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Workshops on Applications of Evolutionary Computation

EvoWorkshops 2003: Applications of Evolutionary Computing pp 268–279Cite as

Guiding Single-Objective Optimization Using Multi-objective Methods

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2611))

Abstract

This paper investigates the possibility of using multi-objective methods to guide the search when solving single-objective optimization problems with genetic algorithms.Using the job shop scheduling problem as an example,experiments demonstrate that by using helper-objectives (additional objectives guiding the search),the average performance of a standard GA can be significantly improved.The helper-objectives guide the search towards solutions containing good building blocks and helps the algorithm avoid local optima.The experiments reveal that the approach only works if the number of helper-objectives used simultaneously is low.However,a high number of helper-objectives can be used in the same run by changing the helper-objectives dynamically.

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Author information

Authors and Affiliations

  1. EVALife,Department of Computer Science, University of Aarhus, Ny Munkegade bldg.540, DK-8000, Aarhus C, Denmark

    Mikkel T. Jensen

Authors
  1. Mikkel T. Jensen
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Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  2. Computing Laboratory, University of Kent, Canterbury, Kent, CT2 7NF, UK

    Colin G. Johnson

  3. Dept. of Information and Communications Technologies Faculty of Computer Science, University of A Coruña, A Coruña, CP 15071, Spain

    Juan J. Romero Cardalda

  4. Dept. of Mathematics and Computer Science, Free University of Amsterdam, de Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands

    Elena Marchiori

  5. Schol of Systems Engineering, University of Reading, PO Box 225, Whiteknights, Reading, RG6 6AY, UK

    David W. Corne

  6. AnimatLab, 8 rue du capitaine Scott, 75015, Paris, France

    Jean-Arcady Meyer  & Agnès Guillot  & 

  7. SAP AG, Neurottstrasse 16, 69190, Walldorf, Germany

    Jens Gottlieb

  8. Parallel Computing and Complex Systems Group, University of Leipzig, Augustusplatz 10/11, 04109, Leipzig, Germany

    Martin Middendorf

  9. Algorithms and Data Structures Group Institute of Computer Graphics, Vienna University of Technology, Favoritenstrasse 9-11/186, 1040, Vienna, Austria

    Günther R. Raidl

  10. School of Computing, Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, UK

    Emma Hart

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© 2003 Springer-Verlag Berlin Heidelberg

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Jensen, M.T. (2003). Guiding Single-Objective Optimization Using Multi-objective Methods. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_25

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  • DOI: https://doi.org/10.1007/3-540-36605-9_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

  • eBook Packages: Springer Book Archive

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