Evolving both search and strategy for Reversi players using genetic programming

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.