I'm not a biologist, but if I remember correctly from my AI class, a Genetic Algorithm always has a fixed number of variables (similar to a fixed DNA length/number of genes in a given species). You, the programmer, still has to determine which variable adresses which part of the problem to be solved, and how (similar to gene sequencing?).
So a GA that is supposed to distinguish between two signals, for instance, can't evolve all on its own into a solution for a different "problem", e.g. eliminating all those pesky humans running around. GA is just a technique for obtaining a better solution for a given problem from less ideal solutions. Rather similar to a farmer breeding cows with larger milk output/better feed utilisation from existing cattle, and quite unlike a frog evolving into a prince or some such thing:-).
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I'm not a biologist, but if I remember correctly from my AI class, a Genetic Algorithm always has a fixed number of variables (similar to a fixed DNA length/number of genes in a given species). You, the programmer, still has to determine which variable adresses which part of the problem to be solved, and how (similar to gene sequencing?). So a GA that is supposed to distinguish between two signals, for instance, can't evolve all on its own into a solution for a different "problem", e.g. eliminating all those pesky humans running around. GA is just a technique for obtaining a better solution for a given problem from less ideal solutions. Rather similar to a farmer breeding cows with larger milk output/better feed utilisation from existing cattle, and quite unlike a frog evolving into a prince or some such thing :-).