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30 December 2003 New results on evolving strategies in chess
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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