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
Think about it for a second Tom. If you invent what the definitions of fitness are, then tell the computer to select the ones which have the most traits you deemed fit, then it is a search for those traits that you made up. What is the revelation here?
One interesting note is this though. In your model, the moral of the story is, only less fit organisms can give birth to more fit ones, and the most fit ones can only give birth to less fit. Kind of silly really.
What could they possibly mean by this statement?
Do you think they are referring to programs like WEASEL, ev, and Avida? Are those not “models” of evolution?
I’m still wondering why Tom and his co-author (Greenwood) chose Avida as their platform of choice to “refute” Behe and Dembski in the book Design by Evolution.
I think anyone following this debate has a right to be scratching their head.
In order to perform a sort, do you need to perform a search? If so, is it a mistake to refer to evolution as a process of sorting?
Oi.
Random mutation (in any of its forms) is search. Random mutation effectively says, “let’s try over here”. That is search. Natural selection is really good at, “bad try, you’re dead.”
Random mutation of a working gene is not blind search. It is a nearby search. The chance that function will be found nearby is much greater than the chance of the dice.
De novo genes are successful random searches (or intelligent strategy, depending on which side of the fence you are on) in true random space. The fact that these prove successful, useful within their context, generally blows my ability to view them as unguided processes.
I didn’t see any Bible verses in Chapter 3. What’s up with that DiEb?
Is this in dispute?
Footnote 2
For example:
Thomas P. Schneider, “Evolution of biological information.” Nucleic Acids Res, 28( 14), pp. 2794– 2799 (2000).
R.E. Lenski, C. Ofria, R.T. Pennock, and C. Adami, “The evolutionary origin of complex features.” Nature, 423( 6936), pp. 139– 144 (2003).
G.J. Chaitin, Proving Darwin: Making Biology Mathematical (Pantheon, 2012).
D. Thomas, “War of the Weasels: An Evolutionary Algorithm Beats Intelligent Design.” Skeptical Inquirer, 43, pp. 42– 46 (2010).
D. Thomas, “Target? TARGET? We don’t need no stinkin’ Target!” http:// pandasthumb.org/ archives/ 2006/ 07/ target-target-w-1. html (URL date May 2, 2016).
D. Thomas, “FORTRAN for Genetic Algorithm” (2006). http:// http://www.nmsr.org/ genetic.htm (URL date August 25, 2015).
D. Thomas (2006). “Steiner Genetic Algorithm-C + + Code.” http:// pandasthumb.org/ archives/ 2006/ 07/ steiner-genetic.html (URL date May 2, 2016).
H.S. Wilf and W.J. Ewens, “There’s plenty of time for evolution.” P Natl Acad Sci 107, pp. 22454– 22456 (2010).
R. Dawkins, The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe Without Design (Norton, New York, 1996).
If I lean back in my chair, am I searching my room space?
The problem with the word “search” is that it implies a searcher and and and object.
Facepalm event horizon encountered.
I wonder who here at TSZ is more likely to hold that misconception.
If evolution is a search and it supports ID, what’s the designer searching for, Mung? What’s the target?
Oi indeed. What we have here is a search without any particular goal to be searched for. “Whatever works” is hazy enough not to describe any specifiable goal, but only the limits of a process.
I’d prefer the metaphor of the drunkard’s walk, where the drunkard has no idea when he starts out, as to what sort of place he might enjoy being at. So if the drunkard stumbles upon something not unpleasant, has he “found the goal he was searching for”?
If you answer YES, then you might regard the drunkard as having been guided by some invisible hand. If you regard that spot as one of an endless number of acceptable spots (while the drunkard’s preferences are constantly changing), them you don’t think the drunkard was actually searching for anything.
I don’t argue that unguided Darwinian evolution is a search. I leave that up to Darwinists to argue.
And lest the critics of ID declare victory prematurely, they really should try to understand what Tom’s OP is telling us about Darwinian evolution. Because it sure as hell isn’t describing anything like the designer substitute that most people here think of when they think of Darwinian evolution.
If you want to argue that evolution can mimic design, or create things that have the appearance of design, you’re going to need something more than the model that Tom has presented.
Good Luck!
Flint,
The Drunkard and The Spine.
Now on sale at Barnes and Noble.
Your use of “design” as both a verb and a noun has confused you. I think you need to be more specific. I’m going to guess that what you intended to say was:
“If you think that evolution can mimic the human design process, or create things that look like they were designed by humans.
The first usage is a verb, and nobody has ever remotely claimed that the human design process mimics nature’s design process. These processes aren’t similar in any way. The second usage is a noun, and I don’t think anyone has said that living organisms look like something humans would have come up with.
I’m not sure what point you are trying to make here. I’m not even sure if your ambiguity is consciously deliberate.
I don’t know what you are trying to say here, but I’m not surprised you made no attempt to answer my question. Your MO being mocking attack whenever you can’t answer, I’m going to assume that’s what you’re trying to do here.
What I am trying to say is, what is the usefulness of a drunkard walking analogy? Are we to believe that this is similar to how the theory of evolution developed kidneys, or the lymphatic system?
To me its like saying, a cat chasing a rubber mouse is sort of like how volcanoes form.
That’s irrelevant. If you think evolutionary models actually model “guided” evolution or “directed Darwinism” or whatever you want to call it, what’s that guidance or direction when applied to the real world?
What should I be looking for to be able to tell guided evolution from unguided evolution and why?
Is it just me, or did Tom just write an OP on why evolution is not a search and fail to define what a “search” is? What do you think DiEb, did you notice that too?
Flint the comedian.
The Blind Watchmaker: Why the Evidence of Evolution Reveals a Universe without Design
Noun or Verb? People who live in glass houses and all that.
Mung,
Supported by an EA that demonstrates evolution by design 🙂
Or is it Design by Evolution?
LoL. You think?
Mung,
Have you ever run Weasel without a target? Beyond 3 letters I have never seen it finish.
In chapter Three of their book, the Chapter Tom has chosen to quote from to lead off his OP, DEM provide a list of search algorithms.
Not search algorithms? Irrelevant to the debate over “Design by Evolution”?
The question in my mind isn’t so much whether or not evolution is a search, it’s why do people continue to model evolution as a search by employing the use of search algorithms if evolution is not a search?
The analogy is intended to illustrate that evolution has no particular direction, and like the drunk, it can wander anywhere within very wide limits. I don’t understand why you would say that a theory developed kidneys, but I’ll assume that’s some kind of shortcut to whatever you’re trying to say.
So back to the drunk: there is absolutely no reason WHY kidneys or lymphatic systems might evolve. The number of different organs, or systems, or who knows what, that COULD have evolved is essentially infinite. The theory doesn’t say kidneys will develop, but it does say that THINGS, currently unguessable, will eventually develop. There’s no telling where the drunk will walk, but we can be sure he will keep walking.
I think your problem is, you are looking at specific locations the drunk happened to stumble on, and thinking that these were somehow the TARGET the drunk was aiming at. And we can look at those places and be amazed at how vanishingly unlikely it was to hit those targets, without realizing that we would be equally amazed at the “targets” NO MATTER WHERE THE DRUNK STAGGERED TO! Every one of them vanishingly unlikely.
Evolution can be properly modeled as a search so long as “anything whatsoever that works” constitutes a successful search. A game of pin the tail on the donkey where anything the pin sticks to is as good a donkey as any other.
Did you even read the OP? Or is it that even after reading the OP you are still stuck regurgitating evolutionist talking points? The OP indicates that evolution is clearly directional, always moving towards equilibrium, just like everything else in the universe. The only thing that surprises me about this is that the evolutionists here aren’t arguing with Tom.
bawk. bawk. cluck, cluck, cluck.
Of course this just begs the question, if it is moving towards equilibrium, how did it get out of equilibrium in the first place?
It just happened, that’s all. Isn’t that what I’ve been saying? OMagain even dedicated an OP to it.
it just happened … it just happened … it just happened … “POOF!” AN EYE!
Utterly indistinguishable from magic.
I’d sure love to see someone calculate how long it takes to evolve an eye using Tom’s model.
According to DEM you have injected active information.
😉
“What I hope to convey in this post, primarily by means of graphics…”
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 review: Do not buy the book until…
Evo-Info 3: Evolution is not search
Flint,
For multicellularity we needed a system to get oxygen reliability to all cells. Do you have a proposal on how the drunk watchmaker pulled this off? How many alternatives to the current circulatory system we observe in life can you propose?
I like pictures. That keeps things at my level. I’m trying to picture DiEb complaining that you failed to define “search” and I’m just not seeing it. Like I said, double-standards appear to be in play.
If you are gong to argue that evolution is not search, isn’t it important to say what “search” is? Else how can anyone decide whether they agree with your or disagree with you?
It’s not going to bother me all that much if you don’t. But it might bother DiEb. 🙂
I guess we didn’t read the same OP. In that model, the environment is a constant. And given a constant environment, the Brownian motion is going to be around local peaks. In the real world, the environment is not constant. The equilibrium is at best temporary, and in the longer term is chasing a constantly changing environment.
Like talking to a wall. How long will it take to evolve anything to which you can attach a label? “The eye” is not a target, it’s simply one of a great many possible ways of using the electromagnetic spectrum. A very few of which organisms have stumbled across.
To hear your rendition, the drunk just POOF happens to be where his next step took him. As far as you can tell, that was pure magic. Every aimless step is magic.
To get the [make up a name] to happen, we needed a system to make a very specific use of [name some material]. How many alternatives to that system can you think of? After all, as many as you can think of MUST be all there can be, right?
Intelligent Disequilibration.
Are the changes in the environment that you’re appealing to random with respect to fitness? This is the question that Alan Fox didn’t want to face up to. Perhaps he just didn’t understand it.
LoL. No doubt!
Some theists believe that in the beginning everything was perfect. Whatever that may mean. And it’s all downhill from there!
Tom, there’s something vital missing from your model of evolution. Evolution is a designer. As such, you failed to capture the essence of evolution.
I don’t think you’ve given us a model of evolution at all. 🙂
From Evo-Info Sidebar: Conservation of Performance, for your convenience:
___________________________________
The root cause of my error is a failure to recognize that the “no free lunch” theorems actually address sampling, not search. There are two main components of a search, one of which generates a sample of possible solutions to a problem, and the other of which outputs the best solution it can find in the sample. The theorems address the choice of a sampling component, assuming that the solution-seeking component is fixed. My lemma indicates that sampling processes are devoid of information. There is no conservation of something that does not exist in the first place. As everyone knows, if only by reading the news, sampling processes are distinguished by their biases. The performance (utility) of a sampling component in generating a sample of possible solutions for use by the solution-seeking component has nothing to do with information.
Evolutionary informatics is founded on the conflation of evolution and search. The main topic of the book is evolutionary search for a solution to a problem. What I hope you will remember always, after reading this post, is:
Only the sampling component of an evolutionary search is evolutionary.
The sampling component simulates an evolutionary process in which the “fitness” of a solution is its goodness. (What biologists mean by fitness is not goodness, but instead the expected number of offspring left by an organism, depending on its heritable traits.) The solution-seeking component bears no relation to biological evolution. My lemma says that the evolutionary sampling process gains no information about the fitnesses of unsampled solutions by processing the fitnesses of sampled solutions (and has no information in the first place). Technically, the sample is statistically independent of the fitnesses.
If you remember now what I hope you will remember always, then you will notice that the opening of Chapter 3, “Design Search in Evolution and the Requirement of Intelligence,” is ever so slightly misleading:
Both of the sentences are false. The first is the opposite of the truth. And the problem is not just with this passage. The authors repeatedly conflate scientific modeling of evolution with engineering of an evolutionary search for a solution to a problem. It is vital that they lead readers to misbelieve that the two are the same, because they develop engineering analysis of evolutionary search only for misapplication to models of evolution. Analysis of how well models work, under the unwarranted assumption that modelers do not model, but instead engineer evolutionary searches to solve problems, is an empty accusation of misconduct. The results of such an analysis are not evidence that the assumption holds. Marks et al. write, in the second paragraph of the preface:
Hopefully you see now that they are referring to an evolutionary search as an evolutionary model. It is an evolutionary search for a solution to a problem that performs well (“works”). What they mean by “external information source” is a fitness function. But I established, 21 years ago, that an evolutionary sampling process does not gain information by processing fitnesses. And what sense does it make, when addressing biological evolution, to regard the probabilistic propensity of a (type of) organism to leave offspring as information coming from an external source? The fact of the matter is that Dembski et al. refer to performance as information, and to everything that causes an evolutionary search to perform well as a source of information.3 It is performance that is conserved.
Yeah, the YECs are hilarious in this regard. They need ultra-fast evolution to account for the fabulous diversity of species originating in the representatives of kinds that came off the Ark, about 4300 years ago. But they can’t let go of the belief that mutations only break stuff (God saw that it was very good, Fall of Man, genetic entropy, and all that).
YEC requires hyper-evolution. One of the main reasons I see YECism as self-contradictory and thus irrational. Yet I can’t recall any of them here at TSZ or over at UD that will even admit this is a problem for them.
Maybe one day Salvador will take me off ignore so we can discuss it, lol.
Flint,
Once the heart muscle evolves it needs a medium to pump. The medium must match the heart in order to efficiently reach the cells. The lungs must also match the heart.
The new system must be built as an extension of prior structures so as you go along your degrees of freedom get reduced so the watchmaker needs to sober up fast. 🙂
So, in your thinking, if some algorithm lacks either of these two components it is not a search algorithm.
Yet it seems to me as if evolution has both. Reproduction, random mutation, etc., produce samples, and natural selection outputs the “best” solutions.
Surely you don’t mean that the process can only return one single best solution. It can return a collection of potentially best solutions, can’t it? It’s not like WEASEL where only the best match is taken as the basis for the next generation.
And if the second component of a search returns a less than optimal solution, why should that disqualify it as a search?
Flint,
I wonder how many computer systems the army uses that were designed by drunks stumbling along and hitting things.
Maybe all of them!
I have an exact number here somewhere. Hang on…
From the OP:
How do we tell whether whether or not fitness has nothing to do with an objective specified independently of the evolutionary process?
In WEASEL, fitness is defined according to an objective specified independently of the evolutionary process.
In ev, fitness is defined according to an objective specified independently of the evolutionary process.
In Avida, fitness is defined according to an objective specified independently of the evolutionary process.
It would appear that DEM have a point after all.
Yet Tom defended and relied upon Avida in his critique of ID in the chapter published in Design by Evolution. Perhaps Tom is older and wiser now?
The central claim of the debate is that evolution is “a design process” and that evolution “serves as a designer.” Tom’s model fails to capture either of these defining characteristics of evolution. Therefore, Tom’s model is not an evolutionary model.
I respect Tom, but I feel obligated to respond to his claims lest ignorant people be misled.
Tom writes:
The proper way to test their claims is to test them against the models they actually address.
Tom proposes a new model of evolution and complains that DEM failed to address his model. Well, duh.
When DEM claim that “There have been numerous models proposed for Darwinian evolution,” surely they have specific models in mind. When Tom claims that these models are not models of Darwinian evolution he clearly has in mind a specific model of evolution.
But wait. I’m still waiting to hear Tom’s opinion about the programs targeted by DEM