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> Also, I found these algorithms could always go more and more meta. Haha, I've had this same thought before. I wrote a genetic algorithm to work on the spher
by Xcelerate 13y ago
> Also, I found these algorithms could always go more and more meta.
Haha, I've had this same thought before. I wrote a genetic algorithm to work on the sphere packing problem (in a periodic box) and I kept thinking about how I could mutate the settings within the program and optimize them, and then mutate the rate at which those settings themselves changed, and so on. I wonder if there's an optimal point to stop being "meta"?
- gknoy 13y agoWe could probably find it with some kind of Monte-Carlo technique...
- darkmighty 13y agoThere are information theoretic aspects behind this. You're essentially searching the design space for the fittest and want a fast way to get your answer -- this 'fastest way' is related to the way that 'grants' maximum information. I say this because this reminded me of a wonderful (and acessible) discussion I once glanced over on the following book: http://www.cs.toronto.edu/~mackay/itprnn/book.pdf http://www.cs.toronto.edu/~mackay/itprnn/book.pdf (Page 269, pdf 281) -- it offers answers to this sort of question :)
- randomsearch 13y agoYou're looking for "hyper-heuristics." So called, because unfortunately GAs etc. had already taken the name "meta-heuristics".
- tripzilch 13y ago> I wonder if there's an optimal point to stop being "meta"? Perhaps this will help your thinking in the right direction: http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_opt...