Author: Dan Homan
Date: 06:10:32 05/23/00
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On May 23, 2000 at 03:59:50, Tom Kerrigan wrote: >Hi guys, > >Is there a good way to measure search efficiency? > >In the past, I've gone through test suite logs and compared nodes/ply for each >problem by hand. This is obviously undesirable. :) > >Is there some way I can get a good "magic number" to indicate how efficient my >search is? Is branching factor good or bad? Is there something similar but >better? > >Thanks, >Tom I don't know if this is what you are looking for, but a year or so ago on this group, Bruce Moreland suggested comparing graphs of solution times and ply depth reached as a measure of search improvements. I liked this idea but decided to just use mean ply depth and root mean square (RMS) solution time instead. The mean ply depth for a test suite is nice because it tells me about how deep my program is going in a fixed period of time (I throw Mates out of the average because they cut the search time short). The RMS solution time is also nice because I want my program to find the best move as quickly as possible. I use the RMS solution time because it weights up the problems that take the longest to solve. To optimize my search I try to get the best mean ply depth and RMS solution time compromise on test suites. If I turn up extensions, I usually can reduce the RMS solution time significantly (particularly on a tactical suite), but the mean ply depth goes down significantly as well. If I increase pruning, my mean ply depth goes up significantly, but my RMS solution time also goes up (and some previously solved problems go unsolved). I think the two measures are simple and off-setting which makes them useful to me. - Dan
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