Chess Player by Co-Evolutionary Algorithm

TL;DR

This paper introduces a co-evolutionary algorithm for chess that employs competitive populations to mimic alpha-beta search, with detailed implementation and promising test results against algorithms and humans.

Contribution

It presents a novel co-evolutionary approach to chess playing, emphasizing the design of fitness functions and population interactions.

Findings

Competitive populations improve chess playing strength.

The algorithm performs well against other algorithms and human players.

Detailed implementation enhances reproducibility.

Abstract

A co-evolutionary algorithm (CA) based chess player is presented. Implementation details of the algorithms, namely coding, population, variation operators are described. The alpha-beta or mini-max like behaviour of the player is achieved through two competitive or cooperative populations. Special attention is given to the fitness function evaluation (the heart of the solution). Test results on algorithms vs. algorithms or human player is provided.