SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Ensemble of niching algorithms
Information Sciences: an International Journal
Multimodal optimization by means of a topological species conservation algorithm
IEEE Transactions on Evolutionary Computation
A dual-population genetic algorithm for adaptive diversity control
IEEE Transactions on Evolutionary Computation
Speciation in evolutionary algorithms: adaptive species discovery
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A robust dynamic niching genetic clustering approach for image segmentation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
An attraction basin estimating genetic algorithm for multimodal optimization
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A Review of Niching Genetic Algorithms for Multimodal Function Optimization
Cybernetics and Systems Analysis
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The problem of locating all the optima within a multimodal fitness landscape has been widely addressed in evolutionary computation, and many solutions, based on a large variety of different techniques, have been proposed in the literature. Among them, fitness sharing (FS) is probably the best known and the most widely used. The main criticisms to FS concern both the lack of an explicit mechanism for identifying or providing any information about the location of the peaks in the fitness landscape, and the definition of species implicitly assumed by FS. We present a mechanism of FS, i.e., dynamic fitness sharing, which has been devised in order to overcome these limitations. The proposed method allows an explicit, dynamic identification of the species discovered at each generation, their localization on the fitness landscape, the application of the sharing mechanism to each species separately, and a species elitist strategy. The proposed method has been tested on a set of standard functions largely adopted in the literature to assess the performance of evolutionary algorithms on multimodal functions. Experimental results confirm that our method performs significantly better than FS and other methods proposed in the literature without requiring any further assumption on the fitness landscape than those assumed by the FS itself.