Evolving board-game players with genetic programming
Amit Benbassat, Moshe Sipper · 2011
We present the application of genetic programming (GP) to zero-sum, deterministic, full-knowledge board games. Our work expands previous results in evolving board-state evaluation functions for Lose Checkers to a 10x10 variant of Checkers, as well as Reversi. Our system implements strongly typed GP trees, explicitly defined introns, and a selective directional crossover method.