Genetic Network Programming with General Individual Reconstruction

Genetic network programming (GNP) which has been developed for dealing with problems in dynamic environments is a newly proposed evolutionary approach with the data structure of directed graphs. GNP has been used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc and has obtained some outstanding results. Focusing on GNP´s distinguishing expression ability of the graph structure, this paper proposes a method named genetic network programming with general individual reconstruction (GNP with GIR) which reconstructs the gene of randomly selected individuals and then undergoes the special genetic operations by using the transition information of better individuals. The unique individual reconstruction and genetic operations make individuals not only learn the experiences of better individuals but also strengthen exploration and exploration ability. GNP with GIR will be applied to the tile-world which is an excellent benchmark for evaluating the proposed architecture. The performances of GNP with GIR will be compared with conventional GNP demonstrating its superiority.