Genetic Network Programming with Rule Accumulation Considering Judgment Order

Genetic Network Programming (GNP) is an evolutionary algorithm derived form GA and GP. It can deal with complex problems in dynamic environments efficiently and effectively because of its directed graph structure, reusability of nodes, and implicit memory function. This paper proposed a new method to optimize GNP algorithm by strengthening its exploitation ability through extracting and using rules. In the former research, the order of judgment node chain is ignored. The basic idea of GNP with Rule Accumulation Considering Judgment Order (GNP with RA) is to extract rules with order having high fitness values from each individual and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represents the good experiences of the past behaviors. As a result, the rule pool serves as an experience set of GNP obtained in the evolution process. By extracting the rules during the evolution period and then matching them with the situations of the environment, we could guide agents´ behavior properly and get better performance of the agents. In this paper, GNP with RA is applied to the problem of determining agents´ behaviors and Tile-world was used as the simulation environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP method both in the average fitness value and stability.