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Evolutionary computation is a useful technique for learning behaviors in multiagent systems. Among the several types of evolutionary computation, one natural and popular method is to coevolve multiagent behaviors in multiple, cooperating populations. Recent research has suggested that coevolutionary systems may favor stability rather than performance in some domains. In order to improve upon existing methods, this paper examines the idea of modifying traditional coevolution, biasing it to search for maximal rewards. We introduce a theoretical justification of the improved method and present experiments in three problem domains. We conclude that biasing can help coevolution find better results in some multiagent problem domains.

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Abstruct-The task of understanding coevolutionary algorithms is a very difficult one. These algorithms search landscapes which are in some sense adaptive. As a result, the dynamical behaviors of coevolutionary systems can frequently be even more complex than traditional evolutionary algorithms (EAs). Moreover, traditional EA theory tells us little about coevolutionary algorithms. One major question that has yet to be clearly addressed is whether or not coevolutionary algorithms are well-suited for optimization tasks. Although this question is equally applicable to competitive, as well as cooperative approaches, answering the question for cooperative coevolutionary algorithms is perhaps more attainable.Recently, evolutionary game theoretic (EGT) models have begun to be used to help analyze the dynamical behaviors of coevolutionary algorithms. One type of EGT model which is already reasonably well understood are multi-population symmetric games. We believe these games can be used to analytically model cooperative coevolutionary algorithms. This paper introduces our analysis framework, explaining how and why such models may be generated. It includes some examples illustrating specific theoretical and empirical analyses. We demonstrate that using our framework, a better understanding for the degree to which cooperative coevolutionary algorithms can be used for optimization can be achieved.