A Co-evolutionary Generation Alternation Model for Designing Effective Strategies in One-on-one Competitive Games

Abstract

We have proposed a generation alternation model for co-evolutionary optimization of strategies in one-on-one competitive games. However, the model often degrades the best strategies in the population during the course of co-evolution. To remedy the drawback, we propose an improved model, which takes advantage of a long-term memory of past significant strategies much more effectively than the previous model.