Robustness in Cooperative Coevolution
R. Paul Wiegand
Mitchell A. Potter
Though recent analysis of traditional cooperative coevolutionary algorithms (CCEAs) casts doubt on their suitability for static optimization tasks, our experience is that the algorithms perform quite well in multiagent learning settings. This is due in part because many CCEAs may be quite suitable to finding behaviors for team members that result in good (though not necessarily optimal) performance but which are also robust to changes in other team members. Given this, there are two main goals of this paper. First, we describe a general framework for clearly defining robustness, offering a specific definition for our studies. Second, we examine the hypothesis that CCEAs exploit this robustness property during their search. We use an existing theoretical model to gain intuition about the kind of problem properties that attract populations in the system, then provide a simple empirical study justifying this intuition in a practical setting. The results are the first steps toward a constructive view of CCEAs as optimizers of robustness.