Do genetic algorithms show that evolution works?
Fri Jul 13, 2018 1:13pm

"A genetic algorithm (GA) is a computer program that supposedly simulates biological evolution. GAs have found limited application in generating novel engineering solutions—for example, an electronic circuit that filters out a particular frequency. GAs use mathematical constructs that parallel mutations (random changes in the variables/coefficients), natural selection (elimination of variations in a circuit, for example, that do not move toward the objective of a response to a particular frequency), and even some type of ‘recombination’ (as happens in sexual reproduction). Because of this, some apologists for evolution claim that these programs show that biological evolution can create the information needed to proceed from less complex to more complex organisms (i.e. with more genetic information).

However, GAs do not mimic or simulate biological evolution because with a GA:

•A ‘trait’ can only be quantitative so that any move towards the objective can be selected for. Many biological traits are qualitative—it either works or it does not, so there is no step-wise means of getting from no function to the function.

•A single trait is selected for, whereas any living thing is multidimensional. A GA will not work with three or four different objectives, or I dare say even just two. A GA does not test for survival; it tests for only a single trait. Even with the simplest bacteria, which are not at all simple, hundreds of traits have to be present for it to be viable (survive); selection has to operate on all traits that affect survival.

•Something always survives to carry on the process. There is no rule in evolution that says that some organism(s) in the evolving population will remain viable no matter what mutations occur. In fact, the GAs that I have looked at artificially preserve the best of the previous generation and protect it from mutations or recombination in case nothing better is produced in the next iteration. This has a ratchet effect that ensures that the GA will generate the desired outcome—any move in the right direction is protected. This is certainly the case with Dawkins’ (in)famous ‘Weasel’ simulation—see Weasel Words and Dawkins’ weasel revisited.

•Perfect selection (selection coefficient, s = 1.0) is often applied so that in each generation only the best survives to ‘reproduce’ to produce the next generation. In the real world, selection coefficients of 0.01 or less are considered realistic, in which case it would take many generations for an information-adding mutation to permeate through a population. Putting it another way, the cost of substitution is ignored (see ReMine’s The Biotic Message for a thorough run-down of this, which is completely ignored in GAs—see Population genetics, Haldane’s Dilemma, etc.).

•The flip side to this is that high rates of ‘reproduction’ are used. Bacteria can only double their numbers per generation. Many ‘higher’ organisms can only do a little better, but GAs commonly produce 100s or 1000s of ‘offspring’ per generation. For example, if a population of 1,000 bacteria had only one survivor (999 died), then it would take 10 generations to get back to 1,000.

•Generation time is ignored. A generation can happen in a computer in microseconds whereas even the best bacteria take about 20 minutes. Multicellular organisms have far longer generation times.

•The mutation rate is artificially high (by many orders of magnitude). This is sustainable because the ‘genome’ is small (see next point) and artificial rules are invoked to protect the best ‘organism’ from mutations, for example. Such mutation rates in real organisms would result in all the offspring being non-viable (error catastrophe). This is why living things have exquisitely designed editing machinery to minimize copying errors to the rate of one in about 10 billion (for humans).

•The ‘genome’ is artificially small and only does one thing. The smallest real world genome is over 0.5 million base pairs (and it is an obligate parasite, which depends on its host for many of the substrates needed) with several hundred proteins coded. This is equivalent to over a million bits of information. Even if a GA generated 1800 bits of real information, as one of the commonly-touted ones claims, that is equivalent to maybe one small enzyme—and that was achieved with totally artificial mutation rates, generation times, selection coefficients, etc., etc. In fact, this is also how the body’s immune system develops specific antibodies, with these designed conditions totally different to any whole organism. This is pointed out in more detail by biophysicist Dr. Lee Spetner in his refutation of a skeptic.

•In real organisms, mutations occur throughout the genome, not just in a gene or section that specifies a given trait. This means that all the deleterious changes to other traits have to be eliminated along with selecting for the rare desirable changes in the trait being selected for. This is ignored in GAs."

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