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What designs for genetic algorithms are there, if they are classified differently and/or have different names, that leverage models for epigenetics in evolution? What are the pros/cons of the designs? Are there vast insufficiencies or wide-open questions about their usefulness?

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    $\begingroup$ This seems a perfectly reasonable question to me - it's well defined and the answer within mainstream GA research is 'yes'. $\endgroup$ – NietzscheanAI Aug 4 '16 at 5:10
  • $\begingroup$ Not sure how the meta link or help recommendations apply; this is not a programming question but a request for high-level information about a specific area of artificial intelligence research. Edited it anyway. Hope this is a more useful question. $\endgroup$ – dynrepsys Aug 4 '16 at 14:53
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Genetic algorithms are an analogy to biology, not a copy of it. The core piece of the analogy is that the "phenotype", or the observable portion of a solution, is constructed from the "genotype", or the internal portion of a solution.

For example, a number (the phenotype) can be stored as a binary series of "0"s and "1"s (the genotype), and by changing individual bits we make potentially dramatic changes in the resulting number, and by combining two genotypes we can get a broad range of 'related' numbers.

Epigenetics are wrinkles in the genotype -> phenotype mapping, making it a non-deterministic function, and so incorporating them would degrade the performance of a genetic algorithm by adding unnecessary noise.

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  • $\begingroup$ Ok, but if our genetic algorithms are far less effective than their analogs in biology, then isn't it reasonable for there to be efforts to improve the fidelity of the analogy? $\endgroup$ – dynrepsys Aug 2 '16 at 21:07
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    $\begingroup$ @dynrepsys: Are our genetic algorithms far less effective than their analogs? How would we know? $\endgroup$ – Matthew Graves Aug 2 '16 at 21:14
  • $\begingroup$ I guess the burden is on proving they are as effective, rather than proving the negative. Are there any genetic algorithms that are able to generate a surviving gene in an information space comparable to a small biological system over an equivalent number of generations with a reasonable cost function? I don't know of any but I'm open to the possibility that I'm just uninformed. $\endgroup$ – dynrepsys Aug 2 '16 at 21:33
  • $\begingroup$ "an information space comparable to a small biological system over an equivalent number of generations with a reasonable cost function?" - computationally that is HUGE. The biological space of a DNA genome is explored via physical function of proteins. Even calculating protein folding for a single gene is a supercomputer problem taking many hours of processing. And that says very little about its function in a chemical/physical environment, how it interacts with 1000s of other proteins in different concentrations. Software GAs are tiny search engines in comparison to biology of life. $\endgroup$ – Neil Slater Apr 12 '18 at 6:13
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Over the last few years, evolutionary computation research has shown increasing interest in including some aspect of epigenetics. For example:

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