Evolving both search and strategy for Reversi players using genetic programming

Amit Benbassat, Moshe Sipper

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 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 languageEnglish
Title of host publication2012 IEEE Conference on Computational Intelligence and Games, CIG 2012
Pages47-54
Number of pages8
DOIs
StatePublished - 1 Dec 2012
Event2012 IEEE International Conference on Computational Intelligence and Games, CIG 2012 - Granada, Spain
Duration: 11 Sep 201214 Sep 2012

Publication series

Name2012 IEEE Conference on Computational Intelligence and Games, CIG 2012

Conference

Conference2012 IEEE International Conference on Computational Intelligence and Games, CIG 2012
Country/TerritorySpain
CityGranada
Period11/09/1214/09/12

ASJC Scopus subject areas

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

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