Abstract
Genetic Network Programming (GNP) is a graph based evolutionary algorithm. The efficiency of GNP, especially in dynamic environments, has been proved in many applications due to its directed graph structure. In this paper, GNP is applied to extract rules in stock trading markets. The method is an extension of GNP with Rule Accumulation (GNP RA). There are two main points: rule extraction and action determination. Rule extraction which is carried out in the training period is to extract the 1st order rules and 2nd order rules. Rules obtained from the individual with the highest profit are saved in different rule pools according to its action and order information. So far, in GNP RA, only the 1st order rules were extracted. Then, in this paper, actions are determined by the matching degree checking which order rules, the 1st order rules or 2nd order rules, are more useful. Simulation results show that the 2nd order rules perform better than the 1st order rules.
