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
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
Group selection vs multi-level selection: some example models using evolutionary games
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Investigations of Wilson's and Traulsen's group selection models in evolutionary computation
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Rethinking multilevel selection in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
Sustainable cooperative coevolution with a multi-armed bandit
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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To successfully search multiple coadaptive subcomponents in a solution, we developed a novel cooperative evolutionary algorithm based on a new computational multilevel selection framework. This algorithm constructs cooperative solutions hierarchically by implementing the idea of group selection. We show that this simple and straightforward algorithm is able to accelerate evolutionary speed and improve solution accuracy on string covering problems as compared to other EAs used in literature. In addition, the structure of the solution and the roles played by each subcomponent in the solution emerge as a result of evolution without human interference.