Evolution is often presented as problem-solving. Genetic algorithms are often offered as proofs of evolution’s ability to solve problems. Genetic algorithms are as search algorithms.
As one book says:
Fundamentally, all evolutionary algorithms can be viewed as search algorithms which search through a set of possible solutions looking for the best – or “fittest” – solution.
Tom has asked me to specify a problem independently from the evolutionary process. Now I have to admit that I don’t really understand what that means. But I like Tom and I have a lot of respect for him, so I want to give it my best shot and see where it takes us. I’m also hoping this will shed some light on claims about how problem-solving genetic algorithms are designed to solve a particular problem.
Purpose and Desire: What Makes Something “Alive” and Why Modern Darwinism Has Failed to Explain It is the new book by physiologist J. Scott Turner, author of The Tinkerer’s Accomplice: How Design Emerges from Life Itself.
The book may make some “skeptics” uncomfortable, but maybe they should read it anyways.
From the book:
I have come to believe that there is something presently wrong with how we scientists think about life, its existence, its origins, and its evolution.
Without a coherent theory of life, whatever we think about life doesn’t hold water. This applies to the major contribution we claim that the modern science of life offers to the popular culture: Darwinism.
… there sits at the heart of modern Darwinism an unresolved tautology that undermines its validity.
… do we have a coherent theory of evolution? The firmly settled answer to this question is supposed to be “yes” …
I intend to argue in this book that the answer to my question might actually be “no.”
Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by optimization or evolutionary algorithms. The purpose of this chapter is to provide a foundational understanding of coevolutionary algorithms and to highlight critical theoretical and empirical work done over the last two decades. This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it.
Handbook of Natural Computing
I tire of keiths and his revisionist history. In a recent thread…
Glen: The real question is if Mung has read and comprehended Losos’ book.
keiths: Yes, which brings to mind what happened with Andreas Wagner’s book, Arrival of the Fittest. Mung was blathering about how it was an ID-friendly book, which is nonsense.
I challenged him:
Alan’s review barely touches on what I think are the most important ideas in the book: those concerning the “libraries”, the “networks”, and the extent to which the networks extend across the libraries.
How about summarizing those ideas for us in your own words? That will serve the dual purpose of 1) filling a gap in Alan’s review and 2) demonstrating that you actually understand what Wagner is saying.
Having summarized those ideas, if you still don’t (or pretend not to) understand the implications for ID, I’ll help you out.
Think of it as being similar to an ideological Turing test. I’d like to see if you even bothered, or were able, to understand the book before dismissing it as no threat to ID.
keiths: To no one’s surprise, Mung squirmed, stalled, and then skedaddled.
Here’s what actually happened:
Improbable Destinies: Fate, Chance, and the Future of Evolution
I love books like this. Pure wonder about the living world. The beauty. The mystery. Shattering the myths of Darwinism while still clinging desperately to them.
We learn that Darwinism has retarded evolutionary thought for at least a century because the picture that Darwin gave us (which his disciples followed for over a hundred years) was false. Evolution can be tested. It can be observed within human lifetimes. It doesn’t require the infinitesimal insensible aggregations over millenia previously thought. Evolution can be really really fast. Which ought to be good news for young earth creationists.
We also learn that the oft-heard claim that degree of similarity implies degree of relatedness is false. That some species A looks very much like some species B doesn’t at all mean that they are more closely related than some other species which is visibly different.
Another nail in the coffin.
I come from the Michael Behe school if ID. I accept common descent, by which I mean universal common ancestry. It seems to be the consensus view in science, it seems reasonable to me, and I don’t have any compelling reasons to doubt it.
But could I actually defend my belief in common ancestry if asked? All living organisms share certain features in common. Organisms leave offspring. Therefore, universal common ancestry. That sounds pretty weak, I admit. I need to do better.
I don’t believe that new organisms appear out of thin air, so to speak. I accept that the organisms of today are the offspring of prior organisms. Even young earth creationists accept common descent prior to the flood and common decent after the flood, though they resist the idea of universal common ancestry. I still haven’t figured out how and where they draw the lines though, so why should I draw a line that I cannot defend. Therefore common ancestry. That still sounds pretty weak, I admit. I still need to do better.
First post here at TSZ: Where does information come from? July 27, 2011.
In that light this post is about a month late. But please express your thanks to Elizabeth for hosting this blog.
There’s been some debate here at TSZ recently about probability and the interpretation of probability.
I took some flak (my personal subjective opinion) for attempting to distinguish between calculating probabilities and estimating probabilities.
Yet in recent reading I came across this bit of text:
How do you determine the probability that a given event will occur? There are two ways: You can calculate it theoretically, or you can estimate it experimentally by performing a large number of trials.
– Probability: For the Enthusiastic Beginning. p. 335
In Why Evolution is True, Jerry Coyne writes that gradualism is one of the six tenets of “the modern theory of evolution” (which he equates with Darwinism – see page 3).
Eugene Koonin writes that the tenet of gradualism is known to be false (The Logic of Chance p. 398).
Yet gradualism is obviously still quite popular here in “The Skeptical Zone.”
Surely gradualism is not a logical requirement or entailment of the theory of evolution. Neither is it supported by the evidence.
I’m all in favor of mocking stupidity, and here’s something definitely worth mocking.
In arguing for evolution, author Alan R. Rogers appeals to the Nilsson and Pelger paper on how simple it is to evolve an eye. He writes:
If eyes evolve, they must do so often and easily. Could it really be so easy?
Dan-Eric Nilsson and Susanne Pelger have answered this question. They constructed an evolutionary story much like the one that I told above.
– The Evidence for Evolution. p. 42.