Comparing the performance of deterministic dynamic adaptation GA and self adaptive GA in online auctions environment

The proliferation of online auctions has caused the increasing need to monitor and track multiple bids in multiple auctions. As a solution to the problem, 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). Due to the dynamic and unpredictable nature of online auctions, the agent utilizes genetic algorithm to search for effective solution. Instead of using the conventional genetic algorithm, this paper investigates the application of deterministic dynamic adaptation genetic algorithm and self adaptive 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.