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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.

Co-evolutionary algorithms (CEAs) have been applied to optimization and machine learning problems with often mediocre results. One of the causes for the unfulfilled expectations is the discrepancy between the external problem solving goal and the internal mechanisms of the algorithm. In this paper, we investigate in a principled way the relationships between the internal subjective metric used as fitness by a co-evolutionary algorithm and the external objective metric measuring the algorithm's progress towards the envisioned goal. We point out the complexity of these relationships and explain their causes.

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Abstract.There continues to be a growing interest in the use of co-evolutionary algorithms to solve difficult computational problems. Their performance however has varied widely from good to disappointing. The main reason for this is that co-evolutionary systems can display quite complex phenomena. Therefore, in order to efficiently use co-evolutionary algorithms for problem solving, one must have a good understanding of their dynamical behavior. To build such understanding, we have constructed a methodology for analyzing co-evolutionary dynamics based on trajectories of bestof-generation individuals. We applied this methodology to gain insights into how to tune certain algorithm parameters in order to improve performance.