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

Sergio Alvarado, Adriana Lara, Victor Sosa, Oliver Schutze

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509035106
DOIs
StatePublished - 21 Nov 2016
Event13th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2016 - Mexico City, Mexico
Duration: 26 Sep 201630 Sep 2016

Publication series

Name2016 13th International Conference on Electrical Engineering,Computing Science and Automatic Control, CCE 2016

Conference

Conference13th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2016
Country/TerritoryMexico
CityMexico City
Period26/09/1630/09/16

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