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
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
The Nature of Niching: Genetic Algorithms and the Evolution of Optimal, Cooperative Populations
The Nature of Niching: Genetic Algorithms and the Evolution of Optimal, Cooperative Populations
A sequential niche technique for multimodal function optimization
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
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach
International Journal of Artificial Life Research
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This paper presents a new method, which combines sharing and a fuzzy clustering technique to improve the performance of genetic algorithms in multimodal function optimization. This approach permits some limitations of the traditional sharing scheme to be overcome. Without using any prior information, it allows both location and maintenance of niches. Computer simulations show good performance for several multimodal test functions including a deceptive problem.