• DocumentCode
    1634053
  • Title

    Chaos updating rotated gates quantum-inspired genetic algorithm

  • Author

    Hui, Chen ; Jiashu, Zhang ; Chao, Zhang

  • Author_Institution
    Signal & Inf. Process. Key Lab. of Sichuan Province, Southwest Jiaotong Univ., Chengdu, China
  • Volume
    2
  • fYear
    2004
  • Firstpage
    1108
  • Abstract
    From the recent research on combinational optimization of the knapsack problem, the quantum-inspired genetic algorithm (QGA) was proved to be better than conventional genetic algorithms. To accelerate the convergence speed of the QGA, the paper proposes research issues on QGA such as Q-gate. A novel Q-gate updating algorithm called chaos updating rotated gates quantum-inspired genetic algorithm (CQGA) is proposed. An analysis of the two main characters of quantum computing and chaos is also presented. This algorithm demonstrates the convergence of the quantum genetic algorithm (QGA). Several experiments are carried out on a class of numerical and combinatorial optimization problems. The results show the updated QGA makes QGA more powerful than the previous QGA in convergence speed.
  • Keywords
    chaos; combinatorial mathematics; convergence of numerical methods; genetic algorithms; knapsack problems; quantum computing; Q-gate updating algorithm; chaos; chaos updating rotated gates quantum-inspired genetic algorithm; combinational optimization; convergence speed; knapsack problem; quantum computing; quantum genetic algorithm; Chaos; Convergence; Evolutionary computation; Genetic algorithms; Information processing; Parameter estimation; Quantum computing; Quantum entanglement; Quantum mechanics; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Circuits and Systems, 2004. ICCCAS 2004. 2004 International Conference on
  • Print_ISBN
    0-7803-8647-7
  • Type

    conf

  • DOI
    10.1109/ICCCAS.2004.1346370
  • Filename
    1346370