Injecting CMA-ES into MOEA/D

MOEA/D is an aggregation-based evolutionary algorithm whichhas been proved extremely efficient and effective for solving multi-objective optimization problems. It is based on the idea of de-composing the original multi-objective problem into several single-objective subproblems by means of wel l-defined...

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Detalles Bibliográficos
Autores: SAUL ZAPOTECAS MARTINEZ, KIYOSHI TANAKA
Tipo de recurso: capítulo de libro
Estado:Versión publicada
Fecha de publicación:2015
País:México
Institución:Universidad Autónoma Metropolitana
Repositorio:Concentración de Recursos de Información Científica y Académica, UAM Cuajimalpa
Idioma:inglés
OAI Identifier:oai:ilitia.cua.uam.mx:123456789/475
Acceso en línea:http://ilitia.cua.uam.mx:8080/jspui/handle/123456789/475
Access Level:acceso abierto
Palabra clave:info:eu-repo/classification/cti/7
Algoritmos computacionales
Computación evolutiva
Inteligencia artificial
Descripción
Sumario:MOEA/D is an aggregation-based evolutionary algorithm whichhas been proved extremely efficient and effective for solving multi-objective optimization problems. It is based on the idea of de-composing the original multi-objective problem into several single-objective subproblems by means of wel l-defined scalari zi ng f unc-tions. Those single-objective subproblems are solved in a cooper-ative manner by defining a neighborhood relation between them.This makes MOEA/D particularly interesting when attempting toplug and to leverage single-objective optimizers in a multi-objectivesetting. In this context, we investigate the benefits that MOEA/Dcan achieve when coupled with CMA-ES, which is believed to bea pow erful single-objective optimizer. We rely on the ability ofCMA-ES to deal with injected solutions in order to update differ-ent covariance matrices with respect to each subproblem definedin MOEA/D. We show that by cooperatively evolving neighboringCMA-ES components, we are able to obtain competitive results fordifferent multi-objective benchmark functions.