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Use of a Genetic Heritage for Solving the Assignment Problem with Two Objectives

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

The paper concerns a multiobjective heuristic to compute approximate efficient solutions for the assignment problem with two objectives. The aim here is to show that the genetic information extracted from supported solutions constitutes a useful genetic heritage to be used by crossover operators to approximate non-supported solutions. Bound sets describe one acceptable limit for applying a local search over an offspring. Results of extensive numerical experiments are reported. All exact efficient solutions are obtained using Cplex in a basic enumerative procedure. A comparison with published results shows the efficiency of this approach.

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References

  1. M. Ehrgott and X. Gandibleux, Multiobjective Combinatorial Optimization. In Multiple Criteria Optimization: State of the Art Annotated Bibliographic Survey (M. Ehrgott and X. Gandibleux Eds.), pp. 369–444, Kluwer’s International Series in Operations Research and Management Science: Volume 52, Kluwer Academic Publishers, Boston, 2002.

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Author information

Authors and Affiliations

  1. LAMIH/ROI - UMR CNRS 8530, Université de Valenciennes, Campus “Le Mont Houy”, F-59313, Valenciennes cedex 9, France

    Xavier Gandibleux

  2. Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka, 599-8231, Japan

    Hiroyuki Morita

  3. Kyoto University, Yoshidahonmachi, Sakyo ward, Kyoto, 606-8501, Japan

    Naoki Katoh

Authors
  1. Xavier Gandibleux
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  2. Hiroyuki Morita
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  3. Naoki Katoh
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Gandibleux, X., Morita, H., Katoh, N. (2003). Use of a Genetic Heritage for Solving the Assignment Problem with Two Objectives. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_4

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  • DOI: https://doi.org/10.1007/3-540-36970-8_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

  • eBook Packages: Springer Book Archive

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