Genetic Network Programming with Rule Chains

Genetic network programming (GNP) is a newly developed evolutionary approach which can evolve itself and find the optimal solutions. A lot of research has been done and it has been 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 data mining, elevator supervising control systems, the strategy of buying and selling stocks in stock markets, forecasting the traffic volumes in road networks, etc. In order to improve GNPpsilas performance further, this paper proposes a method called GNP with Rule Chains. The aim of the proposed 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 proposed method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tileworld was used as a simulation environment. The simulation results show some advantages of GNP with rule chains over conventional GNPs.