Evolving bidding strategies using self-adaptation genetic algorithm

Kim, Soon Gan and Patricia Anthony and Teo, Jason Tze Wi and Kim, On Chin (2009) Evolving bidding strategies using self-adaptation genetic algorithm. In: 2009 International Symposium on Intelligent Ubiquitous Computing and Education (IUCE), 16-17 May 2009, Chengdu, China.

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This paper investigates the application of selfadaptation 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. © 2009 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Bidding agent, Bidding strategies, Component, Genetic algorithm, Online auction, Self-adaptation
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: ADMIN ADMIN
Date Deposited: 24 Mar 2011 17:02
Last Modified: 30 Dec 2014 14:30
URI: http://eprints.ums.edu.my/id/eprint/2557

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