Genetic Network Programming with new genetic operators

Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solutions. It is based on the ideas of classical evolutionary computation methods such as Genetic Algorithm (GA) and Genetic Programming (GP) and uses the data structure of directed graphs which is the unique feature of GNP. Many studies have demonstrated that GNP can well solve the complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is being used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc. 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 GNP by using the exploited information extensively during the evolution process of GNP. In the enhanced architecture, we proposed the new genetic operator named Individual Reconstruction which reconstructs and enhances the worst individuals by using the elite information and the crossover and mutation operators of GNP are also modified. 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 new GNP is compared with the conventional GNP. The simulation results show some advantages of the proposed method over the conventional GNPs demonstrating its superiority.