• DocumentCode
    2037465
  • Title

    Evolving Classification Rules by Unconstrained Gene Expression Programming

  • Author

    Zhang, Jianwei ; Wu, Zhijian ; Guo, Jinglei ; Peng, Min ; Zhang, Yingjiang ; Wang, Chunzhi

  • Author_Institution
    State Key Lab. of Software Eng., Wuhan Univ., Wuhan
  • fYear
    2009
  • fDate
    23-24 May 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Unconstrained Gene Expression Programming (UGEP), a new unconstrained linear encoded Gene Expression Programming (GEP), is introduced and applied to solve classification problems in this paper. Different from GEP, both amount and length of the genes are dynamically adjusted in the UGEP chromosome during the evolution process. Experiment results indicate that UGEP perform better than GEP in classification problems.
  • Keywords
    data mining; classification rules; data mining; unconstrained gene expression programming; Biological cells; Classification tree analysis; Data mining; Decision trees; Evolutionary computation; Gene expression; Genetic programming; Laboratories; Linear programming; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-3893-8
  • Electronic_ISBN
    978-1-4244-3894-5
  • Type

    conf

  • DOI
    10.1109/IWISA.2009.5072858
  • Filename
    5072858
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2037465