• DocumentCode
    2495441
  • Title

    Research on genetic algorithm theory and its application on missile fire allocation

  • Author

    Feng, Jie ; Jiang, Ning ; Wang, Jitang ; Wang, Jun

  • Author_Institution
    Dept. of combat command, Dalian Naval Acad., Zhongshan
  • fYear
    2008
  • fDate
    25-27 June 2008
  • Firstpage
    7032
  • Lastpage
    7035
  • Abstract
    Genetic algorithm (GA) is a kind of recently developing optimization algorithm, which can be widely used in solving many practical problems, such as function optimization, graphics recognition, machine learning, artificial neural network, artificial life, optimization scheduling etc. First, a kind of genetic algorithm (GA) and the optimization problems were defined. Then the operations of genetic algorithm including selection, crossover and mutation were described. Finally, its application on missile fire allocation is illustrated. The optimization problem of missile fire allocation was described and its mathematical model was established. Solving the optimization problem of missile fire allocation with GA was demonstrated including problem coding. The production of the initial population, fitness computation, selection, crossover and mutation. The result shows that GA can solve nonlinear optimization problems efficiently.
  • Keywords
    genetic algorithms; military systems; missiles; fitness computation; genetic algorithm; mathematical model; missile fire allocation; nonlinear optimization; problem coding; Artificial neural networks; Fires; Genetic algorithms; Genetic mutations; Graphics; Machine learning; Machine learning algorithms; Mathematical model; Missiles; Scheduling algorithm; Genetic Algorithm; missile fire allocation; nonlinear optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4244-2113-8
  • Electronic_ISBN
    978-1-4244-2114-5
  • Type

    conf

  • DOI
    10.1109/WCICA.2008.4594006
  • Filename
    4594006