Genetic Algorithms for Evolving Computer Chess Programs

被引：24

作者：

David, Omid E. ^{[1
]}

van den Herik, H. Jaap ^{[2
]}

Koppel, Moshe ^{[1
]}

Netanyahu, Nathan S. ^{[1
,3
]}

机构：

[1] Bar Ilan Univ, Dept Comp Sci, IL-52900 Ramat Gan, Israel

[2] Tilburg Univ, Tilburg Ctr Cognit & Commun, NL-5037 AB Tilburg, Netherlands

[3] Univ Maryland, Ctr Automat Res, College Pk, MD 20742 USA

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2014年 / 18卷 / 05期

关键词：

Computer chess; fitness evaluation; games; genetic algorithms; parameter tuning; NEURAL-NETWORKS; EVOLUTION; SEARCH; CHECKERS;

D O I：

10.1109/TEVC.2013.2285111

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values of which are initialized randomly. The evaluation function of the program is evolved by learning from databases of (human) grandmaster games. At first, the organisms are evolved to mimic the behavior of human grandmasters, and then these organisms are further improved upon by means of coevolution. The search mechanism is evolved by learning from tactical test suites. Our results show that the evolved program outperforms a two-time world computer chess champion and is at par with the other leading computer chess programs.

引用

页码：779 / 789

页数：11

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