A painless gradient-assisted multi-objective memetic mechanism for solving continuous bi-objective optimization problems

Adriana Lara López, Carlos A.Coello Coello, Oliver Schütze

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

13 Citas (Scopus)

Resumen

In this work we present a simple way to introduce gradient-based information as a means to improve the search performed by a multi-objective evolutionary algorithm (MOEA). Our proposal can be easily incorporated into any MOEA, and is able to improve its performance when solving continuous bi-objective problems. We propose a novel mechanism to control the balance between the local search, and the global search performed by a MOEA. We discuss the advantages of the proposed method and its possible use when dealing with more objectives. Finally, we provide some guidelines regarding the use of our proposed approach.

Idioma originalInglés
Título de la publicación alojada2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOI
EstadoPublicada - 2010
Publicado de forma externa
Evento2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Espana
Duración: 18 jul. 201023 jul. 2010

Serie de la publicación

Nombre2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Conferencia

Conferencia2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
País/TerritorioEspana
CiudadBarcelona
Período18/07/1023/07/10

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