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...
| Autores: | , |
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| 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 |
| 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. |
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