Title: Expert-Driven Genetic Algorithms for Simulating Evaluation Functions,
Abstract: 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.