• DocumentCode
    2350413
  • Title

    Optimizing lineage information in genetic algorithms for producing superior models

  • Author

    Boetticher, Gary D. ; Rudisill, Jason

  • Author_Institution
    University of Houston ¿ Clear Lake, Houston, TX 77058, USA
  • fYear
    2008
  • fDate
    13-15 July 2008
  • Firstpage
    314
  • Lastpage
    318
  • Abstract
    A lot of research in the area of genetic algorithms (GA) is applied, but little research examines the impact of lineage information in optimizing a GA. Normally, researchers consider primarily elitism, an approach which carries only a very small fixed subset of the population to the next generation, as a lineage strategy. This paper investigates several different lineage percentages (what percent of the population to carry forward) to determine an ideal percentage or range from improving the accuracy of a GA. Several experiments are performed, and all results are statistically validated.
  • Keywords
    Biological cells; Chromosome mapping; Couplings; Equations; Genetic algorithms; Genetic mutations; Lakes; Learning systems; Noise generators; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Reuse and Integration, 2008. IRI 2008. IEEE International Conference on
  • Conference_Location
    Las Vegas, NV, USA
  • Print_ISBN
    978-1-4244-2659-1
  • Electronic_ISBN
    978-1-4244-2660-7
  • Type

    conf

  • DOI
    10.1109/IRI.2008.4583049
  • Filename
    4583049