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New “Reports of the NCSE” is out

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Here. I was especially interested in a couple of articles. One, by Lorence G and Barbara J Collins, is More Geological Reasons Noah’s Flood Did Not Happen (pdf). It contains a good discussion of what “uniformitarianism” means in contemporary geology, as distinguished from its 19th century usage.

Another is from James A Shapiro, University of Chicago geneticist, whose ideas about an alleged paradigm shift in evolutionary theory have been severely criticized by (among others) Jerry Coyne, also at the U of Chicago. See

Shapiro’s article in the new RNCSE, however, is an attempted rebuttal of Larry Moran’s scathing RNCSE review of Shapiro’s new book, Evolution: A View from the 21st Century. Moran’s review concluded

Shapiro, like [Richard von] Sternberg, is widely admired in the “intelligent design” community and there’s a good reason for this. This book is highly critical of old-fashioned evolutionary theory (neo-Darwinism) using many of the same silly arguments promoted by the Fellows of the Discovery Institute’s Center for Science and Culture. Those fellows are dead wrong and so is Shapiro.

Fun times.

It has been announced that Robert Sokal died on April 9. I wrote a brief obituary here last autumn for his co-worker Peter Sneath. Together they pioneered the use of clustering algorithms in taxonomy, and argued for the adoption of phenetic methods based on clustering there. While they were ultimately unsuccessful in this, they became founding fathers of work on mathematical clustering, and their book Principles of Numerical Taxonomy was widely-noticed and greatly stimulated the development of phylogeny algorithms. A paper by Michener and Sokal (1957) is, as far as I can tell, the first one publishing a numerical phylogeny. His publication of the 1965 paper by Camin and Sokal in Evolution, and a visit he made to the University of Chicago that year, inspired me to start working on phylogeny algorithms.

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Robert Sokal in 1964 at the International
Entomological Congress in London
Bob Sokal, more recently

Bob’s Stony Brook colleague Michael Bell has written a fine obituary, which I reprint below with his permission.

James F. Crow 1916 - 2012

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James F. Crow died peacefully in his sleep in Madison, Wisconsin on January 4th at the age of 95, having nearly reached his 96th birthday. Jim, as everyone who knew him called him, was one of the most important population geneticists of the 20th century, a major figure in the generation that followed Fisher, Wright, and Haldane.

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Jim Crow in Mishima, Japan, 1972               Crow and Kimura in discussion, Mishima railroad station, 1972            photos by J.F.

His father was a cytologist who did graduate work soon after the rediscovery of Mendel’s work. Jim did his graduate work in the 1930s at the University of Texas, where he had gone in hopes of working with H. J. Muller (who had, however, already left). He later had opportunities to work with Muller, and always considered himself primarily influenced by Muller. After working at Dartmouth College during and after World War II, he moved to the University of Wisconsin - Madison, where he spent the rest of his career. His many honors included election to the National Academy of Sciences and as a Foreign Member of the Royal Society.

He was famous as a teacher and mentor of numerous population geneticists, of whom I am one. In the 1950s he started traveling to Japan; he had many Japanese collaborators and students. Motoo Kimura was his Ph.D. student, and began a longtime collaboration with him. In 1970 they published An Introduction to Population Genetics Theory, which became the standard textbook of that field. Jim’s plain and folksy speaking style was the same as his writing style – he was enormously prolific and famous for his clear exposition. Among its many effects on the field, the book popularized Gustave Malécot’s way of defining inbreeding coefficients and using them to compute covariances among relatives for quantitative characters.

Jim’s many papers included major work on mutational load and other forms of genetic load, the concepts of inbreeding and variance effective population number, and expanding on R. A. Fisher’s and H. J. Muller’s theory of the evolutionary advantage of recombination. In the 1950s and early 1960s he was a major participant in the debate over genetic variation in natural populations, arguing against Theodosius Dobzhansky’s view that attributed it largely to balancing selection. With Motoo Kimura in 1964 he derived the expected heterozygosity brought about by neutral mutation, and he played a major role in assisting Kimura in effectively presenting his case for neutral mutation. He helped bring Sewall Wright to Madison in 1955, and Jim and Ann Crow were important as friends during Wright’s later years.

In addition to these he contributed numerous insights in his many papers. He was interested in all of genetics, and read its literature widely. As an invariably polite, surprisingly modest, and easily approachable mentor who was always interested in clarifying and simplifying models, he had a great effect. Through his lab passed much of a generation of theoretically-inclined population geneticists. If your name was Morton, Kimura, Maruyama, Hiraizumi, Kerr, Sandler, Hartl, Langley, Gillespie, Ewens, Li, Nagylaki, Aoki, Lande, Bull, Gimelfarb, Kondrashov, Phillips, or Wu, you were among the many who were in Jim’s debt, and remember him warmly as friend and role model.

I talked to Bill Dembski in person about my work on using Genetic Algorithms to solve Steiner’s problem way back in 2001. He didn’t “get” it then, and he still doesn’t!

Reacting to this news story, “Supercolony trails follow mathematical Steiner tree”, Dembski writes today that

Some years back, ID critic Dave Thomas used to tout the power of genetic algorithms for their ability of solve the Steiner Problem, which basically tries to minimize distance of paths that connect nodes on a two-dimensional surface (last I looked, he’s still making this line of criticism - see here). In fact, none of his criticisms hit the mark – the information problem that he claims to resolve in evolutionary terms merely pushes the design problem deeper … In ID terms, there’s no problem – ants were designed with various capacities, and this either happens to be one of them or is one acquired through other programmed/designed capacities. On Darwinian evolutionary grounds, however, one would have to say something like the following: ants are the result of a Darwinian evolutionary process that programmed the ants with, presumably, a genetic algorithm that enables them, when put in separate colonies, to trace out paths that resolve the Steiner Problem. In other words, evolution, by some weird self-similarity, embedded an evolutionary program into the neurophysiology of the ants that enables them to solve the Steiner problem (which, presumably, gives these ants a selective advantage).

Kudos to Dr. Dembski for this classic Goal-Post movement! The purpose of my original article was simply to move the discussion of Genetic algorithms beyond the ID “Dawkins Defense,” namely that all genetic algorithms suffer the “Weasel” flaw of needing the solutions to be incorporated directly into the fitness function.

Dembski’s response is remarkable in that it totally avoids the issues I raised. Just because ants can find ways for colonies to make efficient paths has no bearing on whether genetic problems can be applied without having solutions in hand already.

My original article on Steiner (Target? TARGET? We don’t need no stinkin’ Target!) showed that there are also physical methods for solving Steiner’s problem, including minimal-surface soap films.

If soap films can solve Steiner problems, why not ants? And this bolsters the Weasel defense, how?

My Skeptical Inquirer article from last year, “War of the Weasels: An Evolutionary Algorithm Beats Intelligent Design” has a nice summary of these Weasel Wars, including the marvelous story of UD’s software engineer, Sal Cordova, getting whupped by a Genetic Algorithm on an open-book design problem. The article posting is courtesy of Southern Methodist University’s Critical Thinking/Physics Class!

More: Panda’s Thumb’s “EvoMath” category.

Many readers will be familiar with longtime TalkOrigins regular Doug Theobald – he is the author of “29+ Evidences for Macroevolution: The Scientific Case for Common Descent,” pretty much the most impressive FAQ of all time. Oh, and he’s a professor too, and has published some other stuff.

Today he has published a pretty impressive paper in Nature. It is entitled “A formal test of the theory of universal common ancestry.” Basically, it applies the likelihood-based and Bayesian phylogenetic techniques that have been developed over the last decade or two, adds in some standard model-selection theory, and uses these to assess “universal common ancestry” (UCA). A lot of arguments “for common ancestry”, e.g. biogeography, are really arguments for the common ancestry of groups of modern-day organisms – like mammals – rather than arguments that every living thing we know about shares common ancestry. There have been some powerful arguments for UCA over the years – e.g. the extremely conserved (if not quite identical) genetic code (and as everyone except Paul Nelson knows, “almost identical” and “identical” are virtually the same thing statistically, so his decade of yammering about the non-universality of the genetic code has had no impact on this evidence). However, although the arguments remain powerful and convincing, they weren’t usually quantitative and statistical, and it takes some serious work to construct a statistical assessment of something as deep and universal as common ancestry. This is what Doug has done.

He’s getting a lot of press. Just in Nature there is a News & Views from Mike Steel and David Penny, and a Nature podcast.

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PT veterans may remember several posts from 2006, in a summer-long series of articles about Genetic Algorithms, Dawkins’ Weasel, and Fixed Targets.

It’s taken me a few years to get off my duff and write up a proper version for the Skeptical Inquirer. I’m pleased to report that my article has been published in the May/June 2010 issue.

The Good News: Several of my computer-generated diagrams have been professionally redrawn, and look splendid!

The Bad News: Besides the “Web-Extra” sidebar about Solving Steiner Problems using soap films, the article itself, “The War of the Weasels: How an Intelligent Design Theorist was Bested in a Public Math Competition by a Genetic Algorithm!”, appears only in the print copy. You will have to go to your local newstand to get a print copy, or order one from the Committee for Skeptical Inquiry (CSI) directly.

So, after almost four years, how has the ID community responded? Are they still fixated on Dawkins’ “Weasel” demonstration? Do they still maintain that all genetic algorithms require detailed knowledge of their solutions, just as the phrase “METHINKS IT IS LIKE A WEASEL” was the “fixed target” in Dawkins’ 1986 exposition?

More below the fold.

A couple of months ago, I finished a first reading of Stephen Meyer’s new book, Signature in the Cell. It was very slow going because there is so much wrong with it, and I tried to take notes on everything that struck me.

Two things struck me as I read it: first, its essential dishonesty, and second, Meyer’s significant misunderstandings of information theory. I’ll devote a post to the book’s many mispresentations another day, and concentrate on information theory today. I’m not a biologist, so I’ll leave a detailed discussion of what’s wrong with his biology to others.

In Signature in the Cell, Meyer talks about three different kinds of information: Shannon information, Kolmogorov information, and a third kind that has been invented by ID creationists and has no coherent definition. I’ll call the third kind “creationist information”.

Intelligent design creationists love to talk about information theory, but unfortunately they rarely understand it. Jonathan Wells is the latest ID creationist to demonstrate this.

In a recent post at “Evolution News & Views” describing an event at the University of Oklahoma, Wells said, “I replied that duplicating a gene doesn’t increase information content any more than photocopying a paper increases its information content.”

Wells is wrong. I frequently give this as an exercise in my classes at the University of Waterloo: Prove that if x is a string of symbols, then the Kolmogorov information in xx is greater than that in x for infinitely many strings x. Most of my students can do this one, but it looks like information expert Jonathan Wells can’t.

Like many incompetent people, Wells is blissfully unaware of his incompetence. He closes by saying, “Despite all their taxpayer-funded professors and museum exhibits, despite all their threats to dismantle us and expose us as retards, the Darwinists lost.”

We don’t have to “expose” the intelligent design creationists as buffoons; they do it themselves whenever they open their mouths.

Durston’s devious distortions

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A few people (actually, a lot of people) have written to me asking me to address Kirk Durston’s probability argument that supposedly makes evolution impossible. I’d love to. I actually prepared extensively to deal with it, since it’s the argument he almost always trots out to debate for intelligent design, but — and this is a key point — Durston didn’t discuss this stuff at all! He brought out a few of the slides very late in the debate when there was no time for me to refute them, but otherwise, he was relying entirely on vague arguments about a first cause, accusations of corruption against atheists, and very silly biblical nonsense about Jesus. So this really isn’t about revisiting the debate at all — this is the stuff Durston sensibly avoided bringing up in a confrontation with somebody who’d be able to see through his smokescreen.

If you want to see Durston’s argument, it’s on YouTube. I notice the clowns on Uncommon Descent are crowing that this is a triumphant victory, but note again — Durston did not give this argument at our debate. In a chance to confront a biologist with his claims, Durston tucked his tail between his legs and ran away.

Creationists think information theory poses a serious challenge to modern evolutionary biology – but that only goes to show that creationists are as ignorant of information theory as they are of biology.

Whenever a creationist brings up this argument, insist that they answer the following five questions. All five questions are based on the Kolmogorov interpretation of information theory. I like this version of information theory because (a) it does not depend on any hypothesized probability distribution (a frequent refuge of scoundrels) (b) the answers about how information can change when a string is changed are unambiguous and agreed upon by all mathematicians, allowing less wiggle room to weasel out of the inevitable conclusions, and (c ) it applies to discrete strings of symbols and hence corresponds well with DNA.

All five questions are completely elementary, and I ask these questions in an introduction to the theory of Kolmogorov information for undergraduates at Waterloo. My undergraduates can nearly always answer these questions correctly, but creationists usually cannot…

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According to Rotten Tomatoes, the movie WΔZ starts showing today in the UK. The movie is a psychological thriller/horror movie and has been compared to Se7en. What makes this movie interesting is the fact that the screenplay was inspired by Price’s Equation:

Price’s Equation is a broader version of Fisher’s Fundamental Theorem of Natural Selection. It describes how the change in trait with phenotypes is related to the phenotypes’ fitnesses, . Note that the genetics of the trait (mutation, ploidy, etc.) is contained in the second term. See Wikipedia for more details.

Now according to the Rotten Tomatoes exclusive on WΔZ:

The script comes from City of Vice scribe Clive Bradley, who claims to have come up with the movie’s premise after flicking through a book on Darwinism. “It featured a mathematical equation—W Delta Z—formulated by American population geneticist George R. Price,” he explains. “It supposedly shows that there’s no real altruism in nature; no such thing as selflessness. Price was so upset by his findings that he ended up giving away all his possessions to the poor and, eventually homeless himself, committed suicide with a pair of nail scissors in a filthy London squat.”

The study of the evolution of altruism goes beyond the description above, and I hope moviegoers won’t be seduced by this fictional account of evolutionary theory. (I’m waiting to see what demagoguery that AiG, DI, and the Expelled frauds come up with about this movie.) Now, it is true that according to Price’s Equation, altruistic behavior that benefits a species at the cost of individual fitness is selected against. (Note that a deleterious phenotype can still exist in a population through mutation-selection balance or genetic drift.) However, if the altruism only benefits certain members of the species (e.g. relatives), then altruism can be selected for.

This is represented by Hamilton’s rule: . This describes under what conditions an altruistic allele will invade a population. is the cost of the allele to the “actor”, is the relatedness of the receiver to the actor, and is the benefit that the receiver receives by the actor being altruistic. The consequence of Hamilton’s rule is that selfish genes can still be altruistic. There is a lot of interesting literature about the evolution of altruism, including how punishment can reinforce altruism. I recommend Sean Rice’s Evolutionary Theory, Chapter 10, as a good starting point.

So if anyone in the UK goes to see this movie this weekend, please send us an overview/review.

As promised, hot off the presses, here is a little tutorial I’ve decided to call Genetic Algorithms for Uncommonly Dense Software Engineers. Given some of the bizarre commentary issuing from the ID community over at Uncommon Descent regarding my past posts on Genetic Algorithms, I’ve developed this guide to help the folks over there figure out if the Genetic Algorithms (GAs) they are working on employ a “fixed-target” approach (like Dawkins’s Weasel), or if they are instead un-targeted, like most GAs used in research and industry are.

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They came for a contest that might someday be viewed as a pivotal moment in the eternal conflict between Darwin and Design.

On one side were the Intelligent Designers. They came from California and Alabama, New Mexico and England, Finland and the Netherlands, and from all around the world. They came from academia, and from industry, and from the armed services. They came armed with computer spreadsheets, home-made programs, graph paper and calculators. They applied trigonometry and calculus, intuition and insight, knowledge of minimal soap films and surface tension, database optimizing algorithms and random searches, and other techniques available only to Intelligent Designers. And they strived to answer the tricky question “What is the Steiner Tree (smallest possible network of straight line segments connecting six given points) for the network shown in “Take the Design Challenge!”

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On the other side were Evolutionary (or Genetic) Algorithms, in which herds of digital organisms were bred over many generations. Each organism was a string of numbers and letters, which were “transcribed” by fixed rules as representing some of the billions upon billions of possible candidate networks for the given problem. Those organisms whose lengths were smaller gained a slightly better chance at being a parent of one of the organisms of the next generation, and mutations of the strings were allowed to happen occasionally. In this process, no trigonometry or calculus was required. No information about characteristics of Steiner Trees was necessary. But, as the strings competed with each other, marvelous and unexpected designs began to appear.

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Although most of the Intelligent Designers were not members of the “Intelligent Design” movement, which had been officially invited to respond, the ID community did indeed weigh in, via the efforts of Salvador Cordova, one of the IDers running the show at William Dembski’s blog Uncommon Descent.

So, what is the Answer? Did Salvador do better than Darwin? Did our team of Intelligent Designers find the True Steiner, or did they, like the evolutionary algorithm, find “MacGyver” (not-quite-perfect-but-extremely-functional) solutions also?

Readers, let’s enter the Design Room and meet our Winners!

In July, I described a Genetic Algorithm that, unlike Dawkins’ “Weasel” experiment, specifies no fixed “Target” for the simulation, but instead rewards those members of the current population which use fewer or shorter segments to connect a fixed set of points. As the algorithm progresses, it finds a multitude of answers for the math problem called “Minimization of Steiner Trees,” i.e. the shortest possible straight-line networks connecting the fixed points.

Last Monday, I posted Take the Design Challenge, wherein I called for solutions to a tricky little 6-point network. Next Monday, I will announce the winners (there are 20 entries already, several with true Steiner Solutions, and others with almost-as-good “MacGyver” solutions).

Imagine my surprise, then, when I found Salvador Cordova at Uncommon Descent spewing blatant falsehoods about this work. I was shocked - shocked, I say - to catch the UD Software Engineers in a lie. And quite a lie it is - with the help of mathematicians like Carl Gauss, I’m going to lift the veil from the obfuscations of IDers, and prove it’s a Lie, much as you would prove a mathematical theorem.

Since posting my essay on Genetic Algorithms, I’ve since developed a brand-new C++ version of my Steiner Networks genetic algorithm, a vast improvement over the old Fortran number-clunker I developed five years ago.

And already, the new code is leading to some very interesting results.

In light of William Dembski’s remarks in No Free Lunch, basically arguing that in all Genetic Algorithms,

… the fitness function … is well-defined and readily supplies the complex specified information that an optimal crooked wire genetic antenna [or any other problem solved with Genetic Algorithms] seems to acquire for free,

I’m giving Intelligent Design proponents (and everyone else!) a chance to actually Design something!

As you recall, my algorithm involves finding Steiner Trees, the shortest networks of straight-line segments connecting a given collection of fixed points. These networks may include additional variable “Steiner Points” where segments may meet.

The Challenge Here is a collection of six fixed points. Designers, send your candidates for the Steiner Solution for this particular 6-point system to me at nmsrdaveATswcp.com (replace the AT with an @ if you please).

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I will announce the winners (if any) in a week’s time, and also will present the answer that Evolution came up with. I’m interested in proposed solutions from any and all (you don’t have to be in the ID camp), but am especially interested in solutions by ID advocates, since y’all are saying that the solution is already implicitly defined in the statement of the problem (finding shortest connected networks). Here’s a Hint: 4steiner.gif

Mathematicians and Evolution

Readers of this blog are doubtless familiar with the Discovery Institute's anemic list of scientists who “dissent from Darwinism.” The list is sadly short on biologists, forcing the DI to accept anyone with a PhD in any branch of science as a possible signatory.

Casey Luskin attempts to defend this practice by explaining why mathematicians are supremely well-placed to offer authoritative pronouncements on the merits of evolutionary theory.

Over at EvolutionBlog, I have replied to his desperate sputterings. In Part One I discuss the question of whether mathematicians, or non-biologists generally, have any authority to be discussing evolutionary theory. In Part Two I consider Luskin's thoughts on the matter. Comments can be left there. Enjoy!

Here follows the guts of my new C++ program for solving Steiner Tree problems with a Genetic Algorithm.

I have eliminated much of the Microsoft Foundation Class support code, focusing mainly on the number-crunching routines. I will be happy to share the complete code with interested parties.

The original FORTRAN version from five years ago is still online at NMSR.

You’ll see that I’ve cleaned up and organized everything quite a bit, and completely re-done the snippet which checks for properly connected solutions.

Dave August 21st, 2006

Genetic Algorithms are simplified simulations of evolution that often produce surprising and useful answers in their own right. Creationists and Intelligent Design proponents often criticize such algorithms for not generating true novelty, and claim that these mathematical recipes always sneak the “answer” into the program via the algorithm’s fitness testing functions.

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There’s a little problem with this claim, however. While some Genetic Algorithms, such as Richard Dawkin’s “Weasel” simulation, or the “Hello World” genetic algorithm discussed a few days ago on the Thumb, indeed include a precise description of the intended “Target” during “fitness testing” on of the numerical organisms being bred by the programmer, such precise specifications are normally only used for tutorial demonstrations rather than generation of true novelty.

In this post, I will present my research on a Genetic Algorithm I developed a few years ago, for the specific purpose of addressing the question Can Genetic Algorithms Succeed Without Precise “Targets”? For this investigation, I picked a math problem for which there is a single, specific answer, yet one for which several interesting “quasi-answers” - multiple “targets” - also exist.

PT readers, you are about to enter the Strange and Curious world of “The MacGyvers.” Buckle up your seat belts, folks - our ride through Fitness Landscapes could get a little bumpy.

I don’t know about you, but whenever I want to learn about information theory, I naturally turn to the creationists. Why, they know so much about geology, biology, and paleontology, it only seems reasonable that their expertise would extend to mathematics and computer science.

Take Nancy Pearcey, for example. Here, for example, we learn that Ms. Pearcey has studied philosophy, German, and and music at Iowa State; that she has a master’s degree in biblical studies; that she is a senior fellow at that temple of truth, the Discovery Institute; and that for nine years she worked with former Watergate conspirator and convicted criminal Charles Colson on his radio show, “Breakpoint”. Why, those seem exactly the sort of credentials one would want in an instructor of information theory…

Read more at Recursivity.

Paging Mr. Chromosome

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Forget prime numbers in the movie “Contact”, your own last name may be encoded in your DNA, reports Science

Paging Mr. Chromosome Your last name may be encoded in your DNA

A genetic study of British men finds a one in four chance that two strangers with the same last name share an ancestor. The relationship implies that certain surnames have a unique DNA signature–a fact that could help police narrow down suspects in some unsolved cases. But the criminally intent John Smiths of the world need not worry, because the signatures are found predominantly for rare surnames.

Now that’s a ‘Design Inference’

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