Genetic network programming with route nodes

Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have accomplished significant contribution to the study of evolutionary computation. And in the past decade, a new approach named Genetic Network Programming (GNP) has been proposed. It is designed for especially solving complex problems in dynamic environments. Generally speaking, GNP is based on the algorithms of existed classical evolutionary computation techniques and uses the data structure of directed graphs which becomes the unique feature of GNP. So far, many studies have indicated that GNP can solve the complex problems in the dynamic environments very efficiently and effectively. Focusing on GNP's distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for the standard GNP in order to improve the performance of GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated during evolution. And among the accumulated information, some of them are selected and encapsulated in the Route Nodes which are used to guide the evolution process. In this paper, the proposed architecture has been applied to the tile-world which is an excellent bench mark for evaluating the evolutionary computation architecture. The performance of the GNP with Route Nodes (GNP-RN) is compared with the conventional GNP. The simulation results show some merits of the proposed method over the conventional GNPs demonstrating its superiority.