Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of decentralized and spatially distributed genetic algorithms
An analysis of decentralized and spatially distributed genetic algorithms
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
A Weighted Local Sharing Technique for Multimodal Optimisation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
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Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm (EA). However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone (and vice-versa).