• DocumentCode
    3014775
  • Title

    Improved Genetic Programming Algorithm

  • Author

    Cheng, Huifang ; Zhang, Yongqiang ; Li, Fangping

  • Author_Institution
    Sch. of Inf. & Electr. Eng., Hebei Univ. of Eng., Handan, China
  • fYear
    2009
  • fDate
    8-9 Dec. 2009
  • Firstpage
    168
  • Lastpage
    171
  • Abstract
    The present study aims at improving the problem solving ability of the canonical genetic programming algorithm. The proposed method can be described as follows. The first investigates initialising population, the second investigates reproduction operator, the third investigates crossover operator, the fourth investigates mutation operation. This approach is examined on two experiments about symbolic regression. The results suggest that the new approach is more effective and more efficient than the canonical one.
  • Keywords
    genetic algorithms; regression analysis; canonical genetic programming algorithm; crossover operator; mutation operation; problem solving; reproduction operator; symbolic regression; Asia; Convergence; Educational institutions; Genetic algorithms; Genetic engineering; Genetic mutations; Genetic programming; Problem-solving; Random number generation; Wheels; Convergence; Genetic Programming; Novel method; Operator;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Interaction and Affective Computing, 2009. ASIA '09. International Asia Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3910-2
  • Electronic_ISBN
    978-1-4244-5406-8
  • Type

    conf

  • DOI
    10.1109/ASIA.2009.39
  • Filename
    5376006