Fitness Distance Correlation in Structural Mutation Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Leonardo Vanneschi and Marco Tomassini and 
                 Philippe Collard and Manuel Clergue",
  title =        "Fitness Distance Correlation in Structural Mutation
                 Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "455--464",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "3-540-00971-X",
  URL =          "",
  DOI =          "doi:10.1007/3-540-36599-0_43",
  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
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops

Genetic Programming entries for Leonardo Vanneschi Marco Tomassini Philippe Collard Manuel Clergue