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Effective Use of Directional Information in Multi-objective Evolutionary Computation

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2723))

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Abstract

While genetically inspired approaches to multi-objective optimization have many advantages over conventional approaches, they do not explicitly exploit directional/gradient information. This paper describes how steepest-descent, multi-objective optimization theory can be combined with EC concepts to produce improved algorithms. It shows how approximate directional information can be efficiently extracted from parent individuals, and how a multi-objective gradient can be calculated, such that children individuals can be placed in appropriate, dominating search directions. The paper describes and introduces the basic theoretical concepts as well as demonstrating some of the concepts on a simple test problem.

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Author information

Authors and Affiliations

  1. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Martin Brown

  2. The Intelligent Computer Systems Centre, The University of The West of England, Bristol, UK

    Robert E. Smith

Authors
  1. Martin Brown
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  2. Robert E. Smith
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Brown, M., Smith, R.E. (2003). Effective Use of Directional Information in Multi-objective Evolutionary Computation. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_92

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  • DOI: https://doi.org/10.1007/3-540-45105-6_92

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

  • eBook Packages: Springer Book Archive

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