• DocumentCode
    2995378
  • Title

    Learning, evolution and generalisation

  • Author

    Kushchu, Ibrahim

  • Author_Institution
    GSIM, Int. Univ. of Japan, Niigata, Japan
  • Volume
    4
  • fYear
    2003
  • fDate
    8-12 Dec. 2003
  • Firstpage
    2441
  • Abstract
    The conventional machine learning approaches can provide well established experimental basis for genetic based learners. Examination of the research imply that the link between evolutionary learning and conventional learning studies may be improved. This is especially true in terms of practices adopting generalisation as a performance evaluation criterion for learning. In this paper an overview of significant number of experiments from classifier systems and genetic programming are presented. Suggestions to accommodate generalisation in the context of evolutionary learning are provided within a generic learning framework and its implication for evolutionary generalisation is discussed.
  • Keywords
    evolutionary computation; generalisation (artificial intelligence); learning (artificial intelligence); pattern classification; classifier systems; evolutionary learning; generalisation; genetic programming; machine learning; Artificial intelligence; Bridges; Design methodology; Genetic programming; Learning systems; Machine learning; Problem-solving; 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.2003.1299394
  • Filename
    1299394