• DocumentCode
    2998538
  • Title

    Diversity analysis in cellular and multipopulation genetic programming

  • Author

    Folino, G. ; Pizzuti, C. ; Spezzano, G. ; Vanneschi, L. ; Tomassini, M.

  • Author_Institution
    ICAR-CNR, Rende, Italy
  • Volume
    1
  • fYear
    2003
  • fDate
    8-12 Dec. 2003
  • Firstpage
    305
  • Abstract
    This paper presents a study that evaluates the influence of the 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 analyze their correlation with performance. Results show that diversity is not necessarily synonym of good convergence.
  • Keywords
    convergence; genetic algorithms; parallel algorithms; statistical analysis; cellular genetic programming; convergence; diversity analysis; diversity measures; evolution; multipopulation genetic programming; parallel genetic programming model; population diversity; Computer science; Convergence; Costs; Evolutionary computation; Genetic mutations; Genetic programming; Measurement standards; Performance analysis; Size measurement; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
  • Print_ISBN
    0-7803-7804-0
  • Type

    conf

  • DOI
    10.1109/CEC.2003.1299589
  • Filename
    1299589