• DocumentCode
    3145342
  • Title

    Evolving Bidding Strategies Using Self-Adaptation Genetic Algorithm

  • Author

    Soon, Gan Kim ; Anthony, Patricia ; Teo, Jason ; On, Chin Kim

  • Author_Institution
    Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah, Kota Kinabalu, Malaysia
  • fYear
    2009
  • fDate
    15-16 May 2009
  • Firstpage
    222
  • Lastpage
    225
  • Abstract
    This paper investigates the application of self-adaptation genetic algorithm on a flexible and configurable heuristic decision making framework that can tackle the problem of bidding across multiple auctions that apply different protocols (English, Vickrey and Dutch) by using an autonomous agent to search for the most effective strategies (offline). Our study shows that self-adaptation genetic algorithm performance is much better than conventional genetic algorithm. An empirical evaluation on the effectiveness of genetic algorithm and self-adaptation genetic algorithm for searching the most effective strategies in the heuristic decision making framework are discussed in this paper.
  • Keywords
    decision making; electronic commerce; genetic algorithms; mobile agents; autonomous agent; bidding strategies; configurable heuristic decision making; flexible heuristic decision making; online auction; self-adaptation genetic algorithm; Autonomous agents; Decision making; Evolutionary computation; Genetic algorithms; Genetic engineering; Genetic mutations; Information technology; Intelligent agent; Monitoring; Protocols; Bidding Agent; Bidding Strategies; Genetic Algorithm; Online Auction; Self-Adaptation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3619-4
  • Type

    conf

  • DOI
    10.1109/IUCE.2009.108
  • Filename
    5223188