Robustness in cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Introducing probabilistic adaptive mapping developmental genetic programming with redundant mappings
Genetic Programming and Evolvable Machines
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Coevolutionary bid-based genetic programming for problem decomposition in classification
Genetic Programming and Evolvable Machines
Social Organization of Evolving Multiple Classifier System Functioning in Changing Environments
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Investigating collaboration methods of random immigrant scheme in cooperative coevolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Coevolution of heterogeneous multi-robot teams
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Symbiosis, complexification and simplicity under GP
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical convergence guarantees for cooperative coevolutionary algorithms
Evolutionary Computation
Shaping fitness functions for coevolving cooperative multiagent systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Sustainable cooperative coevolution with a multi-armed bandit
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Cooperative coevolutionary algorithms (CEAs) offer great potential for concurrent multiagent learning domains and are of special utility to domains involving teams of multiple agents. Unfortunately, they also exhibit pathologies resulting from their game-theoretic nature, and these pathologies interfere with finding solutions that correspond to optimal collaborations of interacting agents. We address this problem by biasing a cooperative CEA in such a way that the fitness of an individual is based partly on the result of interactions with other individuals (as is usual), and partly on an estimate of the best possible reward for that individual if partnered with its optimal collaborator. We justify this idea using existing theoretical models of a relevant subclass of CEAs, demonstrate how to apply biasing in a way that is robust with respect to parameterization, and provide some experimental evidence to validate the biasing approach. We show that it is possible to bias coevolutionary methods to better search for optimal multiagent behaviors