Chess Player by Co-Evolutionary Algorithm
This is an incremental approach for chess AI development, applying co-evolutionary methods to a well-known domain.
The paper tackles the problem of creating a chess-playing agent using a co-evolutionary algorithm, achieving competitive performance against other algorithms and human players, though specific numerical results are not detailed in the 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.