Empirical model-building and response surface
Empirical model-building and response surface
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The Coevolution of Antibodies for Concept Learning
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
Learning team behaviors with adaptive heterogeneity
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
The Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
Archive-based cooperative coevolutionary algorithms
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
Improving coevolutionary search for optimal multiagent behaviors
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Heterogeneity in the coevolved behaviors of mobile robots: the emergence of specialists
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Exploring the explorative advantage of the cooperative coevolutionary (1+1) EA
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
Automatic task decomposition for the neuroevolution of augmenting topologies (NEAT) algorithm
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Cooperative coevolutionary algorithms have the potential to significantly speed up the search process by dividing the space into parts that can each be conquered separately. However, recent research presented theoretical and empirical arguments that these algorithms tend to converge to suboptimal solutions in the search space, and are thus not fit for optimization tasks. This paper details an extended formal model for cooperative coevolutionary algorithms, and uses it to explore possible reasons these algorithms converge to optimal or suboptimal solutions. We demonstrate that, under specific conditions, this theoretical model will converge to the globally optimal solution. The proofs provide the underlying theoretical foundation for a better application of cooperative coevolutionary algorithms. We demonstrate the practical advantages of applying ideas from this theoretical work to a simple problem domain.