FEA and Darwinian Computer Simulations

_{Gil Dodgen

June 11, 2011

Intelligent Design

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Intelligent Design}

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In my work as a software engineer in aerospace R&D I use what is arguably the most sophisticated, universally applicable, finite-element analysis program ever devised by the most brilliant people in field, refined and tested for 35 years since its inception in the mid-1970s for the development of variable-yield nuclear weapons at Lawrence Livermore National Laboratory. It is called LS-DYNA (LS for Livermore Software, and DYNA for the evaluation of dynamic, nonlinear, transient systems).

A finite element is an attempt to descretize on a macro level what occurs at a molecular level in a physical system, so that a result is amenable to a practical computational solution. The learning curve for the use of this sophisticated technology is extremely steep, and the most important thing one learns is that empirical verification of the simulation results is absolutely required to validate the predictions of any FEA model.

In an LS-DYNA simulation, all the laws of physics and the mathematics that describe them are precisely known. In addition, all of the material properties associated with the physical objects are precisely quantified with empirical verification (density, modulus of elasticity, and much more).

The FEA solver computes a physical result by solving millions of differential equations with a minimal integration time step based on the time required for a disturbance traveling at the speed of sound to traverse the smallest finite element with the greatest mass density.

Even with all of this, and countless man-years of experience by sophisticated and experienced users (LS-DYNA has been used for many years in the auto industry for simulating car crashes) empirical verification is always required, by actually crashing a car to validate the FEA results.

In light of all this, consider the typical Darwinian computer simulation and the trust that could be put in one.

Darwinian computer simulations are simply a pathetic joke as they relate to biological reality. This should be obvious to anyone with experience in the field of legitimate computer simulation.

Comments

DrBot and Elizabeth Liddle bow out.Mung_{June 24, 2011
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I should say at this stage, that during my very interesting time here at UD, I have come to the interim conclusion that the best ID arguments are not against “Darwinian evolution” at all, but against the notion that the kind of self-replicators that are a prerequisite for Darwinian evolution are not adequately explained in non-ID terms.
It all ties in. But you've ordered Signature in the Cell, right? I've been re-reading just to prep, lol. But it's not just any self-replicators, it's the kind of information and information system we actually find in living things.Mung_{June 19, 2011
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Well, there’s an interesting argument! GAs start further from a solution so they have more chance of reaching one?
If you recall, and I'll find the link for you if you wish, you were the one who claimed that the initial population was unfit because they were all chosen at random. I responded that since the initial point in the search space was decided at random, there was a chance that one of the genomes could land smack dab on the best solution from the outset. So starting a a random spot in the search space is not the same as starting out further from a solution. Since we don't know in advance where in the search space a solution will be found, starting our search at random spots is as good a choice as any, and actually increases the likelihood of a successful search. You can of course test this by starting your initial population all off with the same genome and see which method performs better. The point is, even the way the initial population is seeded is purposely chosen. As is the size of the initial population.Mung_{June 19, 2011
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Thanks for accepting my word UPD. I appreciate it. I will also get to your latest post in due course. You raise some important points. Mung:
So let’s talk bout the initial population in a GA. Often, the specific setting of each “bit” of each chromosome/genotype is generated randomly. This is highly unlike a natural population. The result is that in a GA the begin for a search for a solution begins at random points in the space of potential solutions. This increases the likelihood of a successful search.
Well, there's an interesting argument! GAs start further from a solution so they have more chance of reaching one?
But natural populations start our very close together on the “fitness landscape.” Does it then follow that the likelihood of a successful search is decreased? Does this mean it is less likely that natural populations will find novel solutions?
heh. Well, we can "start" a simulation wherever we want. One thing that I have done is to evolve a population to survive optimally in a given "environment", then change the "environment", rather than go back to square one. And sure, they can do that. But I may be misunderstanding you (that's the trouble with metaphors, even when they are fairly specific, like "fitness landscape"). What exactly do you mean when you say: "natural populations start out very close together on the fitness landscape?" This would certainly be true during speciation, or even during the kind of temporary divergence between populations that the Grants observed in the Galapagos finches. But those kinds of divergences (where beak size distributions tend to become bimodal if there are two sizes of seed, or even trimodal if there are three, and unimodal if there is a more gaussian distribution of seed sizes) are eminently modelable by Darwinian simulations. In fact in Jonathan Weiner's book he actually cites simulations, and reports other field examples as well. What is true is that once a population has started down a path, potential solutions are constrained by what has gone before in that lineage. And in asexually reproducing species this is even more of a problem as there is far reduced opportunity to "mix and match". So in that sense, I'd agree - "natural" populations, once established, are constrained to a subset of the "solution space", by which I mean, solutions to the problem of persisting in the current environment. Once you are a tetrapod it's pretty difficult for population to explore six-legged solutions! (Although two-legged, no-legged, and winged are all possible). But this is one of the strongest arguments for Darwinian evolution rather than ID - the fact that living things form nested hierarchies of "solutions". Even if a population of tetrapods finds itself in an environment where back-legs are pretty irrelevant, but a finned tail would come in handy, it has to take a machete through uncharted solution-space, rather than simply borrow a ready-made from a friendly neighbourhood fish. I should say at this stage, that during my very interesting time here at UD, I have come to the interim conclusion that the best ID arguments are not against "Darwinian evolution" at all, but against the notion that the kind of self-replicators that are a prerequisite for Darwinian evolution are not adequately explained in non-ID terms. I don't think it's a watertight one, but I think it's the best you've got, by quite a long shot :)Elizabeth Liddle_{June 19, 2011
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So let's talk bout the initial population in a GA. Often, the specific setting of each "bit" of each chromosome/genotype is generated randomly. This is highly unlike a natural population. The result is that in a GA the begin for a search for a solution begins at random points in the space of potential solutions. This increases the likelihood of a successful search. But natural populations start our very close together on the "fitness landscape." Does it then follow that the likelihood of a successful search is decreased? Does this mean it is less likely that natural populations will find novel solutions?Mung_{June 18, 2011
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However, if you can bear it, feel free to register at Talk Rational ...
But there's no Intelligent Design forum there. ;)Mung_{June 17, 2011
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But hey, you say you got lost or whatever.......fine I accept your word. ;)Upright BiPed_{June 17, 2011
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Elizabeth, I have been on the threads that you have been on these past days sending you little hints. You had to virtually step over the one on this thread, for instance. The specific thread you and I have been participating in ("At Some Point The Obvious Becomes Transparently Obvious") has a direct link to it that has dutifully appeared on every page UD has served up for the past untold number of days. Its hardly a task to find it. (Ahem)Upright BiPed_{June 17, 2011
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Given that you’ve been consistently active on this forum for the past days while the operational definition you requested had been posted (and that posting brought to your attention) then these sentiments of yours must surely be called into question.
I'm afraid I have only just seen your link. I haven't yet acquired the knack of keeping up with new posts on this site, and I'm not permanently logged on. However, I knew you were busy, and was happy to wait for your response. Had I known it was there I would have responded earlier. Please don't assume that no response means I have dropped out of the conversation. Unfortunately this site, unlike traditional forums, doesn't have any way of letting participants communicate with each other to let them know if a post needs a reply, nor does it bump recently responded-to threads. However, if you can bear it, feel free to register at Talk Rational (you don't need to post) http://www.talkrational.org/index.php and contact me by PM if you need to alert me to a post here. I am "Febble" there. That applies to anyone here BTW :) In between logins an awful lot of water has flowed under the bridge sometimes, and sometimes I miss links to replies. But *puts on stern face and growls* - please don't jump to conclusions about my integrity just because you haven't received a reply. It's much more likely that I haven't seen it, or even that I've lost the link to the thread. *puts smiley face back on* :D Cheers LizzieElizabeth Liddle_{June 17, 2011
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2.3.1 Representation (Definition of Individuals) The first step in defining an EA is to link the "real world" to the "EA world", that is, to set up a bridge between the original problem context and the problem-solving space where evolution takes place. Objects forming possible solutions within the original problem context are referred to as phenotypes, while their encoding, that is, the individuals within the EA are called genotypes. The first design step is commonly called representation, as it amounts to specifying a mapping from the phenotypes onto a set of genotypes that are said to represent these phenotypes. - A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
So what should we make of an argument that asserts that it's the encoding that is designed, not the genotype? The encoding is the genotype. The first design step. The genotype (what I have also been calling the chromosome) in a GA is designed with a future goal in mind. The design of the genotype is critical to the successful operation of the GA. GAs are fully teleological, and therefore quite unlike biological evolution. Unless, of course, evolution is also teleological and living organisms are designed.Mung_{June 17, 2011
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UB: Okay, your link did not seem to work for me. Gkairosfocus_{June 17, 2011
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#114 Yes, thank you Kairos. That is exactly the post I was referring to.Upright BiPed_{June 17, 2011
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2.3 Components of Evolutionary Algorithms p 18 EAs have a number of components, procedures or operators that must be specified in order to define a particular EA. The most important components are: - Representation (definition of individuals) - Evaluation function (or fitness function) - Population - Parent selection mechanism - Variation operators, recombination and mutation - Survivor selection mechanism (replacement) Each of these components must be specified in order to define a particular EA. Furthermore, to obtain a running algorithm the initialisation procedure and a termination condition must be also defined. - A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
That's at least 6 components that need to be specified, and maybe more. The more that GAs appear to mimic biological evolution, the more life appears to be designed.Mung_{June 16, 2011
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...the first stage of building any evolutionary algorithm is to decide on a genetic representation of a candidate solution to the problem. This involves defining the genotype and the mapping from genotype to phenotype. When choosing a representation, it is important to choose the "right" representation for the problem being solved. Getting the representation right is one of the most difficult parts of designing a good evolutionary algorithm. - A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
genotype = chromosome defining the genotype and mapping = design chromosome design "Getting the representation right is one of the most difficult parts of designing a good evolutionary algorithm." You don't say. Lizzie, you left that out of your really important to distinguish three things in a GA post. Just sayin' Looks like there's more design in a GA than you're willing to credit.Mung_{June 16, 2011
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UB: I would add that in aggregate we should have at least 1,000 bits between the two, but that is probably going to be met by anything that specifies a protocol. Gkairosfocus_{June 16, 2011
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UB: Do you mean this post? With this?
In retrospect, when I stated that recorded information requires symbols in order to exist, it would have been more correct to say that recorded information requires both symbols and the discrete protocols that actualize them. Without symbols, recorded information cannot exist, and without protocols it cannot be transferred. Yet, we know in the cell that information both exists and is transferred . . . . Your simulation should be an attempt to cause the rise of symbols and their discrete protocols (two of the fundamental requirements of recorded information between a sender and a receiver) from a source of nothing more than chance contingency and physical law. And therefore, to be an actual falsification of ID, your simulation would be required to demonstrate that indeed symbols and their discrete protocols came into physical existence by nothing more than chance and physical law.
kairosfocus_{June 16, 2011
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Well, now. It does look as though my assumption is justified....now doesn't it? ;)Upright BiPed_{June 16, 2011
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Now I must get some much needed sleep - and I'm away for a little break tomorrow so I may not be commenting here for a while. ;)DrBot_{June 16, 2011
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There is nothing in nature or in evolutionary theory that says that populations start out less fit and get more fit. well, yes,there is actually. It’s called “adaptation” – evolving to survive optimally in a new or changing environment.
But there is also nothing that says that populations cannot sometimes loose fitness - after all fitness is partly a result of the environment, and environments can sometimes change fast. But most of the time the trend is for adaptation to the environment (increase in fitness)DrBot_{June 16, 2011
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Even if the initial bit string of each candidate solution in the initial population is chosen completely at random, there is still the possibility that when one of those chromosomes is tested for fitness, it will be as fit a “genome” as you’re ever going to find during the run. You simply cannot legitimately compare the initial population in a GA with a population of organisms in nature.
Quite correct. The first round of fitness evaluations on a random population amount to a random search, and you can get lucky. It is the process that follows that is the evolutionary search. If you were modelling how a population changes over the generations you might be better starting with a population of similar individuals. If you were modelling what happened after the first self replicator came to exist (either by design or not) then you would start with a population of 1, but allow the population to grow.
And whatever else you design, you do not design the genotypes that do the stuff rather well.
Which is not the same as saying that the genotype itself is not designed, which is what the current debate is about.
you do not design the genotypes, which is not the same as saying that the genotype itself is not designed? Actually that looks exactly the same, which is why I emphasised the point about how it is the encoding scheme that is designed or imposed, not the genotype.DrBot_{June 16, 2011
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There is nothing in nature or in evolutionary theory that says that populations start out less fit and get more fit. well, yes,there is actually. It's called "adaptation" - evolving to survive optimally in a new or changing environment. Hence the "Origin of Species">Elizabeth Liddle_{June 16, 2011
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As in, how much design is involved, and where, and why. Because if we don’t get that right, if we try to compare GA’s to biological evolution, or if we think GA’s model evolution, we’ll be fooling ourselves.
Thats quite correct, you just have to remember some of the things she said in that post no 43. For example, the real world exists, and fitness, or rather reproductive success is intrinsic - it is the result of real creatures existing in the real world. In a simulation we have to model reproductive sucess and an environment in some form so it has to be designed - models don't just appear in memory, we need to design an environment, a method of affecting reproductive success, of mutating genes. All aspects of a GA have to be designed or are imposed in some form, what matters when using them to study biological evolution is that they are a good approximation for the aspect of evolution being studied. When you play around with mutation rates in a simulated model of an aspect of biological evolution you are going to use the results of this parameter tuning to compare to emperical data. It's the same in physics, you can model gravity and matter, and design, a simulation where planets form. The fact that gravity and matter were put in to the simulation by design doesn't mean they are designed in reality (although they could be - its irrelevant to the purpose of the simulation). What matters is that your model is useful when trying to understand how planets might have formed, and produces results that can be compared to emperical data, and which can then be used to develop better models.DrBot_{June 16, 2011
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Elizabeth Liddle @98:
However, the common principle is the simple Darwinian one: you start off with a population of not-terribly fit individuals, you let them breed with a probability that is related to some fitness criterion (which obviously you design, but in nature could be anything, from camouflage to length of neck), and with variance. Then, a bit later, you find you have a population of individuals who can do stuff rather well that the original population did poorly or not at all.
There is nothing in nature or in evolutionary theory that says that populations start out less fit and get more fit. And in a GA each chromosome, even those in the initial population, is a potential (candidate) solution. Even if the initial bit string of each candidate solution in the initial population is chosen completely at random, there is still the possibility that when one of those chromosomes is tested for fitness, it will be as fit a "genome" as you're ever going to find during the run. You simply cannot legitimately compare the initial population in a GA with a population of organisms in nature.
And whatever else you design, you do not design the genotypes that do the stuff rather well.
Which is not the same as saying that the genotype itself is not designed, which is what the current debate is about. See my post @91.Mung_{June 16, 2011
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I’m not discussing evolutionary theory modelling in the main, I’m discussing how Genetic Algorithms work from an engineering perspective ...
And I'm discussing what it takes to get a GA to work in the first place. :) As in, how much design is involved, and where, and why. Because if we don't get that right, if we try to compare GA's to biological evolution, or if we think GA's model evolution, we'll be fooling ourselves. See Elizabeth's post @43.Mung_{June 16, 2011
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Yes, that's what I thought.Upright BiPed_{June 16, 2011
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You assume that the challenge was abandoned because?DrBot_{June 16, 2011
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Bot, have you been following the conversation? I hadn't noticed?Upright BiPed_{June 16, 2011
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You left out a third possible outcome beyond the glory of success or the admission of failure. That is the one where you simply disappear from the conversation after the odds of your success begin to stare you in the face. By abandoning the challenge after the requested operational definition was sorted out, you’ve not only failed to make your case, but you’ve also escaped the “downside” by not sticking around long enough to accept defeat.
Or simply that setting up an experiment to test this cannot be done in a week. You assume that the challenge was abandoned because?DrBot_{June 16, 2011
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Dr Liddle, do “operational definitions” have expiration dates? - - - - - - - - - - - When you wrote:
I am setting up a test of the hypothesis that, contrary to the claims of ID, Information (of a specific type, which we are currently trying to operationalise) can be generated without Intelligent Design. Obviously I will do my best to find a context that supports my hypothesis. But I may fail. That’s the downside (but also the glory) of science. On the other hand, if I succeed, then the ID argument fails.
You left out a third possible outcome beyond the glory of success or the admission of failure. That is the one where you simply disappear from the conversation after the odds of your success begin to stare you in the face. By abandoning the challenge after the requested operational definition was sorted out, you’ve not only failed to make your case, but you’ve also escaped the “downside” by not sticking around long enough to accept defeat. Why stoop to acknowledge the validity of your opponent’s argument, right? ;)
…your claim was that Information of the kind that is seen in living things could not be generated by Darwinian processes. I think it can, and I offered to demonstrate that it could. Sure it was a bit lacking in humility, I guess, but it’s not as though I was unprepared to put my efforts where my mouth is and risk hubris, I am.
Given that you’ve been consistently active on this forum for the past days while the operational definition you requested had been posted (and that posting brought to your attention) then these sentiments of yours must surely be called into question.Upright BiPed_{June 16, 2011
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Charles: I'm saying one of the things we can do with GAs. Yes, of course, we can model sexual selection, if that's what we are interested in (I've done that too). We can also let population size vary. We can have a very simple "phenotype" in which the phenotype is simply what the genotype does, or we can separate the functions, and even build in some stochastic slack between genotype and phenotype. However, the common principle is the simple Darwinian one: you start off with a population of not-terribly fit individuals, you let them breed with a probability that is related to some fitness criterion (which obviously you design, but in nature could be anything, from camouflage to length of neck), and with variance. Then, a bit later, you find you have a population of individuals who can do stuff rather well that the original population did poorly or not at all. And whatever else you design, you do not design the genotypes that do the stuff rather well. And often you found they've hit on a trick you would never have thought of. And sometimes they even cheat :)Elizabeth Liddle_{June 16, 2011
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