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Injecting CMA-ES into MOEA/D

Abstract : MOEA/D is an aggregation-based evolutionary algorithm which has been proved extremely efficient and effective for solving multiobjective optimization problems. It is based on the idea of decomposing the original multi-objective problem into several singleobjective subproblems by means of well-defined scalarizing functions. Those single-objective subproblems are solved in a cooperative manner by defining a neighborhood relation between them. This makes MOEA/D particularly interesting when attempting to plug and to leverage single-objective optimizers in a multi-objective setting. In this context, we investigate the benefits that MOEA/D can achieve when coupled with CMA-ES, which is believed to be a powerful single-objective optimizer. We rely on the ability of CMA-ES to deal with injected solutions in order to update different covariance matrices with respect to each subproblem defined in MOEA/D. We show that by cooperatively evolving neighboring CMA-ES components, we are able to obtain competitive results for different multi-objective benchmark functions.
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Submitted on : Tuesday, April 28, 2015 - 11:59:56 PM
Last modification on : Tuesday, November 22, 2022 - 2:26:16 PM
Long-term archiving on: : Wednesday, April 19, 2017 - 9:11:31 AM


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Saúl Zapotecas-Martínez, Bilel Derbel, Arnaud Liefooghe, Dimo Brockhoff, Hernán E. Aguirre, et al.. Injecting CMA-ES into MOEA/D. Genetic and Evolutionary Computation Conference (GECCO 2015), Jul 2015, Madrid, Spain. ⟨10.1145/2739480.2754754⟩. ⟨hal-01146738⟩



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