A bidding strategy using genetic network programming with adjusting parameters for large-scale continuous double auction

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.