Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
The dynamics of the best individuals in co-evolution
Natural Computing: an international journal
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
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Previous study shows that using a random immigrant scheme in a cooperative coevolutionary algorithm (RI-CCEA) can significantly track the moving peaks in dynamic optimization. In this paper, we further investigate its behavior in the multi-modal environments where peak locations, peak coverage and peak heights of the moving peaks are changing during the course of optimization. Of the particular interest to us is the different combinations of the collaboration methods used by the original individuals and the RI individuals of the CCEA populations. Empirical comparisons show that in the moderate-changing or slow-changing environments, using the best collaborations in original individuals in the RICCEA outperforms other variants in our experiments, while the choice of the collaboration methods in RI individuals is insignificant. In a fast-changing environment, using the random collaborations in original individuals is crucial to achieve a better performance and the choice of the collaboration methods in RI individuals is also significant.