• DocumentCode
    3310276
  • Title

    An Improved Mutative Scale Chaos Optimization Quantum Genetic Algorithm

  • Author

    Teng, Hao ; Zhao, Baohua ; Yang, Bingru

  • Author_Institution
    Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, Beijing
  • Volume
    6
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    301
  • Lastpage
    305
  • Abstract
    The theory of chaos optimization is introduced in this paper; and through improving the constringency strategy of mutative scale chaos optimization method, we can enhance the efficiency and the performance of chaos optimization method; then aiming at the trouble of easy getting into local minimum existed in quantum genetic algorithm, this paper presents a new chaos quantum genetic algorithm. Using the improved mutative scale chaos optimization method, chaotic search for the optimization is implemented to the population which is processed one time with the quantum genetic algorithm, which can lead to the rapid evolution of the population. The test of typical function shows that the performance of this method is better than quantum genetic algorithm and genetic algorithm.
  • Keywords
    chaos; genetic algorithms; quantum computing; chaos quantum genetic algorithm; mutative scale chaos optimization; Chaos; Convergence; Decision making; Genetic algorithms; Genetic engineering; Information science; Optimization methods; Quantum computing; Quantum mechanics; Testing; Chaos Optimization; Constringency Strategy; Mutative Scale; Quantum Genetic Algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.739
  • Filename
    4667849