Fitness distance correlation in genetic programming: A constructive counterexample

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@InProceedings{vanneschi:2003:fdcigpacc,
  author =       "L. Vanneschi and M. Tomassini and P. Collard and 
                 M. Clergue",
  title =        "Fitness distance correlation in genetic programming: A
                 constructive counterexample",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "289--296",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Algorithm
                 design and analysis, Genetic mutations, Hamming
                 distance, Laboratories, Sampling methods, Statistics,
                 Stochastic processes, Tree data structures, statistical
                 analysis, constructive counterexample, fitness distance
                 correlation coefficient, hand-tailored function,
                 infallible measure, problem difficulty",
  ISBN =         "0-7803-7804-0",
  DOI =          "doi:10.1109/CEC.2003.1299587",
  abstract =     "The fitness distance correlation coefficient has been
                 shown to be a reasonable measure to quantify problem
                 difficulty in genetic algorithms and genetic
                 programming for a wide set of problems. In this paper
                 we present an hand-tailored function for which fitness
                 distance correlation fails to correctly predict problem
                 difficulty in genetic programming. This counterexample
                 proves that fitness distance correlation, although
                 reliable, is not an infallible measure to quantify
                 problem difficulty.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",
}

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

Citations