Fitness Clouds and Problem Hardness in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Leonardo Vanneschi and Manuel Clergue and 
                 Philippe Collard and Marco Tomassini and S\'ebastien V\'erel",
  title =        "Fitness Clouds and Problem Hardness in Genetic
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part II",
  year =         "2004",
  editor =       "Kalyanmoy Deb and Riccardo Poli and 
                 Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and 
                 Paul Darwen and Dipankar Dasgupta and Dario Floreano and 
                 James Foster and Mark Harman and Owen Holland and 
                 Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and 
                 Dirk Thierens and Andy Tyrrell",
  series =       "Lecture Notes in Computer Science",
  pages =        "690--701",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3103",
  ISBN =         "3-540-22343-6",
  ISSN =         "0302-9743",
  URL =          "",
  DOI =          "doi:10.1007/b98645",
  size =         "12",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Abstract. This paper presents an investigation of
                 genetic programming fitness landscapes. We propose a
                 new indicator of problem hardness for tree-based
                 genetic programming, called negative slope coefficient,
                 based on the concept of fitness cloud. The negative
                 slope coefficient is a predictive measure, i.e. it can
                 be calculated without prior knowledge of the global
                 optima.The fitness cloud is generated via a sampling of
                 individuals obtained with the Metropolis-Hastings
                 method. The reliability of the negative slope
                 coefficient is tested on a set of well known and
                 representative genetic programming benchmarks,
                 comprising the binomial-3 problem, the even parity
                 problem and the artificial ant on the Santa Fe trail.",
  notes =        "GECCO-2004 A joint meeting of the thirteenth
                 international conference on genetic algorithms
                 (ICGA-2004) and the ninth annual genetic programming
                 conference (GP-2004)",

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