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.
Partially supported by the Ministry of Human Resource Development, Government of India
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References
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Kumar, R., Rockett, P.I.: Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Genetic Algorithm. Evolutionary Computation 10(3): 282–314, 2002
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Kumar, R., Rockett, P. (2003). Evolutionary Multimodal Optimization Revisited. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_40
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DOI: https://doi.org/10.1007/3-540-45110-2_40
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