Randomized algorithms
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Parameter Control within a Co-operative Co-evolutionary Genetic Algorithm
PPSN VII Proceedings of the 7th 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
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
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 Cooperative Coevolutionary (1+1) EA
Evolutionary Computation
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
Distribution replacement: how survival of the worst can out perform survival of the fittest
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Competitive coevolutionary learning of fuzzy systems for job exchange in computational grids
Evolutionary Computation
On advantages of scheduling using genetic fuzzy systems
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Theoretical convergence guarantees for cooperative coevolutionary algorithms
Evolutionary Computation
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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Using a well-known cooperative coevolutionary function optimization framework, a very simple cooperative coevolutionary (1+1) EA is defined. This algorithm is investigated in the context of expected optimization time. The focus is on the impact the cooperative coevolutionary approach has and on the possible advantage it may have over more traditional evolutionary approaches. Therefore, a systematic comparison between the expected optimization times of this coevolutionary algorithm and the ordinary (1+1) EA is presented. The main result is that separability of the objective function alone is is not sufficient to make the cooperative coevolutionary approach beneficial. By presenting a clear structured example function and analyzing the algorithms' performance, it is shown that the cooperative coevolutionary approach comes with new explorative possibilities. This can lead to an immense speed-up of the optimization.