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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2001: Evolutionary Multi-Criterion Optimization pp 429–442Cite as

The Supported Solutions Used as a Genetic Information in a Population Heuristic

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1993))

Abstract

Population heuristics present native abilities for solving optimization problems with multiple objectives. The convergence to the efficient frontier is improved when the population contains à good genetic information’. In the context of combinatorial optimization problems with two objectives, the supported solutions are used to elaborate such information, defining a resolution principle in two phases. First the supported efficient solution set, or an approximation, is computed. Second this information is used to improve the performance of a population heuristic during the generation of the effiient frontier. This principle has been experimented on two classes of problems : the 1 | | (ΣCi ; Tmax) permutation scheduling problems, and the biobjective 0-1 knapsack problems. The motivations of this principle are developed. The numerical experiments are reported and discussed.

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References

  1. J.E. Beasley Population heuristics, Working paper, March 1999, The Management School, Imperial College, London, England (1999).

    Google Scholar 

  2. Ch.-L. Chen and R. Bulfin, Complexity of single machine, multi-criteria scheduling problems, European Journal of Operational Research, 70, pp. 115–125 (1993).

    Article  MATH  Google Scholar 

  3. M. Ehrgott. Multiple Criteria Optimization — Classification and Methodology. Shaker Verlag, Aachen (1997).

    Google Scholar 

  4. M. Ehrgott and X. Gandibleux, A survey and annotated bibliography of multi-objective combinatorial optimization, OR-Spectrum, Vol.22, No.4, pp.425–460 (2000).

    MATH  MathSciNet  Google Scholar 

  5. M. Ehrgott and X. Gandibleux, Bounds and bound sets for biobjective combinato-rial optimization problems, Technical report (to appear in the proceedings of 15th MCDM International Conference), University of Auckland, New-Zealand (2000).

    Google Scholar 

  6. X. Gandibleux, H. Morita and N. Katoh, A genetic Algorithm for 0-1 MultiObjec-tive Knapsack Problem, Technical report (presented at NACA98, July 28-31 1998, Niigata, Japan), University of Valenciennes, France (1998).

    Google Scholar 

  7. A. M. Geoffrion, Proper efficiency and the theory of vector maximization. Journal of Mathematical Analysis and Applications, 22, pp. 618–630 (1968).

    Article  MATH  MathSciNet  Google Scholar 

  8. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learn-ing, Addison-Wesley (1989).

    Google Scholar 

  9. A. Hertz and D. Kobler, A framework for the Description of Population Based Methods, EUROXVI, Tutorials and Research Reviews, 23 pages (1998).

    Google Scholar 

  10. S. Martello and P. Toth. Knapsack Problems-Algorithms and Computer Implemen-tations. John Wiley & Sons, Chichester (1990).

    Google Scholar 

  11. H. Morita and N. Katoh, A Genetic algorithm for a bicriteria flow-shop schedul-ing problem (in Japanese), Transactions of the Institute of Systems, Control and Information Engineers, Vol.10, No.3, pp.127–136 (1997).

    Google Scholar 

  12. H. Morita, X. Gandibleux and N. Katoh, Experimental feedback on biobjective permutation scheduling problems solved with a population heuristic, Technical report(submitted to Foundations of Computing and Decision Sciences Journal), University of Valenciennes, France (2000).

    Google Scholar 

  13. A. Nagar, J. Haddock and S. Heragu, Multiple and bicriterion scheduling: A liter-ature survey, European Journal of Operational Research, Vol.81, pp.88–104 (1995).

    Article  MATH  Google Scholar 

  14. E.L. Ulungu, J. Teghem and P. Fortemps, Heuristics for multi-objective combinato-rial optimization problem by simulated annealing, MCDM: Theory and applications (J. Gu, G. Chen, Q. Wei and S. Wang Edts.), SCI-TECH Information services, pp. 228–238 (1995).

    Google Scholar 

  15. L. N. Van Wassenhove and L. F. Gelders, Solving a bicriterion scheduling problem, European Journal of Operational Research, Vol.4, pp.42–48 (1980).

    Article  MathSciNet  Google Scholar 

  16. M. Visée, J. Teghem, M. Pirlot, and E.L. Ulungu. Two-phases method and branch and bound procedures to solve the bi-objective knapsack problem. Journal of Global Optimization, 12, pp. 139–155 (1998).

    Article  MATH  Google Scholar 

  17. MCDM Numerical Instances Library, MultiObjective Combinatorial Optimization Problems, http://www.univ-valenciennes.fr/ROAD/MCDM/.

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Author information

Authors and Affiliations

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

    Xavier Gandibleux

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

    Hiroyuki Morita

  3. Kyoto University, Yoshidahonmachi, 606-8501, Sakyo wardKyoto, 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. Department of Electrical Engineering Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, ETH Zentrum, Gloriastrasse 35, 8092, Zurich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

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

    Kalyanmoy Deb

  3. Electrical Engineering Department,Computer Science Section Av.Instituto Politecnico Nacional No.2508, CINVESTAV-IPN, Col.San Pedro Zacatenco, 07300, Mexico City, Mexico

    Carlos Artemio Coello Coello

  4. Department of Computer Science, University of Reading, P.O.Box 225, Reading RG6 6AY, Whiteknights, UK

    David Corne

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© 2001 Springer-Verlag Berlin Heidelberg

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Gandibleux, X., Morita, H., Katoh, N. (2001). The Supported Solutions Used as a Genetic Information in a Population Heuristic. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_30

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  • DOI: https://doi.org/10.1007/3-540-44719-9_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41745-3

  • Online ISBN: 978-3-540-44719-1

  • eBook Packages: Springer Book Archive

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