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 th...

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Bibliographic Details
Main Authors: Soon, Gan Kim, Anthony, Patricia, Teo, Jason, On, Chin Kim
Format: Conference Proceeding
Language:English
Subjects:
Autonomous agents
Bidding Strategies
Decision making
Deterministic Dynamic Adaptation
Electronic commerce
Evolution (biology)
Gallium nitride
Genetic Algorithm
Genetic algorithms
Information security
Information technology
Monitoring
Online Auction
Protocols
Self-Adaptation
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Summary: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.
DOI:10.1109/ISECS.2009.241