Human Devolution

[Pulse]

There is controversy regarding GA, as Will Larson expressed in his article Genetic Programming: A Novel Failure. 

Just had a quick scan of this. Genetic programming is not the same as a genetic algorithm.

Genetic programming is an idea for using GAs to create computer programs. That wasn't very successful for many different reasons. GAs are used for so many more things. For example for creating neural networks, electronic system diagrams, aerofoils for use on planes etc. They are simply a computational search based on an understanding of evolution.

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 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.

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.

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 a rate of about one in a billion per cell division.

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. 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.

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.).

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.

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.

The outcome with a GA is ‘pre-ordained’ (‘formal’). Evolution is by definition purposeless, so no computer program that has a pre-determined goal can simulate it—period. This is most obviously true of Dawkins’ ‘weasel’ program, where the selection of each letter sequence is determined entirely on its match with the pre-programmed goal sequence (see further reading below). That GAs are not valid simulations of evolution because of this fundamental problem has been acknowledged—see this 2009 quote. Perhaps if the programmer could come up with a program that allowed any random change 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).

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.


Please google The Problem Of Information For The Theory Of Evolution Has Tom Schneider Really Solved It? For more critiques of GA.

---

Evolutionary theory requires some mutations to go ‘uphill’—to add new information.



The mutations which we observe are generally neutral (they don’t effectively change the information, or the ‘meaning’ in the code) or else they are informationally downhill—defects which lose/corrupt information.



The rare ‘beneficial’ mutations to which evolutionists cling all appear to be like wingless animals, blind cave animals, and many examples of antibiotic resistance. They are downhill changes, losses of information which, though they may give a survival advantage, are headed in precisely the wrong direction for evolution.



The examples commonly cited as ‘evolution happening today’ usually involving adaptation by natural selection, are without exception instances in which the net result is a loss of information in the population—either by mutation or by way of reduced genetic variety.



All of our real-world experience, especially in the ‘information age’, would indicate that to rely on accidental copying mistakes to generate real information is the stuff of wishful thinking by ‘true believers’, not science.


Please google Has evolution really been observed? on the Creation dot com website for much more evidence which you will obviously ignore point blank, with no credible rebuttal of any type whatsoever.

Another citation from a creationist website with absolutely no scientific credibility.

Once again I ask, please provide scientific, peer reviewed, evidence for your claims.

Please read the Book Genetic Entropy by Prof John Sanford, he has copious links to peer reviewed journals; 

Here's a few to whet your appetite, but despite your request, I really doubt you'll bother, I've seen so many atheists brush aside evidence, but that's an insincere search for Truth and Intellectual laziness, wouldn't you agree?

The most definitive findings were published in 2010 in the Proceedings of the National Academy of Science by Lynch. That paper indicates human fitness is declining at 3–5% per generation. 

Crow, J., The high spontaneous mutation rate: Is it a health risk? Proceedings of the National Academy of Sciences 94(16):8380–8386,1997; pnas.org/content/94/16/8380.

Lynch, M., Rate, molecular spectrum, and consequences of human mutation, Proceedings of the National Academy of Sciences 107(3):961–968, 2010

Kondrashov, A., Contamination of the genome by very slightly deleterious mutations: why have we not died 100 times over? Journal of Theoretical Biology 175(4):583–594, 1995


Carter, R. and Sanford, J., A new look at an old virus: patterns of mutation accumulation in the human H1N1 influenza virus since 1918, Theoretical Biology and Medical Modelling 9(42):1–19, 2012.

See Sanford, J. and Nelson, C., The Next Step in Understanding Population Dynamics: Comprehensive Numerical Simulation, Chapter 7 (pages 117–135) in: Carmen Fusté, M. (Ed.), Studies in Population Genetics, InTech, 2012.