• DocumentCode
    618085
  • Title

    Adaptive selection of helper-objectives for test case generation

  • Author

    Buzdalov, Maxim ; Buzdalova, Arina

  • Author_Institution
    St. Petersburg Nat. Res. Univ. of Inf. Technol., Mech. & Opt., St. Petersburg, Russia
  • fYear
    2013
  • fDate
    20-23 June 2013
  • Firstpage
    2245
  • Lastpage
    2250
  • Abstract
    In this paper a method of adaptive selection of helper-objectives in evolutionary algorithms, which was previously applied to model problems only, is applied to generation of test cases for programming challenge tasks. The method is based on reinforcement learning. Experiments show that the proposed method performs equally well compared to the best helper-objectives selected by hand.
  • Keywords
    evolutionary computation; learning (artificial intelligence); adaptive helper-objectives selection; evolutionary algorithms; reinforcement learning; test case generation; Evolutionary computation; Genetic algorithms; Learning (artificial intelligence); Optimization; Programming; Sociology; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2013 IEEE Congress on
  • Conference_Location
    Cancun
  • Print_ISBN
    978-1-4799-0453-2
  • Electronic_ISBN
    978-1-4799-0452-5
  • Type

    conf

  • DOI
    10.1109/CEC.2013.6557836
  • Filename
    6557836
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=618085