He isn’t goign to give you his thoughts on that link GGG because computer algorithms DO NOT mimic nature in any way shape or form- they are intelligently designed, carefully controlled environments(despite their claism of ‘randomness’), carefully controlled processes that are simply NOT found in nature
Be sure to give this link a read too as it points out the ludicrous idea that computer models simulate some supposed imaginary evolutionary process:
http://www.trueorigin.org/schneider.asp
Unlike the fictitious positive and negative integers used in the simulation, in earlier papers the weight matrix was derived using real data on functional sites(10) [143]. Known binding sites were selected from genbank, lined up and the proportion of each of the 4 bases found at each position of a sequence was determined (see Appendix).
Binding of a protein to DNA or RNA is rarely the simple matter implied by the computer program, but generally requires cooperation with other carefully crafted proteins(11). For example, transcription in eukaroytes is regulated by a group of gene-specific activator and repressor proteins[24] at specific binding sites. Simulating the production of one recognizer member of such ensembles by random point mutations has not been justified nor validated as being biologically conceivable. Instead, an arbitrary proportion of positive and negative integers in the computer program defined how to converge towards a short term goal flawlessly irrespective of any biological selective significance or stochastic effects.
How is chance to know a random mutation would lead towards developing a binding interaction? ‘Rsequence does not tell us anything about the physical mechanism a recognizer uses to contact the nucleic acid.’ [25]
Lacking any intelligence to choose, 3 dimensional shapes on the regulatory protein must be generated to permit the exact binding with a specific DNA sequence, like a well-meshed machine. That is why a methionine-carrying tNRA is able to identify a very short sequence on mRNA, AUG, and position a physically large m-RNA properly at the ribosome complex: it is due to the specialized geometry prepared at the ribosome’s P site. There is nothing biologically remarkable about AUG alone. Crystallographic, molecular modelling and cryo-electron microscopy studies have shed insight as how such feats are possible. Translating an mRNA strand one codon at a time requires the whole ribosome complex to act in a synchronized fashion, aptly described as a rachet-like mechanism[26]. The cell’s survival depends on ribosomes being able to locate the binding sites correctly [27](12).
Exactly how polypeptides are supposed to be able to identify that a location is or will become a useful binding site is deemed irrelevant: ‘As mentioned above, the exact form of the recognition mechanism is immaterial because of the generality of information theory.’ [1] Quite the contrary, for a realistic evolutionary simulation such physical details are critically relevant, and is a fatal oversight in the simulation. It is assumed random point mutations provide half the 64 member population with a 100% effective survival advantage, based on fine tuning of a single type of binding site under development. This is geometrically and thermodynamically unrealistic. Developing such precise binding interactions, one random mutation at a time, has nothing to do with the mathematics of information theory and needs to be quantitatively simulated based on physical realities. Any assumption of recognizable Darwinian selectivity for the intermediate stages needs to be quantitatively justified.
The requirements on recognizer and binding site are generally very stringent a must be close to perfect to be of any use whatsoever [All Emphasis' added by me]
The whole article is very itneresting and exposes just how NON RANDOM these supposed 'random evolutionary models' really are- it takes a great deal of intelligent design infact, and unrealistic biological scenarios, to 'make htese systems work' as 'nature intended' naturalism to function lol
I gave an answer; the article that GGG referenced was nonsensical, as is the Schneider paper you reference. Both articles clearly show a fundamental LACK of understanding of what a Genetic Algorithm is.
If you’d like to really learn - from a pure computer science standpoint - what a GA really is, then I can suggest some excellent textbooks for you. Of course, it does mean you’ll need to just put on your math and programming hats and drop your theological lenses.
Of course, since the Bible makes no mention of computers and programming, perhaps anything to do with either is witchcraft? Are you allowed to even use computers and programs?
But the mathematical models that are used in support of "irreducible complexity" are perfect.
Thanks, I love reading Royal Truman (and his oft co-auther, Peter Borger). I will read it today, after the Renaissance Fair!
All the best—GGG