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Genetically programmed strategies for chess endgame

Classical chess engines exhaustively explore moving possibilities from a chessboard configuration to choose what the next best move to play is. In this article we present a new method to solve chess endgames without using Brute-Force algorithms or endgame tables. We are proposing to use Genetic Programming to combine elementary chess patterns defined by a chess expert. We apply this method specifically to the classical King-Rook-King endgame. We show that computed strategies are both effective and generic for they manage to win against several opponents (human players and artificial ones such as the chess engine CRAFTY). Besides, the method allows to propose strategies that are clearly readable and useable for a purpose such as teaching chess.