Paper Info
Title
A bidding strategy using genetic network programming with adjusting parameters for large-scale continuous double auction
Abstract
Along with the explosive development of electronic commerce, trading goods online becomes much more popular and the trading volume over internet has been increased hugely. Concentrating particularly on continuous double auction (CDA), which is an efficient market mechanism, this paper studied and discussed a Genetic Network programming (GNP) based bidding strategy with adjusting parameters for autonomous software agents in agent-based large-scale CDAs (GNP-AP). GNP is one of the evolutionary computations, and the individuals with directed graph structures represents the potential bidding strategies. Combined with the heuristic control rules, each individual can collect and judge the auction information, then choose the decision-making transition depending on the judgment results. The parameters of CDAs to select the right decision are adjusted during the evolution in order to get more profits for large-scale CDAs. In the experiments, we studied and discussed the performance of the proposed bidding strategies and compared it with other classic bidding strategies and the previous strategy developed by GNP with rectifying node (GNP-RN) in the large-scale CDA under different settings.
Year
DOI
Venue
2012
10.1109/ICSMC.2012.6378015
SMC
Keywords
Field
DocType
genetic network programming,decision-making transition,parameter adjustment,continuous double auction,large-scale cda,decision making,autonomous software agents,evolutionary computations,gnp-ap,internet,large-scale continuous double auction,bidding strategy,genetic algorithms,agent-based large-scale cda,directed graphs,online good trading,electronic commerce,heuristic control rules,software agents,market mechanism,directed graph structures,genetics,statistics,sociology,history,economic indicators,programming
Heuristic,Computer science,Directed graph,Software agent,Real-time bidding,Artificial intelligence,Ebidding,Bidding,Machine learning,Genetic algorithm,The Internet
Conference
ISSN
ISBN
Citations 
1062-922X
978-1-4673-1712-2
2
PageRank 
References 
Authors
0.41
6
3
Name
Order
Citations
PageRank
Chuan Yue122524.00
Shingo Mabu249377.00
Kotaro Hirasawa3704113.11