[Computer-go] CLOP: Confident LocalOptimization forNoisyBlack-Box Parameter Tuning

Brian Sheppard sheppardco at aol.com
Sun Oct 9 08:29:54 PDT 2011

I changed my implementation to use a Glicko rating system, which
incorporates a Gaussian prior, and now results are much better. My
implementation seems to be getting the same general results as published in
your paper.

The prior works particularly well for game applications, IMO, because it
sets the expectation that there is a way to set parameters that win at least
50%. In a well-designed game experiment that will always be the case. (E.g.,
at worst you can turn your new feature off by setting a weight to 0, or

But I am curious: have you ever run a test of Correlated5? Your paper has
results for Correlated2 and Rosenbrock5, but not Correlated5.

When I run Correlated5, the run can get stuck in a non-improving state for a
very long time. I don't have the patience to wait it out, so I don't know if
it will eventually detect a gradient.


>I deal with +INF/-INF with a prior: the Gaussian prior regularizes the
regression, so its tends to remain flat and close to 0.5 when very few
samples have been colle

More information about the Computer-go mailing list