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International Conference on Parallel Problem Solving from Nature

PPSN 1996: Parallel Problem Solving from Nature — PPSN IV pp 574–583Cite as

Climbing up NP-hard hills

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

Abstract

Evolutionary algorithms are sophisticated hill-climbers. In this paper, we discuss the ability of this class of local search algorithms to provide useful and efficient heuristics to solve NP-hard problems. Our discussion is illustrated on experiments aiming at solving the job-shop-scheduling problem. We focus on the components of the EA, pointing out the importance of the objective function as well as the manner the operators are applied. Experiments clearly show the efficiency of local search methods in this context, the trade-off between “pure” and hybrid algorithms, as well as the very good performance obtained by simple hill-climbing algorithms. This work has to be regarded as a step towards a better understanding of the way search algorithms wander in a fitness landscape.

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Author information

Authors and Affiliations

  1. Laboratoire d'Informatique du Littoral, BP 719, 62228, Calais Cedex, France

    D. Duvivier

  2. Laboratoire d'Informatique du Littoral, France

    Ph. Preux

  3. Laboratoire d'Informatique Fondamentale de Lille, URA CNRS 369, Cité scientifique, 59655, Villeneuve d'Ascq Cedex, France

    E. -G. Talbi

Authors
  1. D. Duvivier
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  2. Ph. Preux
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  3. E. -G. Talbi
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Duvivier, D., Preux, P., Talbi, E.G. (1996). Climbing up NP-hard hills. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1021

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  • DOI: https://doi.org/10.1007/3-540-61723-X_1021

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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