Eric Boesch This is probably massive overkill, but one of the most successful techniques for multi-parameter optimization is Taguchi methods.
http://en.wikipedia.org/wiki/Taguchi_methods However, in my experience, starting with the decisions that make the biggest difference, and then adding in the next-biggest decision, and so on, usually gets close enough to optimal. In my cgbg performance tests, most reasonable decisions affect total program performance by a factor of 2 at most, which translates to 1 rank at most. Likewise, I doubt that tweaking of existing MC parameters will bring more than 1 rank. Further improvement will require new ideas. Michael Wing > By the way, does anybody know of any nifty tools or heuristics for > efficient probabilistic multi-parameter optimization? In other words, > like multi-dimensional optimization, except instead of your function > returning a deterministic value, it returns the result of a Bernoulli > trial, and the heuristic uses those trial results to converge as > rapidly as possible to parameter values that roughly maximize the > success probability. The obvious approach is to cycle through all > dimensions in sequence, treating it as a one-dimensional optimization > problem -- though the best way to optimize in one dimension isn't > obvious to me either -- but just as with the deterministic version of > optimization, I assume it's possible to do better than that. It might > be fun problem to play with, but if good tools already exist then it > wouldn't be very productive for me to waste time reinventing the > broken wheel. _______________________________________________ computer-go mailing list [email protected] http://www.computer-go.org/mailman/listinfo/computer-go/
