Author: Volker Böhm
Date: 23:23:02 07/06/04
Go up one level in this thread
On July 06, 2004 at 14:16:37, Robert Hyatt wrote: >On July 06, 2004 at 14:09:39, Stuart Cracraft wrote: > >>Okay here's a new table with revisions based >>on input. Now, higher effort and return numbers >>mean more effort and return. And gone is the >>value number replaced with Russell's return/effort >>ratio. Have revised some numbers to try to fit in >>with some opinions. >> >>The higher the third column the more that feature >>gives in program performance for less work expended. >> >>EFFORT RETURN RETURN/EFFORT Feature >>1 5 5 capture ordering >>3 4 1.33 null move >>4 4 1 null move with verification >>4 4 1 search pv first >>4 5 1.25 static exchange evaluator > >This can make a big difference. IE once you have a SEE procedure working, you >can use it to reduce the size of the q-search by over 50%, doubling your search >speed instantly. At very low risk. I get other results in tests with our engine (spike). I tested two types of q-search pruning: 1. Pruning if eval + piece-to-hit-value + margin < alpha 2. SEE based pruning Pruning type 1 will only work if you have a "fail-soft" lazy-eval with not too high margins or no lazy-eval. Results: In all three types the reduction is about 1/3 of total nodes. I tested the amount of wrong prunings done by the types of pruning. Type 1 with small margin and no lazy eval: Test of 24 MNodes gives 8 MNodes reduction and 13K Errors, with many not seen mates in 1. Type 2: Test of 24 MNodes gives 8 MNodes reduction and 10K Errors, no problems with mates but sometimes don´t see very high material loss. Improvements: Type 1: With lazy-eval (there is a corellation betwenn lazy-eval margins and type 1 pruning) and no pruning of checking captures. Test of 24 MNodes gives 5,5 MNodes reduction and 6 errors (errors caused by pawns hitting to row 7 or beeing hit on row 7). Game results with the Type 1 with "improvements" are on average 10-15% better against other engines than Type 2 prunings (5 Min/Game on Celeron 1,3 GHZ). Perhaps there are good rules to improve SEE pruning (giving less errors). I haven´t found them yet. Greetings > > > >>4 5 1.25 transposition table >>4 5 1.25 transposition table with 2-tier replacement >>3 3 1 history heuristic, killers, other ordering >>2 1 .5 aspiration >>2 2 1 iterative deepening >>2 2 1 pawn hashing w/ complex pawn evaluation >>3 2 .66 capture extension >>1 5 5 check extension >>1 1 1 pawn to 6th/7th extension >>3 3 1 futility >>3 2 .66 razoring >>5 3 .6 mate-at-a-glance
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