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Some combinatorial landscapes on which a Genetic Algorithm outperforms other Stochastic iterative methods

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Evolutionary Computing (AISB EC 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 993))

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Abstract

It is of major practical and theoretical interest to investigate the conditions under which a Genetic Algorithm (GA) will perform better or worse than Simulated Annealing (SA) and/or Stochastic Hill-climbing (SH). Confusion of terms makes this investigation difficult, but at least the following fairly precise question can be asked: “What landscapes exist on which the employment of recombination and selection from a population lead to significant benefits over standard single-member SA and SH?” We show one class of landscape arising from a class of real-world problems which fits the bill. These are set-covering problems, in which the encoding used (hence defining the landscape) possesses a particular form of directed epistasis, Further, by distilling the features of this landscape which we feel are important to this empirical result, we introduce a general class of landscapes which also display this ‘GA advantage’.

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Authors and Affiliations

  1. Department of Artificial Intelligence, University of Edinburgh, 80 South Bridge, EH1 1HN, Edinburgh, UK

    Dave Corne & Peter Ross

Authors
  1. Dave Corne
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  2. Peter Ross
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Terence C. Fogarty

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© 1995 Springer-Verlag Berlin Heidelberg

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Corne, D., Ross, P. (1995). Some combinatorial landscapes on which a Genetic Algorithm outperforms other Stochastic iterative methods. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1995. Lecture Notes in Computer Science, vol 993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60469-3_20

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  • DOI: https://doi.org/10.1007/3-540-60469-3_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60469-3

  • Online ISBN: 978-3-540-47515-6

  • eBook Packages: Springer Book Archive

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