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Fitness Clouds and Problem Hardness in Genetic Programming

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

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Abstract

This paper presents an investigation of genetic programming fitness landscapes. We propose a new indicator of problem hardness for tree-based genetic programming, called negative slope coefficient, based on the concept of fitness cloud. The negative slope coefficient is a predictive measure, i.e. it can be calculated without prior knowledge of the global optima. The fitness cloud is generated via a sampling of individuals obtained with the Metropolis-Hastings method. The reliability of the negative slope coefficient is tested on a set of well known and representative genetic programming benchmarks, comprising the binomial-3 problem, the even parity problem and the artificial ant on the Santa Fe trail.

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Authors and Affiliations

  1. I3S Laboratory, University of Nice, Sophia Antipolis, France

    Manuel Clergue, Philippe Collard & Sébastien Vérel

  2. Information Systems Department, University of Lausanne, Lausanne, Switzerland

    Leonardo Vanneschi & Marco Tomassini

Authors
  1. Leonardo Vanneschi
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  2. Manuel Clergue
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  3. Philippe Collard
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  4. Marco Tomassini
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  5. Sébastien Vérel
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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© 2004 Springer-Verlag Berlin Heidelberg

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Vanneschi, L., Clergue, M., Collard, P., Tomassini, M., Vérel, S. (2004). Fitness Clouds and Problem Hardness in Genetic Programming. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_76

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_76

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

  • eBook Packages: Springer Book Archive

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