Abstract The SMS-EMOA is a simple and powerful evolutionary metaheuristic for computing approximations to Pareto front based on the dominated hypervolume indicator (S-metric). However, as other state-of-the-art metaheuristics, it consumes a high number of function evaluations in order to compute accurate approximations. To reduce its total computational cost and response time for problems with time consuming evaluators, we suggest three adjustments: Step-size adaptation, Kriging metamodeling, and Steady-State Parallelization. We show that all these measures contribute to the acceleration of the SMS-EMOA on continuous benchmark problems as well as on a application problem - the quantum mechanical optimal control with shaped laser pulses. © Springer Physica-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Klinkenberg, J. W., Emmerich, M. T. M., Deutz, A. H., Shir, O. M., & Bäck, T. (2010). A reduced-cost SMS-EMOA using kriging, self-adaptation, and parallelization. Lecture Notes in Economics and Mathematical Systems, 634, 301–311. https://doi.org/10.1007/978-3-642-04045-0_26
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