• DocumentCode
    2326888
  • Title

    Defining locality in genetic programming to predict performance

  • Author

    Galván-López, Edgar ; McDermott, James ; O´Neill, Michael ; Brabazon, Anthony

  • Author_Institution
    Natural Comput. Res. & Applic. Group, Univ. Coll. Dublin, Dublin, Ireland
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    A key indicator of problem difficulty in evolutionary computation problems is the landscape´s locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype-fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.
  • Keywords
    genetic algorithms; mathematical programming; evolutionary computation problem; genetic programming; genotype-fitness mapping; genotype-phenotype mapping; performance prediction; Clouds; Context; Correlation; Distortion measurement; Encoding; Equations; Genetic programming;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2010 IEEE Congress on
  • Conference_Location
    Barcelona
  • Print_ISBN
    978-1-4244-6909-3
  • Type

    conf

  • DOI
    10.1109/CEC.2010.5586095
  • Filename
    5586095