  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 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 with a smaller number of parameters in its evaluation function to mimic
the behavior of a superior mentor which uses a more extensive evaluation
function. In principle, our mentor-assisted approach could be used in a wide
range of problems for which appropriate mentors are available.
