• DocumentCode
    2171317
  • Title

    New genetic algorithm improved and its applications

  • Author

    Xin, Zhao ; Chunbo, Xiu

  • Author_Institution
    Sch. of Electr. Eng. & Autom., Tianjin Polytech. Univ., Tianjin, China
  • fYear
    2011
  • fDate
    9-11 Sept. 2011
  • Firstpage
    926
  • Lastpage
    928
  • Abstract
    A novel genetic algorithm, named double population genetic algorithm (DPGA), is proposed to improve the performance of the conventional genetic algorithm. An elaborate searching space around the current optimal solution is divided from the original searching space. One small population executes genetic operators to speed up the convergence of the algorithm in the elaborate searching space. And the boundaries of the elaborate searching space are reduced continuously to enhance the searching density during the optimization. Another big population executes genetic operators to ensure the global optimal ability of the algorithm. In this way, the algorithm has global searching ability and fast convergence rate. A lot of simulation results prove that the algorithm can accelerate searching rate, enhance the searching efficiency, and give satisfied results to function optimization problems.
  • Keywords
    convergence; genetic algorithms; double population genetic algorithm; elaborate searching space; fast convergence rate; function optimization problem; genetic operators; global optimal ability; global searching ability; optimal solution; Algorithm design and analysis; Convergence; Educational institutions; Genetic algorithms; Genetics; Optimization; Search problems; elaborate searching space; function optimization; genetic algorithm; population;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics, Communications and Control (ICECC), 2011 International Conference on
  • Conference_Location
    Ningbo
  • Print_ISBN
    978-1-4577-0320-1
  • Type

    conf

  • DOI
    10.1109/ICECC.2011.6066413
  • Filename
    6066413