A bidding strategy for Continuous Double Auctions based on Genetic Network Programming with generalized judgments

On-line auctions are the electronic marketplaces for trading commodities or services in modern life. Continuous Double Auction (CDA), which is different from the single-side auction types, allows multiple sellers and multiple buyers to update their asks and bids at the same time successively through the trading period. This paper discusses a new proposed CDA bidding strategy based on Genetic Network Programming (GNP) with generalized judgments using Gaussian functions for the agent-based CDA environment. GNP is one of the evolutionary computations, and the GNP-based bidding strategy can judge and analyze various situations of the ongoing auction using its judgment functions, then determine the most competitive and suitable ask or bid price at each time step according to the directed graph structure of the GNP individual. Especially, in the proposed GNP-based method, the generalized judgment functions are proposed using m-dimensional Gaussian Functions with evolution instead of conventional fixed judgment functions in order to judge the dynamically changing auction situations more sensitively and comprehensively. In addition, the proposed method uses some basic heuristic control rules for helping the auction agent to make ask or bid decisions. The performance of the proposed bidding strategy are studied and compared with other methods in CDA under different settings.