The Effects of Representational Bias on Collaboration Methods in Cooperative Coevolution
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
The MaxSolve algorithm for coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
The effects of interaction frequency on the optimization performance of cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Robustness in cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
An evolutionary game based particle swarm optimization algorithm
Journal of Computational and Applied Mathematics
Improving coevolutionary search for optimal multiagent behaviors
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Evolutionary game theoretic approach for modeling civil violence
IEEE Transactions on Evolutionary Computation
The cooperative royal road: avoiding hitchhiking
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
IEEE Transactions on Evolutionary Computation
A game-theoretic approach for designing mixed mutation strategies
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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The task of understanding coevolutionary algorithms is very difficult. 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 re 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.