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
Niching methods for genetic algorithms
Niching methods for genetic algorithms
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Parallel Genetic Algorithms Population Genetics and Combinatorial 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
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
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Spatially-Structured evolutionary algorithms and sharing: do they mix?
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
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Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing, spatially-structured populations and elitist replacement. In local sharing the bias toward sharing or spatial effect is controlled by the deme (neighbourhood) size. This introduces an undesirable trade-off; to maximise the sharing effect, deme sizes must be large, but the opposite must be true if one wishes to maximise the influence of spatial population structure. This paper introduces a modification to the local sharing method whereby parent selection and fitness sharing operate at two different spatial levels; parent selection is performed within small demes, while the effect of fitness sharing is weighted according to the distances between individuals in the population structure. The proposed method, as tested on several benchmark problems, demonstrates a level of efficiency and parameter robustness that surpasses the basic local sharing method.