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
- the location and the spread of the equilibrium distribution, and also
- 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, uʹ = 2 ⨉ .005 = .01 for the blue process, and uʹ = 1/2 ⨉ .005 = .0025 for the orange process.
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 uʹ = .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 uʹ 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.
The Series
Evo-Info review: Do not buy the book until…
Evo-Info 1: Engineering analysis construed as metaphysics
Evo-Info 2: Teaser for algorithmic specified complexity
Evo-Info sidebar: Conservation of performance in search
Evo-Info 3: Evolution is not search
Evo-Info 4: Non-conservation of algorithmic specified complexity
Evo-Info 4 addendum
Well, then isn’t it a good thing that the post was featured long enough for us to succeed in communicating? Evolution is not a fitness maximizer. Evolution is not searching for an individual of maximum fitness. I copied the opening paragraph for phoodoo, and I’ll do it again for you (with a bit more emphasis added):
I’m showing you something that is standard fare in population genetics, but is understood by few nonspecialists. If you Google, you’ll find lots of references to stationary distributions as well as equilibrium distributions in genetics. I think the animations go a long way to make it understandable. There’s probably something comparable, somewhere on the Web, but I haven’t found it. (Not with a reasonable amount of poking around, anyway. I’d be glad to point folks to other sources.) Furthermore, I’ve made much better sense of Glass’s results than he did.
I am, to a fault, not a self-promoter. But I think that what I’ve put on display should astonish everyone. I didn’t think that evolutionary processes searched, but I was astonished when I first saw the pictures. Minimizing the expected wait for a maximally fit individual, maximum fitness occurs in only 1 of 295 generations over the long term. Maximizing individual fitness fast means maximizing individual fitness quite rarely. That’s stunning. Did I need to say in the post, “That’s stunning”? I thought everyone would be stunned without being told to be stunned. Did I blow it?
Also, I worked hard to get at key conceptual errors of lots of people, definitely not just ID proponents, that I’ve noted over the years. But it seems that you’re just now getting it. Does the problem lie with me? Or did you miss what I was saying because you thought you knew already what I was supposed to be saying?
I think the animations are pretty damned nice when viewed in full-screen mode. They were fun, though a lot of work, as a radical departure from the sort of thing I usually do. Did you notice how big some of the numbers of generations are in my runs of the Glass model? I extended Glass’s results considerably, to expose the regularity in the response of the model to parameter settings. I came up with a screaming implementation, including a novel and publishable implementation of a heap. I pack the 0-1 alleles into vectors, and use bitwise logical operations in superscalar registers to do mutation and crossover. I also generate vectors of random bits in the superscalar registers. To tell the truth, I did all of this shortly after my father died. I checked out of the world for a while, and dug into the project. It was good, just to play with the computer — something I truly love.
Sort of like a roulette wheel , someone sets up the mechanism but the results are unknown.
Mung, to Tom:
Mung,
You seem to have learned nothing from our lengthy discussion of the drift Weasel. A glance at this screenshot makes it obvious that fitness is not maximized:
Fitness maximization is problem solving. It’s hard to talk about evolution without teleology slipping in. That says something about language, not about evolution.
If you ask the evolutionists whether evolution is a teleological process — whether it seeks some end — almost all of them will say no. Then you highlight passages of their writing, and say, “Look, you’re saying that it’s teleological. Lots of you are saying it, over and over.” And they say, “No, evolution is not teleological. That’s not what we meant to say.”
Now what is your response? I’m afraid you’re going to tell me that the reason they don’t keep teleology out of their language is that evolution really is teleological. Do you think that they deny that evolution is teleological, even though they keep writing as though it were teleological, because prior commitment to Darwinian dogma leads them into self-contradiction? Is that it? Darwinists are so illogical? They deny what they see right in front of them.
Nothing that you catch Joe Felsenstein saying in plain language trumps anything that he’s said since the 1960s with various formal models. If you want to find out what he and other theoretical biologists really mean, you go with the models, not the plain language.
Everything — everything — that Marks, Dembski, and Ewert say about fitness as a source of information guiding the process to the target is wrong. From the perspective of the “no free lunch” theorems, the fitnesses are merely data associated with possible solutions in a sample. You cannot gain information about the fitnesses of as-yet unsampled solutions by processing the fitness data associated with sampled solutions.
I have only read a review of the book, but I will say that I doubt that’s an accurate description. My understanding is that Losos is interested in whether what has occurred before in evolution will occur again in the same or similar circumstances. It’s Gould’s “what would happen if we rewound the tape of evolution?” I know for a fact that Losos is not setting up arbitrary problems. To repeat, more or less, what I said above, when the “problem” is tailored to the evolutionary process, it’s not a problem in the sense of the math of Marks, Dembski, and Ewert.
I hate to say this, but you’ve again ignored what I’ve said about specification of a problem. If you cannot write out a problem without referring to what occurs in the evolutionary process, then the “problem” is not a problem in the ordinary sense.
Adaptation is all about opportunism. Almost every adaptation you can conceive is one that is highly unlikely ever to occur.
Tom, to Mung:
Mung’s fixation on teleological language really is pitiful.
If someone were to say “water seeks its own level”, one has to wonder if Mung would immediately pounce, saying “Aha! I told you fluid dynamics was teleological!”
keiths,
Haha, you are right keiths, when people are using that metaphor to talk about class dynamics in society, they really are not being very scientifically precise. They should change the metaphor about people of varying wealths tending to associate with each other, to more accurately reflect that water doesn’t “know” what its own level is.
And furthermore, the early bird doesn’t necessarily catch the worm. keiths want you to be clear on that.
Mung:
You probably can, particularly if they are speaking informally.
It’s more accurate to say that selection strives* to maximize fitness, but succeeds only if other conditions are met; for example, the selective pressure must be strong enough to overcome drift.
*Arf, arf! Another teleological word!
The entire point went right over phoodoo’s head.
You mean continuing to use more teleological words is more accurate?
Keep going keiths, this is fun.
Again, Mung, we covered this stuff in our extended discussion of the drift Weasel. Did none of that register with you?
It’s no wonder you know so little, even after 15 years or so in the evolution debates.
A stitch in time saves nine isn’t mathematically proven!
Don’t count your weasels before they hatch keiths!
Tom:
Tom, two paragraphs later:
Just sayin’.
I’m not sure what Mung would hope to achieve by trawling the interwebs for ‘something someone said’ re: maximising fitness. All we’d end up with is what that person said, which may or may not be correct, or may or may not be correctly interpreted by Mung. It’s this whole science-as-Catechism thing again. Noted 5-PhD-touting cleric X says …
One would have to be particularly careful about how one was understanding the term ‘fitness’. Evolution can’t ‘maximise’ it, else organisms would end up with infinite offspring numbers.
This is of course one of the areas of confusion which the word-gamers like to have fun with. Biologists say X, computer nerds say Y. But if one has played this particular word game a few times already, one knows this, and ought to be capable of parsing simple sentences without help.
Allan,
Amen.
Oops — I mean “hear hear”!
Yes, I lost sight of that.
One thing is clear though; whoever designed the non-linear-delayed-feedback systems in the brain would have to be outside of time and space in order to test its functionality…Though I’m pretty sure Tom and Joe can come up with some math to prove how evolution could have done it… 😉
Yep, that’s the one thing that’s clear.
LOL
Testing functionality? As in an iterative research and development cycle?
One alternative would be a mind that knows all possible existences simultaneously, sort of like a multiverse.
I went outside time and space once. It’s boring; nothing ever happens.
For that matter evolution can’t even improve fitness. How can evolution improve an individuals number of offspring? It can’t. Maybe you mean a random mutations can improve how many babies one has? Compared to what?
I heard he used the same method to come up with the idea to make banana peels slippery. Scatalogical humor, on the other hand, was just a lucky accident.
Funny, heard ID already proved it is impossible for irony to occur naturally.
phoodoo,
Single individuals aren’t really all that relevant to evolution, are they? For, like, the 500th time. Evolution can increase the number of carriers of an allele, if the mean fitness (mean offspring number) of carriers is greater than the mean fitness of non-carriers.
Now this is where you play dumb over that simple statement. In 3… 2 … 1 … ‘Carriers of the non-allele’, or something equally inane.
YEAH ALLAN BUT SURVIVORS SURVIVE ITS A TAUTOLOGY.
The evolution the bacteria and yeasts that cause fermentation has helped humans produce more offspring
Mutations that reduce fitness seem to be accepted without much trouble. “Mutations are all bad”, that sort of thing. It’s pretty obvious that possession of certain genetic traits will reduce the mean number of offspring that accrue to their possessors.
So, the general principle that different alleles can change fitness is accepted. But when faced with the idea that, instead of reducing it, they might raise it – no, never, a thousand times no! But, these are simply two sides of the same coin. If A produces fewer mean offspring than B, B inevitably produces more than A.
But Allan, single individuals are the only thing that produce offspring. Alleles don’t produce offspring.
If they did then maybe an infinite number of offspring WOULD be possible, and then you wouldn’t have needed to remind Tom.
Compared to what?
phoodoo,
The mean fitness of carriers compared to that of non-carriers. Do I have to type this out every time? I’d be most awfully grateful if you’d just cotton on to this point.
Is it your position that there is in fact no difference in mean offspring numbers for any carrier/non-carrier subdivision of any population? That gene alleles do not, in fact, affect offspring numbers in any way?
Funny, I heard ID the Future is going to run a piece Back to the Past on how information can run backwards in time and they would like to interview some evolutionists, like Felseinstein, Harshaman or Dawkins on how from an evolutionary standpoint “living in the past” would seem disadvantageous from evolutionary point of view in comparison to say… living in the present would have a significant advantage…
Allan Miller,
Allan, how many times do you need to be reminded that alleles don’t have offspring?
So when you play this game of trying to shift the definition of fitness to some group of characters, rather than to those that have the offspring, you make a bad argument.
You can have a mutation to an allele, to one allele, and no mutation to another allele, and you have no justification for claiming that it is the mutation to the allele that increased an organisms offspring. You can’t count how many offspring an allele had, organisms are combinations of many alleles and every single combination is different.
As I have said before, I know why you try to play the allele game, but if counting offspring is how you measure fitness, then you count offspring of a whole package of alleles. Every time you start your fitness nonsense, I know you want to escape individuals, but you are stuck with them.
But when you warned Tom about using the term of maximum fitness, you weren’t talking about alleles, because you can’t rule out an infinite number of offspring of an allele, you can only rule out an infinite number of offspring of an individual.
So yes Allan, when you try to shift to the allele game, I will call you on it.
No, I don’t think I have.
As many people have said since you asked this, no, evolution does not maxmize fitness. But fitnesses can have a large effect on evolution, and they the evolutionary changes are often in the direction of higher fitness. But as many people said here since you asked that, other evolutionary forces such as mutation, migration, and genetic drift have effects too. So, my answer is “What they said”.
The OP shows a nice example with fitness differences, mutation, and random genetic drift. And it shows a big effect of selection, but the distributions of genotypes does not end up with all of them having the highest possible fitness.
I notice that in these discussions a great reluctance of the creationist and ID-advocate commenters to admit that natural selection has any effect on increasing the average fitness of the population.
1. They say they’re fine with “microevolution”, but then …
2. They argue that natural selection is not a force.
3. They argue that fitnesses cannot be sensibly discussed, they’re something you only know about after the fact.
4. As Allan Miller noted, they are happy to say that a new mutant allele has lower fitness, but strangely reluctant to admit that, of the two alleles, one has higher fitness. B has lower fitness than A, but let’s not talk about A having higher fitness than B.
Well up top, in the OP, is a simple model where three different evolutionary forces have noticeable effects. Especially selection, which greatly changes the distribution of genotypes. If you turn off selection in the Glass model, the distribution of genotypes stays way over to the left.
Let’s try again: Allan was claiming that mutations that reduce fitness seem to be accepted without much trouble. For example mutations that cause Duchenne’s Muscle dystrophy
Duchenne muscular dystrophy (DMD) is a severe type of muscular dystrophy caused by mutations in the X-linked dystrophin gene. The symptom of muscle weakness usually begins around the age of four in boys and worsens quickly. The prognosis for the patients is very bad, with an life expectancy of around around 25
Are you saying that individual carriers of the mutation cannot be said to have reduced fitness because we are unaware of the allelic composition at the rest of the genome? Conversely, does not the wildtype allele confer increased fitness onto healthy individuals in comparison to those that carry the disease allele?
ETA: corrected typo
Yes I am denying that, at least as far as Allan & Co.’s definition of fitness goes.
You see, for them, it doesn’t matter if you are born with no legs and half a lung, as long as you can make babies, well, you are in luck. And this is precisely why their definition of fitness is so meaningless. Stephen Hawking is more fit than Christiano Ronaldo according to them. He is more fit than Bradley Cooper.
Don’t blame me, I am just the messenger.
I understand that, but as it happens patients with DMD don’t usually have children, satisfying the requirements of the definition of low fitness. Incidentally, this is what is keeping the mutation rare (thankfully).
Marks, Dembski, and Ewert’s book talks about fitnesses and fitness surfaces. Their concept of Active Information measures whether or not a model of “evolutionary search” reaches a target which is a local maximum of the fitness surface.
Somehow when phoodoo does his usual routine about how fitness is a meaningless concept and can only be known after the fact, phoodoo does not complain about their book.
Funny, that.
Fitness is not maximized when there’s no selection. I guess it depends on how you define fitness.
Yes, that’s where I was going next. 🙂
I’m considering an OP on it. I know it looks like I’m ignoring it, but I’m not.
I wasn’t the one who introduced teleology into the conversation.
Absolutely hilarious.
Mung: evolution maximizes fitness.
Keiths: better to say that selection strives to maximize fitness.
keiths thinks introducing more teleology is better.
It depends on who you ask. Another reason evolutionary theory is incoherent. It allows any evolutionist to say just about anything and it’s opposite and no one blinks.
Tomorrow you’ll be telling us how important single individuals are to evolution. Hell, evolut9ionists can’t even agree on whether it’s individuals that evolve or whether it’s populations that evolve or whether it’s something else that evolved. Genotypes, alleles, proteins. etc. etc.
Evolutionists make YEC’s look like saints.
So you met God, and God was boring. Nothing was happening. He wasn’t busy killing people with earthquakes and floods?
Even evolutionists agree. You have to bury your head in the sand and pretend it’s only Creationists who say it. How rational is that?
It didn’t even take you a DAY Allan. Here you are giving eidence against your previous claim that “single individuals aren’t really all that relevant to evolution.”
Looks like they are, after all.
You have to keep in mind who you are speaking to. Allan also believes that species have offspring.
Allan’s erected a staw-man. He’s doing a lot of that lately. I see it as a sign of frustration. He should just chill out and stop with the over-generalizations. 🙂
Ha, I also had a feeling it wouldn’t be as long as tomorrow.
Corneel,
But you said:
And so no, individuals who carry the mutation can not be said to have low fitness, on account of the mutation. Some have children. Not any more than a married couple that prefers to not have children.
Hell’s teeth! Find me anyone who has the least comprehension of evolutionary theory who maintains individuals evolve. There’s no question evolution takes place in populations and change occurs [at gametogenesis resulting in change]* in the genome of offspring, not in the parent. Individual’s genomes are fixed.
ETA* clarity