Efficiency improvement of genetic network programming by tasks decomposition in different types of environments

Genetic Network Programming (GNP) is a relatively recently proposed evolutionary algorithm which is an extension of Genetic Programming (GP). However, individuals in GNP have graph structures. This algorithm is mainly used in decision making process of agent control problems. It uses a graph to make a flowchart and use this flowchart as a decision making strategy that an agent must follow to achieve the goal. One of the most important weaknesses of this algorithm is that crossover and mutation break the structures of individuals during the evolution process. Although it can lead to better structures, this may break suitable ones and increase the time needed to achieve optimal solutions. Meanwhile, all the researches in this field are dedicated to test GNP in deterministic environments. However, most of the real-world problems are stochastic and this is another issue that should be addressed. In this research, we try to find a mechanism that GNP shows better performance in stochastic environments. In order to achieve this goal, the evolution process of GNP was modified. In the proposed method, the experience of promising individuals was saved in consecutive generations. Then, to generate offspring in some predefined number of generations, the saved experiences were used instead of crossover and mutation. The experimental results of the proposed method were compared with GNP and some of its versions in both deterministic and stochastic environments. The results demonstrate the superiority of our proposed method in both deterministic and stochastic environments.