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European Conference on Genetic Programming

EuroGP 2003: Genetic Programming pp 455–464Cite as

Fitness Distance Correlation in Structural Mutation Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2610))

Abstract

A new kind of mutation for genetic programming based on the structural distance operators for trees is presented in this paper. We firstly describe a new genetic programming process based on these operators (we call it structural mutation genetic programming). Then we use structural distance to calculate the fitness distance correlation coefficient and we show that this coefficient is a reasonable measure to express problem difficulty for structural mutation genetic programming for the considered set of problems, i.e. unimodal trap functions, royal trees and MAX problem.

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Author information

Authors and Affiliations

  1. Computer Science Institute, University of Lausanne, Lausanne, Switzerland

    Leonardo Vanneschi & Marco Tomassini

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

    Philippe Collard & Manuel Clergue

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

Editors and Affiliations

  1. Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Conor Ryan

  2. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    Terence Soule

  3. Department of Computer Science, Free University of Amsterdam, 1081, HV Amsterdam, The Netherlands

    Maarten Keijzer

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Edward Tsang  & Riccardo Poli  & 

  5. Department of Computer Science, University of Coimbra, Polo II, Pinhal Marrocos, 3030-290, Coimbra, Portugal

    Ernesto Costa

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© 2003 Springer-Verlag Berlin Heidelberg

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Vanneschi, L., Tomassini, M., Collard, P., Clergue, M. (2003). Fitness Distance Correlation in Structural Mutation Genetic Programming. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_43

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  • DOI: https://doi.org/10.1007/3-540-36599-0_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00971-9

  • Online ISBN: 978-3-540-36599-0

  • eBook Packages: Springer Book Archive

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