• DocumentCode
    527480
  • Title

    An improved genetic algorithm and its application

  • Author

    Liu Gang ; Xuemei, Wang ; Yang Lina

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China
  • Volume
    7
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    3788
  • Lastpage
    3791
  • Abstract
    A novel hybrid learning algorithm based on a genetic algorithm, named simulated annealing genetic algorithm, is put forward in this paper to solve the optimization model of (N+M) fault-tolerant systems. A new adding method based on simulated annealing strategy is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithm and simulated annealing algorithm is used to adjust all parameters including crossover probability and mutation probability. Simulations are presented to illustrate the performance of the proposed algorithm.
  • Keywords
    fault tolerance; genetic algorithms; learning (artificial intelligence); simulated annealing; crossover probability; fault tolerant system; hybrid learning algorithm; mutation probability; optimization model; simulated annealing genetic algorithm; Biological system modeling; Control systems; Convergence; Fault tolerance; Fault tolerant systems; Simulated annealing; fault-tolerant system; genetic algorithm; optimization model; simulated annealing algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5582947
  • Filename
    5582947