Optimizing Selective Search in Chess

Omid David-Tabibi; M. Koppel; Nathan S. Netanyahu

Optimizing Selective Search in Chess

Omid David-Tabibi, M. Koppel, Nathan S. Netanyahu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.

Original language	American English
Title of host publication	ICML Workshop on Machine Learning and Games
State	Published - 2010

Bibliographical note

Place of conference:Israel

Cite this

@inproceedings{29fdf55072594e74b003988a1ed4408a,

title = "Optimizing Selective Search in Chess",

abstract = "In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.",

author = "Omid David-Tabibi and M. Koppel and Netanyahu, \{Nathan S.\}",

note = "Place of conference:Israel",

year = "2010",

language = "American English",

booktitle = "ICML Workshop on Machine Learning and Games",

}

TY - GEN

T1 - Optimizing Selective Search in Chess

AU - David-Tabibi, Omid

AU - Koppel, M.

AU - Netanyahu, Nathan S.

N1 - Place of conference:Israel

PY - 2010

Y1 - 2010

N2 - In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.

AB - In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.

UR - https://scholar.google.co.il/scholar?q=Optimizing+Selective+Search+in+Chess&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - ICML Workshop on Machine Learning and Games

ER -

Optimizing Selective Search in Chess

Abstract

Bibliographical note

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