• DocumentCode
    618182
  • Title

    Island model genetic programming based on frequent trees

  • Author

    Ono, Keishi ; Hanada, Yoshiko ; Kumano, Masahito ; Kimura, Mizue

  • Author_Institution
    Dept. of Electron. & Inf., Ryukoku Univ., Kyoto, Japan
  • fYear
    2013
  • fDate
    20-23 June 2013
  • Firstpage
    2988
  • Lastpage
    2995
  • Abstract
    The Island Model encourages genetic diversity, and often displays better search performance than single population models. In order to enhance the Island Model in the framework of genetic programming (GP), we propose a novel migration strategy based on frequent trees, where the frequent trees in an island mean the sub-trees appearing frequently among the individuals in the island. The proposed method evaluates each island by measuring its activation level in terms of not only how high the best fitness value is but also how many types of frequent trees are newly created, and then makes several individuals migrate from an island with high activation level to an island with low activation level, and vice versa. Using three benchmark problems widely adopted in the literature, we demonstrate that performance improvement can be achieved through incorporating the information of frequent trees into a migration strategy, and the proposed method significantly outperforms a typical method of the Island Model GP.
  • Keywords
    genetic algorithms; trees (mathematics); GP; fitness value; frequent tree; genetic diversity; genetic programming; island model; migration strategy; Benchmark testing; Computational modeling; Genetic programming; Sociology; Statistics; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2013 IEEE Congress on
  • Conference_Location
    Cancun
  • Print_ISBN
    978-1-4799-0453-2
  • Electronic_ISBN
    978-1-4799-0452-5
  • Type

    conf

  • DOI
    10.1109/CEC.2013.6557933
  • Filename
    6557933