Expert-Driven Genetic Algorithms for Simulating Evaluation Functions

TL;DR

This paper presents a method using genetic algorithms guided by expert knowledge to reverse engineer and optimize evaluation functions in computer chess, achieving performance comparable to top programs.

Contribution

It introduces an expert-driven genetic algorithm approach to evolve compact, effective evaluation functions for chess, outperforming established champions.

Findings

Evolved programs match top tournament chess performance

Achieved superior results over a two-time World Champion

Evolved evaluation functions have fewer parameters than the expert

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.