• DocumentCode
    2749071
  • Title

    Immune evolutionary algorithms

  • Author

    Lei, Wang ; Licheng, Jiao

  • Author_Institution
    Key Lab. for Radar Signal Process., Xidian Univ., Xi´´an, China
  • Volume
    3
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    1655
  • Abstract
    Three evolutionary algorithms, the immune genetic algorithm (IGA), the immune evolutionary programming (IEP) and the immune evolutionary strategy (IES), are presented based on the immune theory in biology, which are not only convergent but used for solving complex discrete optimization problems as well. They all construct an immune operator accomplished by two components, vaccination and immune selection. The methods for selecting vaccines and constructing an immune operator are also proposed. Simulations show that these algorithms can restrain the degenerate phenomenon and improve the searching capability of the existing algorithms, therefore increase the convergent speed greatly
  • Keywords
    computational complexity; convergence; evolutionary computation; travelling salesman problems; degenerate phenomenon; immune evolutionary programming; immune evolutionary strategy; immune genetic algorithm; immune selection; searching capability; vaccination; Biological system modeling; Biology computing; Computational modeling; Evolution (biology); Evolutionary computation; Genetic algorithms; Genetic programming; Immune system; Power engineering and energy; Signal processing algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-5747-7
  • Type

    conf

  • DOI
    10.1109/ICOSP.2000.893419
  • Filename
    893419