Abstract
We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have needed niching/sharing to ensure diversity. In this paper, we use a steady-state multiobjective algorithm which preserves diversity without niching to produce diverse sampling of the Pareto-front with significantly lower computational effort. © Springer-Verlag Berlin Heidelberg 2003.
Cite
CITATION STYLE
Kumar, R., & Rockett, P. (2003). Evolutionary multimodal optimization revisited. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2724, 1592–1593. https://doi.org/10.1007/3-540-45110-2_40
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