Operator-Based Distance for Genetic Programming: Subtree Crossover Distance

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Steven Gustafson and Leonardo Vanneschi",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Operator-Based Distance for Genetic Programming:
                 Subtree Crossover Distance",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "178--189",
  URL =          "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-vanneschi.ps",
  URL =          "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-vanneschi.pdf",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "This paper explores distance measures based on genetic
                 operators for genetic programming using tree
                 structures. The consistency between genetic operators
                 and distance measures is a crucial point for analytical
                 measures of problem difficulty, such as fitness
                 distance correlation, and for measures of population
                 diversity, such as entropy or variance. The
                 contribution of this paper is the exploration of
                 possible definitions and approximations of
                 operator-based edit distance measures. In particular,
                 we focus on the subtree crossover operator. An
                 empirical study is presented to illustrate the features
                 of an operator-based distance. This paper makes
                 progress toward improved algorithmic analysis by using
                 appropriate measures of distance and similarity.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",

Genetic Programming entries for Steven M Gustafson Leonardo Vanneschi