Understanding cooperative co-evolutionary dynamics via simple fitness landscapes

Cooperative co-evolution is often used to solve difficult optimization problems by means of problem decomposition. Its performance for such tasks can vary widely from good to disappointing. One of the reasons for this is that attempts to improve co-evolutionary performance using traditional EC analysis techniques often fail to provide the necessary insights into the dynamics of co-evolutionary systems, a key factor affecting performance. In this paper we use two simple fitness landscapes to illustrate the importance of taking a dynamical systems approach to analyzing co-evolutionary algorithms in order to understand them better and to improve their problem solving performance.