Abstract
Abstract—This paper presents a novel use of Genetic Programming, Co-Evolution and Interactive Fitness to evolve algorithms for the game of Tic-Tac-Toe. The selected tree-structured algorithms are evaluated based on a fitness-less double-game strategy and then compete against a human player. This paper will outline the evolution process which leads to producing the best Tic-Tac-Toe playing algorithm. The evolved algorithms have proven effective for playing against human opponents.
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