New results on evolving strategies in chess

David B. Fogel; Tim Hays

doi:10.1117/12.512624

30 December 2003 New results on evolving strategies in chess

David B. Fogel, Tim Hays

Proceedings Volume 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI; (2003) https://doi.org/10.1117/12.512624
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

Abstract

Evolutionary algorithms have been used for learning strategies in diverse games, including Othello, backgammon, checkers, and chess. The paper provides a brief background on efforts in evolutionary learning in chess, and presents recent results on using coevolution to learn strategies by improving existing nominal strategies. Over 10 independent trials, each executed for 50 generations, a simple evolutionary algorithm was able to improve a nominal strategy that was based on material value and positional value adjustments associated with individual pieces. The improvement was estimated at over 284 rating points, taking a Class A player and evolving it into an expert.

Citation Download Citation

David B. Fogel and Tim Hays "New results on evolving strategies in chess", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); https://doi.org/10.1117/12.512624

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