2 research outputs found
Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization
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.Comment: Winner of Best Paper Award in GECCO 2008. arXiv admin note:
substantial text overlap with arXiv:1711.06840, arXiv:1711.0684
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.Comment: arXiv admin note: substantial text overlap with arXiv:1711.06839,
arXiv:1711.0684