• DocumentCode
    507963
  • Title

    A New Virtual Population Based Incremental Learning Approach for Optimizations Using Selfish Gene Theory

  • Author

    Wang, Feng ; Li, Yuanxiang

  • Author_Institution
    State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China
  • Volume
    5
  • fYear
    2009
  • fDate
    14-16 Aug. 2009
  • Firstpage
    342
  • Lastpage
    346
  • Abstract
    In this paper, we proposed a new approach which employed the selfish gene theory to construct virtual population for optimizations. And an incremental learning scheme which based on mutual information entropy was also used to speed up the convergence velocity. Experimental results on several benchmark problems show that, this new approach often performs better than BMDA, COMIT and MIMIC.
  • Keywords
    entropy; evolutionary computation; learning (artificial intelligence); optimisation; incremental learning scheme; mutual information entropy; optimization; selfish gene theory; virtual population based incremental learning approach; Clustering algorithms; Convergence; Electronic design automation and methodology; Entropy; Genetic mutations; Laboratories; Mutual information; Probability distribution; Sampling methods; Software engineering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2009. ICNC '09. Fifth International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-0-7695-3736-8
  • Type

    conf

  • DOI
    10.1109/ICNC.2009.577
  • Filename
    5364250
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=507963