History mechanism supported differential evolution for chess evaluation function tuning

被引：14

作者：

Boskovic, B. ^{[1
]}

Brest, J. ^{[1
]}

Zamuda, A. ^{[1
]}

Greiner, S. ^{[1
]}

Zumer, V. ^{[1
]}

机构：

[1] Univ Maribor, Fac Elect Engn & Comp Sci, SLO-2000 Maribor, Slovenia

来源：

SOFT COMPUTING | 2011年 / 15卷 / 04期

关键词：

Chess evaluation function tuning; Differential evolution; History mechanism; Opposition-based optimization; STATISTICAL COMPARISONS; CLASSIFIERS; CHECKERS; GAME;

D O I：

10.1007/s00500-010-0593-z

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 presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process. The general idea is based on individual evaluations according to played games through several generations and different environments. We introduce a new history mechanism which uses an auxiliary population containing good individuals. This new mechanism ensures that good individuals remain within the evolutionary process, even though they died several generations back and later can be brought back into the evolutionary process. In such a manner the evaluation of individuals is improved and consequently the whole tuning process.

引用

页码：667 / 683

页数：17

共 48 条

[1] Evaluation tuning for computer chess: Linear discriminant methods [J].

Anantharaman, TS .

ICCA JOURNAL, 1997, 20 (04) :224-242

[2]

[Anonymous], 2006 IEEE C EV COMP

[3]

[Anonymous], SCALABLE SEARCH COMP

[4]

[Anonymous], THESIS U PADERBORN G

[5]

[Anonymous], 1995, Tech. Rep. TR-95-012

[6]

[Anonymous], MACHINES LEARN PLAY

[7]

[Anonymous], THESIS CARNEGIE MELL

[8]

[Anonymous], COMMUNICATION

[9]

[Anonymous], ADV NEURAL INFORM PR

[10]

[Anonymous], IEEE T EVOL COMPUT

← 1 2 3 4 5 →