Diversity analysis in cellular and multipopulation genetic programming

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

  author =       "G. Folino and C. Pizzuti and G. Spezzano and 
                 L. Vanneschi and M. Tomassini",
  title =        "Diversity analysis in cellular and multipopulation
                 genetic programming",
  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 =        "305--311",
  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, Computer
                 science, Convergence, Costs, Evolutionary computation,
                 Genetic mutations, Measurement standards, Performance
                 analysis, Size measurement, Testing, convergence,
                 parallel algorithms, statistical analysis, cellular
                 genetic programming, convergence, diversity analysis,
                 diversity measures, evolution, multipopulation genetic
                 programming, parallel genetic programming model,
                 population diversity",
  URL =          "http://www.icar.cnr.it/pizzuti/cec03.pdf",
  DOI =          "doi:10.1109/CEC.2003.1299589",
  ISBN =         "0-7803-7804-0",
  abstract =     "parallel genetic programming (GP) models in
                 maintaining diversity in a population. The parallel
                 models used are the cellular and the multipopulation
                 one. Several measures of diversity are considered to
                 gain a deeper understanding of the conditions under
                 which the evolution of both models is successful. Three
                 standard test problems are used to illustrate the
                 different diversity measures and analyse their
                 correlation with performance. Results show that
                 diversity is not necessarily synonym of good
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Gianluigi Folino Clara Pizzuti Giandomenico Spezzano Leonardo Vanneschi Marco Tomassini