The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) combines a mutation operator that adapts its search distribution to the underlying optimization problem with multicriteria selection. Here, a generational and two steady-state selection schemes for the MO-CMA-ES are compared. Further, a recently proposed method for computationally efficient adaptation of the search distribution is evaluated in the context of the MO-CMA-ES. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Igel, C., Suttorp, T., & Hansen, N. (2007). Steady-state selection and efficient covariance matrix update in the multi-objective CMA-ES. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4403 LNCS, pp. 171–185). Springer Verlag. https://doi.org/10.1007/978-3-540-70928-2_16
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