Comparison of multiple objective genetic algorithms for parallel machine scheduling problems

W. Matthew Carlyle, Bosun Kim, John Fowler, Esma Gel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Many multiple objective genetic algorithms have been developed to approximate the efficient frontier of solutions for multiple objective optimization problems. However, only a limited number of comparison studies have been performed on practical problems. One of the reasons for this may be the lack of commonly accepted measures to compare the solution quality of sets of approximately optimal solutions. In this paper, we perform an extensive set of experiments to quantitatively compare the solutions of two competing algorithms for a bi-criteria parallel machine-scheduling problem.

Original languageEnglish (US)
Title of host publicationEvolutionary Multi-Criterion Optimization - 1st International Conference, EMO 2001, Proceedings
EditorsEckart Zitzler, Lothar Thiele, Kalyanmoy Deb, Carlos A. Coello Coello, David Corne
PublisherSpringer Verlag
Pages472-485
Number of pages14
ISBN (Electronic)9783540417453
DOIs
StatePublished - 2001
Event1st International Conference on Evolutionary Multi-Criterion Optimization, EMO 2001 - Zurich, Switzerland
Duration: Mar 7 2001Mar 9 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1993
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Evolutionary Multi-Criterion Optimization, EMO 2001
CountrySwitzerland
CityZurich
Period3/7/013/9/01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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