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A critical and empirical study of epistasis measures for predicting GA performances: A summary

  • Methodologies
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Artificial Evolution (AE 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1363))

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Abstract

Epistasis measures have been developed for measuring statistical information about the difficulty of a function to be optimized by a genetic algorithm (GA). We give first a review of the work on these measures such as the epistasis correlation. Then we try to relate the epistasis correlation to the overall performances of a binary GA on a set of 14 functions. The only relation that seems to hold strongly is that a high epistasis correlation implies GA-easy, as indicated by the GA theory of deceptiveness. Then, we show that changing the representation of the search space with transformations that improve epistasis measures does not imply the same increasing in the genetic algorithm performances. These both empirical studies seem to indicate that the generality of epistasis measures is limited.

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Author information

Authors and Affiliations

  1. IUT, Dép. Génie Electrique, 150, Avenue du Maréchal Leclerc, 13300, Salon de Provence, France

    S. Rochet

  2. Laboratoire d'informatique, Université de Tours, 64, Avenue Jean Portalis, 37200, Tours, France

    G. Venturini, M. Slimane & E. M. El Kharoubi

Authors
  1. S. Rochet
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  2. G. Venturini
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  3. M. Slimane
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  4. E. M. El Kharoubi
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Editor information

Jin-Kao Hao Evelyne Lutton Edmund Ronald Marc Schoenauer Dominique Snyers

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© 1998 Springer-Verlag Berlin Heidelberg

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Rochet, S., Venturini, G., Slimane, M., El Kharoubi, E.M. (1998). A critical and empirical study of epistasis measures for predicting GA performances: A summary. In: Hao, JK., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds) Artificial Evolution. AE 1997. Lecture Notes in Computer Science, vol 1363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026607

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  • DOI: https://doi.org/10.1007/BFb0026607

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64169-8

  • Online ISBN: 978-3-540-69698-8

  • eBook Packages: Springer Book Archive

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