Evaluating the Efficiency of Self Adaptive GA and Deterministic Dynamic Adaptation GA in Online Auctions Environment
The proliferation of online auctions has caused the increasing need to monitor and track multiple bids in multiple auctions. An autonomous agent was developed to work in 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) as a solution to the problem. This agent utilizes genetic algorithm to search for effective solution in view of the dynamics and the unpredictability nature of online auctions. This paper investigates the application of deterministic dynamic adaptation genetic algorithm and self adaptive genetic algorithm to replace the conventional genetic algorithm to search for the most effective strategies (offline). An empirical evaluation on the comparison between the effectiveness of self-adaptive genetic algorithm and deterministic dynamic adaptation genetic algorithm for searching the most effective strategies in the online auction environment are discussed in this paper.
component Online Auction Bidding Strategies Genetic Algorithm Deterministic Dynamic Adaptation Self-Adaptation
Kim Soon Gan Patricia, Anthony Jason Teo Kim On Chin
School of Engineering and Information Technology Universiti Malaysia Sabah Kota Kinabalu, Sabah, Malaysia
国际会议
Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)
南昌
英文
644-649
2009-05-22（万方平台首次上网日期，不代表论文的发表时间）