Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
An agent-based collaborative evolutionary model for multimodal optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
An evolutionary algorithm with species-specific explosion for multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms
Information Sciences: an International Journal
| Hi-index | 0.00 |
Multimodal optimization problems consist in detecting all global and local optima of a problem. A new evolutionary approach to multimodal optimization called Roaming technique (RO) is presented. Roaming uses two original concepts in order to detect multiple optima: a stability measure for subpopulations and an external population called archive to store detected optima. Individuals in the archive are refined by evolving them independently. Performance of Roaming is compared by means of numerical experiments with two other evolutionary techniques.