An effective mutation operator to deal with multi-objective constrained problems: SPM

Sergio Alvarado, Adriana Lara, Victor Sosa, Oliver Schutze

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

1 Cita (Scopus)

Resumen

In this paper, a novel mutation operator for Evolutionary Multi-objective Algorithms (MOEAs), named as Subspace Polynomial Mutation (SPM) is presented. This specialized mutation operator is particularly designed to deal with constrained continuos problems. As a variation operator, SPM ensures the production of suitable candidate solutions which has not only the chance to improve their survival rate, but that fulfills feasibility also-saving in this way a considerable amount of function evaluations when avoiding unnecessary trials. This feature coupled with the ability of SPM for performing movements along the constrained Pareto set improves the efficiency of the mutation process in a MOEA.

Idioma originalInglés
Título de la publicación alojada2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2016
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781509035106
DOI
EstadoPublicada - 21 nov. 2016
Evento13th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2016 - Mexico City, México
Duración: 26 sep. 201630 sep. 2016

Serie de la publicación

Nombre2016 13th International Conference on Electrical Engineering,Computing Science and Automatic Control, CCE 2016

Conferencia

Conferencia13th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2016
País/TerritorioMéxico
CiudadMexico City
Período26/09/1630/09/16

Huella

Profundice en los temas de investigación de 'An effective mutation operator to deal with multi-objective constrained problems: SPM'. En conjunto forman una huella única.

Citar esto