• DocumentCode
    1635144
  • Title

    Quantum-inspired genetic algorithms

  • Author

    Narayanan, Ajit ; Moore, Mark

  • Author_Institution
    Dept. of Comput. Sci., Exeter Univ., UK
  • fYear
    1996
  • Firstpage
    61
  • Lastpage
    66
  • Abstract
    A novel evolutionary computing method-quantum inspired genetic algorithms-is introduced, where concepts and principles of quantum mechanics are used to inform and inspire more efficient evolutionary computing methods. The basic terminology of quantum mechanics is introduced before a comparison is made between a classical genetic algorithm and a quantum inspired method for the travelling salesperson problem. It is informally shown that the quantum inspired genetic algorithm performs better than the classical counterpart for a small domain. The paper concludes with some speculative comments concerning the relationship between quantum inspired genetic algorithms and various complexity classes
  • Keywords
    computational complexity; genetic algorithms; quantum theory; travelling salesman problems; complexity classes; evolutionary computing; genetic algorithm; performance; quantum inspired genetic algorithms; quantum mechanics; terminology; travelling salesperson problem; Computational modeling; Computer science; Electrons; Energy states; Genetic algorithms; Orbits; Quantum computing; Quantum mechanics; Vectors; Wave functions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
  • Conference_Location
    Nagoya
  • Print_ISBN
    0-7803-2902-3
  • Type

    conf

  • DOI
    10.1109/ICEC.1996.542334
  • Filename
    542334