Gradient based stochastic mutation operators in evolutionary multi-objective optimization

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Abstract

Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems.

Original languageEnglish
Title of host publicationAdaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings
PublisherSpringer-Verlag Italia
Pages58-66
Number of pages9
EditionPART 1
ISBN (Print)9783540715894
DOIs
Publication statusPublished - 2007
Event8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007 - Warsaw, Poland
Duration: 11 Apr 200714 Apr 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4431 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007
Country/TerritoryPoland
CityWarsaw
Period11/04/0714/04/07

Keywords

  • Mutation Operator
  • Search Operator
  • Binary Indicator
  • Local Search Operator
  • Simultaneous Perturbation

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