TY - GEN
T1 - Injecting CMA-ES into MOEA/D
AU - Zapotecas-Martínez, Saúl
AU - Brockhoff, Dimo
AU - Derbel, Bilel
AU - Aguirre, Hernán E.
AU - Liefooghe, Arnaud
AU - Tanaka, Kiyoshi
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/7/11
Y1 - 2015/7/11
N2 - 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.
AB - 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.
KW - Covariance matrix adaption evolution strategy
KW - Decomposition-based moeas
KW - Multi-objective optimization
U2 - 10.1145/2739480.2754754
DO - 10.1145/2739480.2754754
M3 - Conference contribution
AN - SCOPUS:84963670120
T3 - GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
SP - 783
EP - 790
BT - GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
A2 - Silva, Sara
PB - Association for Computing Machinery, Inc
T2 - 16th Genetic and Evolutionary Computation Conference, GECCO 2015
Y2 - 11 July 2015 through 15 July 2015
ER -