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extracensorypower t1_jan4yel wrote

I think they're still useful for "no information at all" scenarios where attempting a solution is just too time consuming or not possible using other methods (e.g. traveling salesman problem).

As a practical matter, I think they're best integrated with other methods as "first cut" solutions that get you closer to something you can work out with a neural net or rule based system.

That said, I'm unaware of any NN or rule based solution better than a GA for solving the traveling salesman problem even now. So, maybe some P-NP problems will always be best attacked with GAs.

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TobusFire OP t1_janzia9 wrote

Agreed. That being said, I think the prior is that you still need to have enough understanding of the state space to be able to design good mutations, cross-over, and fitness. This can easily add a lot of overhead. In contrast, I think that other cool methods like swarm optimization and ant colony optimization are also promising and in some ways simpler.

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extracensorypower t1_jao08hm wrote

Hmm. Hadn't thought of that, but that's probably true. I'm less familiar with the mechanics of these but I bet they'll be similarly good for low or no-information scenarios or P-NP problems.

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