Genetic algorithms—do they show that evolution works?
by Don Batten
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:
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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.
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A GA can only select for a very limited number of traits. 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.
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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.
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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.).
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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.
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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.
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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).
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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.
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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. With genetic algorithms,
the program itself is protected from mutations; only target sequences are mutated.
Indeed, if it were not quarantined from mutations, the program would very quickly
crash. However, the reproduction machinery of an organism is not protected from
mutations.
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There is no problem of irreducible complexity with GAs (see Behe’s
Darwin’s Black Box). Many biological traits require many
different components to be present, functioning together, for the trait to exist
at all (e.g. protein synthesis, DNA replication, reproduction of a cell, blood clotting,
every metabolic pathway, etc.).
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Polygeny (where a trait is determined by the combined action of more than one gene)
and pleiotropy (where one gene can affect several different traits) are ignored.
Furthermore, recessive genes are ignored (recessive genes cannot be selected for
unless present as a pair; i.e. homozygous), which multiplies the number of generations
needed to get a new trait established in a population. The problem of recessive
genes leads to one facet of Haldane’s Dilemma, where the well-known evolutionist
J.B.S. Haldane pointed out that, based on the theorems of population genetics, there
has not been enough time for the sexual organisms with low reproductive rates and
long generation times to evolve. See review of ReMine’s
analysis of Haldane’s Dilemma.
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Multiple coding genes are ignored. From the human genome project, it appears that,
on average, each gene codes for at least three different proteins (see
Genome Mania — Deciphering the human genome. In microbes, genes have
been discovered that code for one protein when ‘read’ in one direction
and a different protein when read backwards, or when the ‘reading’ starts
one letter on. Creating a GA to generate such information-dense coding would seem
to be out of the question. Such demands an intelligence vastly superior to human
beings for its creation.
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The outcome in a GA is ‘pre-ordained’. Evolution is by definition purposeless,
so no computer program that has a pre-determined goal can simulate it—period.
This is blatantly true of Dawkins’ ‘weasel’ program, where the
selection of each letter sequence is determined entirely on its match with the pre-programmed
goal sequence. Perhaps if the programmer could come up with a program that
allowed anything to happen and then measured the survivability of the ‘organisms’,
it might be getting closer to what evolution is supposed to do! Of course that is
impossible (as is evolution).
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With a particular GA, we need to ask how much of the ‘information’ generated
by the program is actually specified in the program, rather than being generated
de novo. A number of modules or subroutines are normally specified in the
program, and the ways these can interact is also specified. The GA program finds
the best combinations of modules and the best ways of interacting them. The amount
of new information generated is usually quite trivial, even with all the artificial
constraints designed to make the GA work.
For the above reasons (and some of them overlap), and no doubt there are more that
could be added, GAs do not validate evolution. It does not take long with a decent
calculator to see that the information space available for a minimal real world
organism of just several hundred proteins is so huge that no naturalistic iterative
real world process could have accounted for it—or even the development of
a new protein with a new trait.
Another type of ‘simulation’ is that of antitheist T.D. Schneider.1 Schneider claims that his
program simulates the naturalistic formation of DNA binding sites for gene control.
This exercise has led to grandstanding by some evolutionists that this proves creationists
wrong. However, many of the same problems outlined above also apply to this programming
exercise. For example, the selection coefficient is extremely high, the genome is
extremely small, the mutation rate high, no possibility of extinction is permitted,
etc. For many other problems, see the
critique by Dr Royal Truman.
Note that we are not saying that mutations and natural selection cannot
generate information (see Spetner’s book, Not by Chance for example).
It’s just that with real world generation times, real-world sized genomes
and real-world organisms which have to survive through multi-dimensional
adaptive traits, there has not been enough time to generate even a tiny amount of
the biological information seen in living things. As Spetner says, look, if mutations
and natural selection have generated all the information we see, then we should
be able to easily find some examples of some new information (i.e. increase in specified
complexity) arising today. No one has yet found one. The best that anyone has come
up with is a GA, which does not simulate real world evolution, for the
reasons outlined above.
Reference
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Schneider, T.D., Evolution of biological information, Nucleic
Acids Research 28(14):2794–2799, 2000. In this paper,
Schneider acknowledges the input of fellow atheist Richard Dawkins. Return
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