Genetic Network Programming with rules

Abstract

Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. As many papers have demonstrated that GNP which has a directed graph structure can deal with dynamic environments very efficiently and effectively. It can be used in many areas such as data mining, forecasting stock markets, elevator system problems, etc. In order to improve GNP's performance further, this paper proposes a method called GNP with Rules. The aim of the proposal method is to balance exploitation and exploration, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposal method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tile-world was used as a simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.