• DocumentCode
    3304125
  • Title

    On learning kDNFns Boolean formulas

  • Author

    Hernandez-Aquirre, A. ; Buckles, Bill P. ; Coello, Carlos A Coello

  • Author_Institution
    Dept. of Electr. Eng. & Comput. Sci., Tulane Univ., New Orleans, LA, USA
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    240
  • Lastpage
    246
  • Abstract
    The number of samples needed to learn an instance of the representation class kDNFns of Boolean formulas is predicted using some tolerance parameters by the PAC framework. When the learning machine is a simple genetic algorithm, the initial population is an issue. Using PAC-learning we derive the population size that has at least one individual at a given Hamming distance from the optimum. Then we show that the GA evolves solutions from initial populations rather far (Hamming distance) from the optimum
  • Keywords
    Boolean functions; genetic algorithms; learning (artificial intelligence); Boolean formulas; PAC-learning; genetic algorithm; learning machine; Biological cells; Error correction; Genetic algorithms; Hamming distance; Machine learning; Neural networks; Terminology; Testing; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolvable Hardware, 2001. Proceedings. The Third NASA/DoD Workshop on
  • Conference_Location
    Long Beach, CA
  • Print_ISBN
    0-7695-1180-5
  • Type

    conf

  • DOI
    10.1109/EH.2001.937967
  • Filename
    937967
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3304125