• DocumentCode
    2787586
  • Title

    The DNA genetic algorithm applied for solving stochastic integer programming expected value models

  • Author

    Wang, Ming-chun ; Tang, Wan-sheng ; Liu, Xin

  • Author_Institution
    Syst. Eng. Inst., Tianjin Univ., Tianjin
  • Volume
    2
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    1020
  • Lastpage
    1024
  • Abstract
    In this paper, how to use DNA genetic algorithm to solve stochastic integer programming expected value models is discussed. Since DNA genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly. These merits and the technique of stochastic simulation are combined, which for estimating the random variables of stochastic integer programming expected value models problem. Base on them, a best solution of this problem can be found. The classical newspaper-selling boy problem is calculated for testifying the feasibility and effectiveness of this method.
  • Keywords
    biocomputing; estimation theory; genetic algorithms; integer programming; mathematics computing; random processes; stochastic programming; DNA genetic algorithm; decoding; newspaper-selling boy problem; random variables estimation; stochastic integer programming expected value models; stochastic simulation; Cybernetics; DNA; Decoding; Genetic algorithms; Linear programming; Machine learning; Mathematical programming; Programming profession; Stochastic processes; Stochastic systems; DNA genetic algorithm; Expected value models; Stochastic integer programming; Stochastic simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4620554
  • Filename
    4620554