i recommend:

http://www.research.att.com/~njas/gosset/index.html

s.


----- Original Message ----
From: "[EMAIL PROTECTED]" <[EMAIL PROTECTED]>
To: computer-go <[email protected]>
Sent: Saturday, January 26, 2008 2:35:01 PM
Subject: Re: [computer-go] 19x19 MC improvement


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.

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