 for the orange process.

An increase in mutation rate speeds convergence to the equilibrium distribution, and reduces the mean frequency of maximum fitness.

I have selected a mutation rate that strikes an optimal balance between the time it takes for the evolutionary process to settle into equilibrium, and the time it takes for maximum fitness to occur when the process is at (or near) equilibrium. With the mutation rate set to .005, the average wait for the first occurrence of maximum fitness, in 1001 runs of the Glass model, is 1857 generations. Over the long term, maximum fitness occurs in about 1 of 295 generations. Although it’s not entirely accurate, it’s not too terribly wrong to think in terms of waiting an average of 1562 generations for the evolutionary process to reach equilibrium, and then waiting an average of 295 generations for a maximally fit individual to emerge. Increasing the mutation rate will decrease the first wait, but the decrease will be more than offset by an increase in the second wait.

Figure 2. Regarding Glass’s algorithm (“Parameter Dependence in Cumulative Selection,” Section 3) as a problem solver, the optimal mutation rate is inversely related to the squared string length (compare to his Figure 3). We focus on the case of string length (number of heritable traits) L = 50, population size N = 500, and mutation rate  = .005, with scaled mutation rate uʹ L2 = 12.5 ≈ 23.64. The actual rate of mutation, commonly denoted u, is 26/27 times the rate reported by Glass. Note that each point on a curve corresponds to an evolutionary process. Setting the parameters does not inform the evolutionary search, as Marks et al. would have you believe, but instead defines an evolutionary process.

Figure 2 provides another perspective on the point at which changes in the two waiting times balance. In each curve, going from left to right, the mutation rate is increasing, the mean fitness at equilibrium is decreasing, and the speed of convergence to the equilibrium distribution is increasing. The middle curve (L = 50) in the middle pane (N = 500) corresponds to Animation 2. As we slide down the curve from the left, the equilibrium distribution in the animation moves to the left. The knee of the curve is the point where the increase in speed of convergence no longer offsets the increase in expected wait for maximum fitness to occur when the process is near equilibrium. The equilibrium distribution at that point is the one shown in Figure 1. Continuing along the curve, we now climb steeply. And it’s easy to see why, looking again at Figure 1. A small shift of the equilibrium distribution to the left, corresponding to a slight increase in mutation rate, greatly reduces the (already low) incidence of maximum fitness. This brings us to an important question, which I’m going to punt into the comments section: why would a biologist care about the expected wait for the first appearance of a type of organism that appears rarely?

You will not make sense of what you’ve seen if you cling to the misconception that evolution searches for the “target” of maximally fit organisms, and that I must have informed the search where to look. What I actually did, by fine-tuning the parameters of the Glass model, was to determine the location and the shape of the equilibrium distribution. For the mutation rate that I selected, the long-term average fitness of the population is only 79 percent of the maximum. So I did not inform the evolutionary process to seek out individuals of maximum fitness. I selected a process that settles far away from the maximum, but not too far away to suit my purpose, which is to observe maximum fitness rapidly. If my objective were to observe maximum fitness often, then I would reduce the mutation rate, and expect to wait longer for the evolutionary process to settle into equilibrium. In any case, my purpose for selecting a process is not the purpose of the process itself. All that the evolutionary process “does” is to settle into statistical equilibrium.

Sanity check of some claims in the book

Unfortunately, the most important thing to know about the Glass model is something that cannot be expressed in pictures: fitness has nothing to do with an objective specified independently of the evolutionary process. Which variants of traits contribute 1 to fitness, and which contribute 0, is irrelevant. The fact of the matter is that I ignore traits entirely in my implementation of the model, and keep track of 1s and 0s instead. Yet I have replicated Glass’s results. You cannot argue that I’ve informed the computer to search for a solution to a given problem when the solution simply does not exist within my program.

Let’s quickly test some assertions by Marks et al. (emphasis added by me) against the reality of the Glass model.

There have been numerous models proposed for Darwinian evolution. […] We show repeatedly that the proposed models all require inclusion of significant knowledge about the problem being solved. If a goal of a model is specified in advance, that’s not Darwinian evolution: it’s intelligent design. So ironically, these models of evolution purported to demonstrate Darwinian evolution necessitate an intelligent designer.

Chapter 1, “Introduction”


[T]he fundamentals of evolutionary models offered by Darwinists and those used by engineers and computer scientists are the same. There is always a teleological goal imposed by an omnipotent programmer, a fitness associated with the goal, a source of active information …, and stochastic updates.

Chapter 6, “Analysis of Some Biologically Motivated Evolutionary Models”


Evolution is often modeled by as [sic] a search process. Mutation, survival of the fittest and repopulation are the components of evolutionary search. Evolutionary search computer programs used by computer scientists for design are typically teleological — they have a goal in mind. This is a significant departure from the off-heard [sic] claim that Darwinian evolution has no goal in mind.

Chapter 3, “Design Search in Evolution and the Requirement of Intelligence”

My implementation of the Glass model tracks only fitnesses, not associated traits, so there cannot be a goal or problem specified independently of the evolutionary process.

Evolutionary models to date point strongly to the necessity of design. Indeed, all current models of evolution require information from an external designer in order to work. All current evolutionary models simply do not work without tapping into an external information source.

Preface to Introduction to Evolutionary Informatics


The sources of information in the fundamental Darwinian evolutionary model include (1) a large population of agents, (2) beneficial mutation, (3) survival of the fittest and (4) initialization.

Chapter 5, “Conservation of Information in Computer Search”

The enumerated items are attributes of an evolutionary process. Change the attributes, and you do not inform the process to search, but instead define a different process. Fitness is the probabilistic propensity of a type of organism to leave offspring, not search guidance coming from an “external information source.” The components of evolution in the Glass model are differential reproduction of individuals as a consequence of their differences in heritable traits, variety in the heritable traits of offspring resulting from recombination and mutation of parental traits, and a greater number of offspring than available resources permit to survive and reproduce. That, and nothing you will find in Introduction to Evolutionary Informatics, is a fundamental Darwinian account.

1,439 thoughts on “Evo-Info 3: Evolution is not search

  1. Flint: It’s not surprising that creationists start with their conclusions and force the data to fit. And I guess it’s not surprising that creationists assume everyone else does the same, because that’s the only approach they can grasp.

    Not that you’re actually talking about anyone here at TSZ.

  2. Joe Felsenstein: Say, an even-odds bet that the mean fitness will end up higher than it starts out. I’ll bet on that; Mung can take the other side.

    I sense Joe the comedian making an entrance.

    Do you think that because you can create a program where “average fitness increases” that I cannot create a program where “average fitness decreases”?

    I’m always up for a bet that makes sense. I’m even willing to lose a bet if it means that Darwinist thinking gets exposed for what it is.

    How do you propose that “we measure the average fitness in that starting population.” Because that would mean that we haven’t looked at the actual number of offspring to determine fitness, an admission that fitness is not measured by reproductive success. That’s worth at least a bottle of fine scotch.

    Joe Felsenstein: Then we do some generations of reproduction, with those fitnesses affecting probabilities of reproduction and of survival. At the end we again measure the average fitness.

    As far as I’m concerned you’ve already lost the bet. You’ve flat out admitted what we’ve known and claimed all along. Fitness determines reproductive success rather than reproductive success determining fitness.

    In your debt. 🙂

  3. phoodoo: You mean you program in which genotypes you will make reproduce more, then see if they reproduce more?

    I’d bet on it.

    But I can program in which genotypes will reproduce less and see that if they reproduce less.

    The important question seems to be overlooked. Are there more models where fitness does not on average increase than models where fitness on average does increase?

  4. Tom English: There is not a single instance of the word modeler in the book. Models come from programmers. See a thing or two or three wrong with that?

    No, I don’t. Their point is that active information comes from the modeler, even if they don’t use that specific term. You appear to be engaged in nitpicking.

    They talk about models but don’t mention modelers! Well, yes, they do mention modelers.

  5. phoodoo: You mean you program in which genotypes you will make reproduce more, then see if they reproduce more?

    It’s not quite that simple, since the offspring of an individual may not be of the same genotype (cf. G. Mendel).

  6. Joe Felsenstein: It’s not quite that simple, since the offspring of an individual may not be of the same genotype (cf. G. Mendel).

    Are you discussing a computer algorithm or real life?

  7. Mung,

    I’m even willing to lose a bet if it means that Darwinist thinking gets exposed for what it is.

    Why don’t you write a formal paper and submit it to a journal for publication? If you’ve indeed exposed what you claim to have exposed then do the world a favour and share that knowledge so that others can be saved from such erroneous thinking.

  8. Mung: How about you create an OP on “Hoyle’s Fallacy” and how THE-DAWKINS-WEASEL eliminated any component of it. Then I’ll happily explain why you are wrong.

    Ahaha funny Mung. Here’s a better idea, I simply explain to you that Hoyle’s fallacy completely neglects any aspect of selection, while Dawkins WEASEL does not.

  9. Mung: Do you think that because you can create a program where “average fitness increases” that I cannot create a program where “average fitness decreases”?

    You can also create a gravitational model where forces are repulsive. Average the effects of both gravitational models and the forces cancel out. Why stop there? let’s do that with electromagnetism too, and every other model. What does that tell us?

  10. The point about creating a model in which fitness decreases is, that is what Marks, Dembski and Ewert did, in their papers and book. They find that natural selection is ineffective on average — because they average over models in which high fitness rewards the genotype and models in which high fitness punishes it.

    Once one makes a few sensible restrictions to a model, their proposed general result turns out not to be true. All you have to have is that different genotypes have fitnesses that can differ. And that fitness proportionately affects reproduction.

    You don’t even have to have the fitness surface be smooth, to escape the confines of the MDE theorem. Very minimal realism suffices. And what I am willing to bet on, is that imposing that minimal level of realism results in natural selection acting to increase average fitness.

  11. dazz, to Mung:

    You can also create a gravitational model where forces are repulsive. Average the effects of both gravitational models and the forces cancel out. Why stop there? let’s do that with electromagnetism too, and every other model. What does that tell us?

    As Mung will tell you, it means that active information has been smuggled into the original models.

    Evolution doesn’t work in real life, and neither does gravity. Therefore Jebus.

  12. Marks, Dembski, and Ewert open Chapter 3 by stating the central fallacy of evolutionary informatics: “Evolution is often modeled by as [sic] a search process.” The long and the short of it is that they do not understand the models, and consequently mistake what a modeler does for what an engineer might do when searching for a solution to a given problem.

    Just for clarification, are not WEASEL, Avida, Tierra considered evolutionary search algoirthms?

    If yes, and they model evolution then that affirms DME. If no, then why not?

  13. phoodoo: Actually, you decide the rules right?

    So you’re a postmodernist, phoodoo. I guess we should say then that the rules are socially constructed. Joe did not make them up on his own.

  14. stcordova: Just for clarification, are not WEASEL, Avida, Tierra considered evolutionary search algoirthms?

    If yes, and they model evolution then that affirms DME.If no, then why not?

    I remember the weasel thread, and Richard’s “math fun” threads where he proposed a genetic algorithm where no target was specified in advanced and the search space was infinite. I remember repeatedly asking Mung, if that algo was a search, what was it looking for and how do you know it’s found a “target”.

    To me, as a layman, Tom’s clarification that evolutionary algorithms are not searches, but samplers instead, makes complete sense. You can’t have a target without some criteria that’s independent from the algorithm. That’s what engineers do with GA’s. You can use the same exact algorithm and get different targets by setting different fitness requirements: for example, the first hit with fitness = 10 or higher will produce results that wouldn’t be targets if the requirement is changed to fitness = 20 or higher.

    In real life, even organisms with lethal mutations are part of the sample, but NS will bias the sample because such an organism won’t leave offspring. Pretending that we should also consider models where organisms with lethal mutations will produce offspring is preposterous, and Joe shouldn’t need to point it out time and again

  15. Rumraket: Ahaha funny Mung. Here’s a better idea, I simply explain to you that Hoyle’s fallacy completely neglects any aspect of selection, while Dawkins WEASEL does not.

    No, you’re wrong. As usual. In the tornado there are physical forces operating that do the selecting. They just aren’t doing the selecting based upon some preconceived final target or outcome, like THE-DAWKINS-WEASEL.

  16. OP: Marks, Dembski, and Ewert open Chapter 3 by stating the central fallacy of evolutionary informatics: “Evolution is often modeled by as [sic] a search process.” The long and the short of it is that they do not understand the models, and consequently mistake what a modeler does for what an engineer might do when searching for a solution to a given problem. emphasis added]

    stcordova: Just for clarification, are not WEASEL, Avida, Tierra considered evolutionary search algoirthms?

    If yes, and they model evolution then that affirms DME. If no, then why not?

    You want to leave Tierra (open-ended evolution — no target) out of the list, as Marks et al. have. Replace it with ev. Avida is a software platform for research in evolution of digital organisms. Marks et al. are attempting pathetically to fob off their pathetic response to one experiment (evolution of EQU) as rejection of a large body of research that’s been conducted using Avida.

    As I said in an earlier comment, a problem is a specification of a finite set \Omega of possible solutions, along with a specification of a nonempty solution set T \subset \Omega. Can you write out the problems that the scientists were given to solve? If so, can you explain why the scientists do not say that they’ve solved given problems?

    I emphasize given because it’s required by the “conservation of information” math. If the modeler specifies jointly (1) an evolutionary process and (2) an event that tends to occur in the process, then the modeler is not solving a problem. (In problem solving, the selection of a method of solution depends on the problem, but the problem does not depend on the selected method. Otherwise, the problem isn’t a problem. In Dembskian terms, you don’t paint a target where the archer’s arrows tend to go, and then say that the archer has information about the target.) The modeler is providing an account of circumstances under which the event may occur. The modeler is not saying that those circumstances magically poof into existence, to permit the evolution of anything and everything you might conceive. Most of what might have occurred in evolutionary processes has not occurred, and never will occur.

    In particular, Lenski et al. were not saying in the Avida-EQU experiment that there would be similar pathways to all “complex features.” They were saying, “This is how complex features can emerge.”

  17. Tom,

    Thank you for the response. The way I read what you say is

    WEASEL, Avida, and (Schneider’s) Ev are not search algorithms

    When I spoke to Bob Marks last year, I asked him about negative selection against complex features. We ran out of time before I could pursue the discussion further.

    The reason I posed that question is that if what happens in the wild is selection AGAINST complexity, then we should not expect natural selection to construct complexity, at best it might maintain what is there or maybe slow down deterioration.

    I wanted to tell him this might be a much easier way to frame the issue of biological evolution rather than the evolutionary informatics approach and conservation theorems, etc.

  18. Mung: No, you’re wrong. As usual. In the tornado there are physical forces operating that do the selecting. They just aren’t doing the selecting based upon some preconceived final target or outcome, like THE-DAWKINS-WEASEL.

    The junkyard tornado is a metaphor for assembly of a huge number of pieces, all at once, into an organism. The way to understand this is not to think about tornadoes, but instead to read what Hoyle wrote. From Wikiquote:

    Life cannot have had a random beginning … The trouble is that there are about two thousand enzymes, and the chance of obtaining them all in a random trial is only one part in 10^{40,000}, an outrageously small probability that could not be faced even if the whole universe consisted of organic soup.

    Fred Hoyle and N. Chandra Wickramasinghe, Evolution from Space

    There’s lots of other juicy stuff I might have quoted. I selected this because they refer outright to a single random trial. Dawkins called this single-step selection, obviously to contrast it with cumulative selection.

    Would you care to share with the class, Mr. Mung, the term that Marks, Dembski, and Ewert use for single-step selection, and the term they use in contrast?

  19. Tom English: Would you care to share with the class, Mr. Mung, the term that Marks, Dembski, and Ewert use for single-step selection, and the term they use in contrast?

    I’ve no idea. I don’t believe that single step selection even makes sense and I have no idea how to measure cumulative selection. Maybe keiths can help out with that last one.

    But THE-DAWKINS-WEASEL isn’t a model proposed for the origin of life, and selection wasn’t operating to help life get started so THE-DAWKINS-WEASEL is still irrelevant.

    And there’s no reason to believe that the selection going on with the tornado is single step selection. Just because Dawkins says it is? Hah.

  20. Tom English: The junkyard tornado is a metaphor for assembly of a huge number of pieces, all at once, into an organism.

    Given where you live you ought to know that tornadoes are not “all at once” phenomena. People who want to pain Hoyle as a creationist might think it’s about “all at once” assembly.

    Hmm… I wonder if I can get Tornado Design taught in public schools.

  21. dazz: Tom’s clarification that evolutionary algorithms are not searches, but samplers instead, makes complete sense.

    To clarify my clarification… 😉

    The practitioner of evolutionary computation uses a simulated evolutionary process to sample the space of possible solutions to a given problem. There’s more to search than sampling.

    The kind of search that Marks et al. address can be decomposed into two components. One component samples the space of possible solutions to the problem. The other component monitors the sampling process, and uses the sample (and associated data) to generate an output — hopefully a solution to the problem.

    When the sampling component simulates an evolutionary process, then the search is referred to as evolutionary, even though there’s nothing “evolutionary” about the component that uses the sample to generate an output.

    When a sampling process depends on data associated with already-sampled objects, the data processing generally biases the sample. Data processing is not a source of information about the data associated with as-yet unsampled objects. This holds irrespective of whether the objects are called possible solutions and the data are called fitnesses.

    Even as (two and a half) engineers, Marks, Dembski, and Ewert are wrong to refer to the association of fitnesses (data) with possible solutions as an “external information source.”

  22. Mung: And there’s no reason to believe that the selection going on with the tornado is single step selection. Just because Dawkins says it is? Hah.

    “Hoyle’s Fallacy” is his inappropriate probabilistic model, illustrated in the quotation I gave you, not his metaphor. So lose the tornado, already.

  23. Tom English: So you’re a postmodernist, phoodoo. I guess we should say then that the rules are socially constructed. Joe did not make them up on his own.

    Society decides how a computer program will be written to determine which genotypes will reproduce the most?

    This is getting weird,.

  24. Joe Felsenstein: And what I am willing to bet on, is that imposing that minimal level of realism results in natural selection acting to increase average fitness.

    Still waiting to hear your proposal as to how “we measure the average fitness in that starting population.” I guess we could assign each “genotype” a fitness and then claim we measured it. What do you say?

  25. Tom English: Avida is a software platform for research in evolution of digital organisms. Marks et al. are attempting pathetically to fob off their pathetic response to one experiment (evolution of EQU) as rejection of a large body of research that’s been conducted using Avida.

    But when Tom and his co-author needed to “refute” Behe and Dembski where did they turn? One experiment (evolution of EQU). Say it isn’t so.

  26. Mung: Still waiting to hear your proposal as to how “we measure the average fitness in that starting population.” I guess we could assign each “genotype” a fitness and then claim we measured it. What do you say?

    I guess you could pay attention when Joe comments on random assignment of fitnesses to genotypes. It suffices for genotypes to differ in fitnesses. Joe and I (mostly Joe) addressed this in “Fitness Surfaces and Searches: Dembski, Ewert, and Marks’s Search for Design.” I made some not-too-dumb comments of my own in the opening of “The Law of Conservation of Information Is Defunct.”

    Yes, one can randomly assign fitnesses to genotypes, randomly select an initial population of genotypes, and measure the average fitness of the initial population.

    Did you notice that Marks et al. again changed the meaning of Law of Conservation of Information? We had LCI for complex specified information, from 1997 to 2007. We had LCI for active information (inversely related to complex specified information) from 2008 to 2016. Both of those LCIs were laws of nature. Now LCI is a nebulous principle of computing, and doesn’t refer to any particular measure of information.

    Do you have any ideas on how to measure progress in ID? It’s clear that a count of books and papers doesn’t work. I guess we could just say, “And Designer saw that it was very good.”

  27. Mung: But when Tom and his co-author needed to “refute” Behe and Dembski where did they turn? One experiment (evolution of EQU). Say it isn’t so.

    Artfully constructed, Mung, though tenuously connected to reality.

  28. Mung: So fitness is assigned?

    I suppose that if I describe the evolutionary process with a Markov model instead of an algorithm, you will exclaim, “Aha! The model only does what you tell mathematics to do.”

  29. Mung, to Joe:

    Still waiting to hear your proposal as to how “we measure the average fitness in that starting population.” I guess we could assign each “genotype” a fitness and then claim we measured it. What do you say?

    If we were modeling the orbital dynamics of planetary systems, Mung would be complaining that we assign masses to the simulated planets instead of measuring them.

    He is simply too dim for these discussions.

  30. Mung: But THE-DAWKINS-WEASEL isn’t a model proposed for the origin of life

    No, but it applies to the emergence of enzymes. Hoyle seems to think these must have just popped into existence spontaneously in some single grand event. And when he calculates this to be an absurdity, he seems to give up entirely and say instead that life must have always existed and be spread around by panspermia.

    and selection wasn’t operating to help life get started so THE-DAWKINS-WEASEL is still irrelevant.

    That depends on what you count as the origin of life. Is a self-replicating molecule making copies of itself life? I don’t think it is. But that’s the kind of thing that can evolve, and therefore a selection process applies. So the incremental steps and cumulative selection aspect could still be very much relevant to the origin and adaptation of the first enzymes.

  31. Mung: Still waiting to hear your proposalas to how “we measure the average fitness in that starting population.” I guess we could assign each “genotype” a fitness and then claim we measured it. What do you say?

    Well that’s easy. Just decide which genotypes you are going to let reproduce more, then THOSE are the ones we will call the most fit.

    And Joe is probably right, the most fits ones will likely reproduce most!

  32. keiths:
    Mung, to Joe:

    If we were modeling the orbital dynamics of planetary systems, Mung would be complaining that we assign masses to the simulated planets instead of measuring them.

    He is simply too dim for these discussions.

    What does this have to do with evolution, dufus?

  33. phoodoo,
    Your insights deserve a wider audience. Have you ever considered formal publication?

  34. Mung, to Joe:

    Still waiting to hear your proposal as to how “we measure the average fitness in that starting population.” I guess we could assign each “genotype” a fitness and then claim we measured it. What do you say?

    keiths:

    If we were modeling the orbital dynamics of planetary systems, Mung would be complaining that we assign masses to the simulated planets instead of measuring them.

    He is simply too dim for these discussions.

    phoodoo:

    What does this have to do with evolution, dufus?

    As I remarked earlier:

    Phoodoo is battling it out with Mung and colewd for the title of “dumbest evolution critic at TSZ”.

  35. I’m back from RL distractions and note that some seasoned members seem to have forgotten that comments casting doubt on the mental abilities of fellow commenters break the rules and render them liable to being moved to guano.

    Please take notice.

    Also I emailed Professor Marks informing him of the existence of this thread (an automatic reply suggests he’s on holiday) and it would be a shame if the lack of substantiveness of such comments discouraged him from responding.

  36. Alan,

    Also I emailed Professor Marks informing him of the existence of this thread (an automatic reply suggests he’s on holiday) and it would be a shame if the lack of substantiveness of such comments discouraged him from responding.

    If he isn’t repulsed by a sanctimonious and hypocritical moderator, then he’s unlikely to be discouraged by truthful comments regarding phoodoo’s mental prowess.

    He’s far more likely to pull a Ewert and stick to venues where he can’t be openly challenged and freely questioned by his critics.

  37. phoodoo seems to have a rather broad definition of Design. Suppose that we ask ourselves what happens if a locus with two alleles has different fitnesses for the three possible genotypes, with the heterozygote Aa having the highest fitness. (This question was asked, and answered, in theoretical population genetics about 95 years ago).

    Since we specified the fitnesses, does that mean that phoodoo considers this a case of Design?

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