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I wouldn't say genetic algorithms are generic solutions. I have to try really hard to implement efficient operators that would allow me fast optimization. Scie

• TelmoMenezes 11y ago
  I find the situation a bit similar to neural networks. It's a bio-inspired model, and we know from nature that it can work spectacularly well. The actual algorithms that became popular (both in neural networks and evolutionary computation) are extreme simplifications of the biological reality. So a lot of the research goes in the direction of finding the "missing ingredients". On the evolutionary computation front, one of the topics I find the most interesting is the genotype-phenotype mapping. Naive genetic algorithms have genotypes that map directly to phenotypes (e.g you are evolving some vector which is a direct solution to a problem). More complex mapping (like what we see in nature) encode a generator for a solution. On this note, exciting stuff happens on the genetic programming front (where you are evolving computer programs, commonly Lisp-like trees). It really is a vast field, ranging from direct engineering applications to artificial life simulations aimed at theoretical biology. Like everything else, you have to be familiar with the literature.
  • spooningtamarin 11y ago
    I'm familiar with the literature. I do not like the research. It is bad, it's rarely reproducible, source code is almost never public thus time/convergence measurements are meaningless.