Expert-Driven Genetic Algorithms for Simulating Evaluation Functions

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert's. The extended experimental results provided in this paper include a report of our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available. Keywords Computer chess, Fitness evaluation, Games, Genetic algorithms, Parameter tuning 1 Introduction Since the dawn of modern computer science, game playing has posed a formidable challenge in the field of Artificial Intelligence. A preliminary version of this paper appeared in Proceedings of the 2008 Genetic and Evolutionary Computation Conference [13] and received the Best Paper Award in the conference's Real-World Applications track. John McCarthy, Ken Thompson, Herbert Simon, and others) developed game-playing programs and used games in AI research. The ongoing key role played by and the impact of computer games on AI should not be underestimated.