A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{tomassini:2005:EC,
  author =       "Marco Tomassini and Leonardo Vanneschi and 
                 Philippe Collard and Manuel Clergue",
  title =        "A Study of Fitness Distance Correlation as a
                 Difficulty Measure in Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2005",
  volume =       "13",
  number =       "2",
  pages =        "213--239",
  month =        "Summer",
  keywords =     "genetic algorithms, genetic programming, problem
                 difficulty, program landscapes, fitness distance
                 correlation",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/1063656054088549",
  size =         "27 pages",
  abstract =     "We present an approach to genetic programming
                 difficulty based on a statistical study of program
                 fitness landscapes. The fitness distance correlation is
                 used as an indicator of problem hardness and we
                 empirically show that such a statistic is adequate in
                 nearly all cases studied here. However, fitness
                 distance correlation has some known problems and these
                 are investigated by constructing an artificial
                 landscape for which the correlation gives contradictory
                 indications. Although our results confirm the
                 usefulness of fitness distance correlation, we point
                 out its shortcomings and give some hints for
                 improvement in assessing problem hardness in genetic
                 programming.",
  publisher =    "MIT Press",
  notes =        "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25",
}

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

Citations