We propose a new niching method for Evolutionary Algorithms which is able to identify and track global and local optima in a multimodal search space. To prevent the loss of diversity we replace the global selection pressure within a single population by local selection of a multi-population strategy. The sub-populations representing species specialized on niches are dynamically identified using standard clustering algorithms on a primordial population. With this multi-population strategy we are able to preserve diversity within the population and to identify global/local optima directly without further post-processing. © Springer-Verlag 2004.
CITATION STYLE
Streichert, F., Stein, G., Ulmer, H., & Zell, A. (2004). A Clustering Based Niching EA for Multimodal Search Spaces. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2936, 293–304. https://doi.org/10.1007/978-3-540-24621-3_24
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