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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2009: Evolutionary Multi-Criterion Optimization pp 423–437Cite as

Online Objective Reduction to Deal with Many-Objective Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5467))

Abstract

In this paper, we propose and analyze two schemes to integrate an objective reduction technique into a multi-objective evolutionary algorithm (moea) in order to cope with many-objective problems. One scheme reduces periodically the number objectives during the search until the required objective subset size has been reached and, towards the end of the search, the original objective set is used again. The second approach is a more conservative scheme that alternately uses the reduced and the entire set of objectives to carry out the search. Besides improving computational efficiency by removing some objectives, the experimental results showed that both objective reduction schemes also considerably improve the convergence of a moea in many-objective problems.

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Author information

Authors and Affiliations

  1. Departamento de Computación, CINVESTAV-IPN (Evolutionary Computation Group), Av. IPN No. 2508, Col. San Pedro Zacatenco, México, D.F. 07360, México

    Antonio López Jaimes, Carlos A. Coello Coello & Jesús E. Urías Barrientos

Authors
  1. Antonio López Jaimes
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  2. Carlos A. Coello Coello
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  3. Jesús E. Urías Barrientos
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Editor information

Editors and Affiliations

  1. Department of Engineering Science, The University of Auckland, 70 Symonds Street, Room 415, 1001, Auckland, New Zealand

    Matthias Ehrgott

  2. Faculty of Science and Technolgoy, Department of Electronic Engineering and Informatics, Universidade do Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  3. Laboratoire d’ Informatique de Nantes -LINA, UMR CNRS 6241, Université de Nantes, 2, Rue de la Houssinière, BP 92208, 44322, Nantes Cedex 03, France

    Xavier Gandibleux

  4. LERIA, Faculty of Sciences, Université d’Angers, 2 Boulevard Lavoisier, 49045, Angers Cedex 01, France

    Jin-Kao Hao

  5. Université de Bretagne-sud - UEB CNRS, UMR 3192 - Lab-STICC, Centre de Recherche,, BP 92116, 56321, Lorient Cedex, France

    Marc Sevaux

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López Jaimes, A., Coello, C.A.C., Urías Barrientos, J.E. (2009). Online Objective Reduction to Deal with Many-Objective Problems. In: Ehrgott, M., Fonseca, C.M., Gandibleux, X., Hao, JK., Sevaux, M. (eds) Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01020-0_34

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  • DOI: https://doi.org/10.1007/978-3-642-01020-0_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01019-4

  • Online ISBN: 978-3-642-01020-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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