Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess

We propose an approach for developing efficient search algorithms through genetic programming. Focusing on the game of chess we evolve entire game-tree search algorithms to solve the Mate-In-N problem: find a key move such that even with the best possible counterplays, the opponent cannot avoid being mated in (or before) move N . We show that our evolved search algorithms successfully solve several instances of the Mate-In-N problem, for the hardest ones developing 47% less gametree nodes than CRAFTY—a state-of-the-art chess engine with a ranking of 2614 points. Improvement is thus not over the basic alpha-beta algorithm, but over a world-class program using all standard enhancements.