Genetic algorithms with dynamic niche sharing for multimodal function optimization
- 23 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 786-791
- https://doi.org/10.1109/icec.1996.542701
Abstract
Genetic algorithms utilize populations of individual hypotheses that converge over time to a single optimum, even within a multimodal domain. This paper examines methods that enable genetic algorithms to identify multiple optima within multimodal domains by maintaining population members within the niches defined by the multiple optima. A new mechanism, dynamic niche sharing, is developed that is able to efficiently identify and search multiple niches (peaks) in a multimodal domain. Dynamic niche sharing is shown to perform better than two other methods for multiple optima identification, standard sharing and deterministic crowding.Keywords
This publication has 3 references indexed in Scilit:
- A Double-Layered Learning Approach to Acquiring Rules for Classification: Integrating Genetic Algorithms with Similarity-Based LearningINFORMS Journal on Computing, 1994
- Implicit Niching in a Learning Classifier System: Nature's WayEvolutionary Computation, 1994
- A Fast Genetic Algorithm with Sharing Scheme Using Cluster Analysis Methods in Multimodal Function OptimizationPublished by Springer Nature ,1993