• DocumentCode
    1675501
  • Title

    Application of Adaptive genetic algorithm in mining industry

  • Author

    Besiashvili, G. ; Khachidze, M. ; Chokhonelidze, D.

  • Author_Institution
    Tbilisi State Univ., Tbilisi, Georgia
  • fYear
    2012
  • Firstpage
    1
  • Lastpage
    3
  • Abstract
    Selection, mutation and crossover are the parameters that stipulate the evolution process. These three methods are used by the genetic algorithms. We´ve tried to apply genetic algorithms in mining industry, particularly in concentrating the manganese. It´s necessary to optimize several parameters for that. In Adaptive genetic algorithms were applied Hamming weight and Hamming distance for selection and crossover. By Hamming Distance we search in chromosomes the similarity combinations and define the crossover point.
  • Keywords
    genetic algorithms; manganese; mining industry; Hamming distance; Hamming weight; adaptive genetic algorithm; crossover point; evolution process; manganese concentration; mining industry; mutation; parameter optimization; selection; similarity combination; Hamming distance; Hamming weight; fitness function; genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Problems of Cybernetics and Informatics (PCI), 2012 IV International Conference
  • Conference_Location
    Baku
  • Print_ISBN
    978-1-4673-4500-2
  • Type

    conf

  • DOI
    10.1109/ICPCI.2012.6486320
  • Filename
    6486320