Evolving both search and strategy for Reversi players using genetic programming

Amit Benbassat; Moshe Sipper

doi:10.1109/CIG.2012.6374137

Evolving both search and strategy for Reversi players using genetic programming

Amit Benbassat, Moshe Sipper

Department of Computer Science

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

9 Scopus citations

Abstract

We present the application of genetic programming to the zero-sum, deterministic, full-knowledge board game of Reversi. Expanding on our previous work on evolving boardstate evaluation functions, we now evolve the search algorithm as well, by allowing evolved programs control of game-tree pruning. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. We show that our system regularly churns out highly competent players and our results prove easy to scale.

Original language	English
Title of host publication	2012 IEEE Conference on Computational Intelligence and Games, CIG 2012
Pages	47-54
Number of pages	8
DOIs	https://doi.org/10.1109/CIG.2012.6374137
State	Published - 1 Dec 2012
Event	2012 IEEE International Conference on Computational Intelligence and Games, CIG 2012 - Granada, Spain Duration: 11 Sep 2012 → 14 Sep 2012

Publication series

Name	2012 IEEE Conference on Computational Intelligence and Games, CIG 2012

Conference

Conference	2012 IEEE International Conference on Computational Intelligence and Games, CIG 2012
Country/Territory	Spain
City	Granada
Period	11/09/12 → 14/09/12

ASJC Scopus subject areas

Artificial Intelligence
Computer Graphics and Computer-Aided Design
Human-Computer Interaction
Software

Access to Document

10.1109/CIG.2012.6374137

Cite this

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title = "Evolving both search and strategy for Reversi players using genetic programming",

abstract = "We present the application of genetic programming to the zero-sum, deterministic, full-knowledge board game of Reversi. Expanding on our previous work on evolving boardstate evaluation functions, we now evolve the search algorithm as well, by allowing evolved programs control of game-tree pruning. We use strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. We show that our system regularly churns out highly competent players and our results prove easy to scale.",

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doi = "10.1109/CIG.2012.6374137",

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series = "2012 IEEE Conference on Computational Intelligence and Games, CIG 2012",

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Benbassat, A & Sipper, M 2012, Evolving both search and strategy for Reversi players using genetic programming. in 2012 IEEE Conference on Computational Intelligence and Games, CIG 2012., 6374137, 2012 IEEE Conference on Computational Intelligence and Games, CIG 2012, pp. 47-54, 2012 IEEE International Conference on Computational Intelligence and Games, CIG 2012, Granada, Spain, 11/09/12. https://doi.org/10.1109/CIG.2012.6374137

Evolving both search and strategy for Reversi players using genetic programming. / Benbassat, Amit; Sipper, Moshe.
2012 IEEE Conference on Computational Intelligence and Games, CIG 2012. 2012. p. 47-54 6374137 (2012 IEEE Conference on Computational Intelligence and Games, CIG 2012).

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

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Evolving both search and strategy for Reversi players using genetic programming

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