On Co-evolutionary Learning of Effective Strategies in One-on-one Contests

Abstract

We have proposed generation alternation models for co-evolutionary optimization of strategies in one-on-one competitive contests. This paper discusses the potentials of the models, through the application to several contests, including Nim and the two-dimensional hockey games. The experimental results show that the proposed model performs better over the conventional models.