Genetic Network Programming (GNP) is an evolutionary algorithm derived from GA and GP. Directed graph structure, reusability of nodes, and implicit memory function enable GNP to deal with complex problems in dynamic environments efficiently and effectively, as many paper demonstrated. This paper proposed a new method to optimize GNP by extracting and using rules. The basic idea of GNP with Rule Accumulation (GNP with RA) is to extract rules with higher fitness values from the elite individuals and store them in the pool every generation. A rule is defined as a sequence of successive judgment results and a processing node, which represent the good experiences of the past behavior. As a result, the rule pool serves as an experience set of GNP obtained in the evolutionary process. By extracting the rules during the evolutionary period and then matching them with the situations of the environment, we could, for example, guide agents' behavior properly and get better performance of the agents. In this paper, we apply GNP with RA to the problem of determining agents' behaviors in the Tile-world environment in order to evaluate its effectiveness. The simulation results demonstrate that GNP with RA could have better performances than the conventional GNP both in the average fitness value and its stability.

In this paper, we propose a heuristic method -- Boltzmann Optimal Route Method trying to find a good approximation to the global optimum route for Origin-Destination pairs through iterations until the total traveling time converges. The overall idea of our method is to update the traveling time of each route section iteratively according to its corresponding traffic volume, and continuously generate a new global route by Q value-based Dynamic Programming combined with Boltzmann distribution. Finally, we can get the global optimum route considering the traffic volumes of the road sections. The new proposed method is compared with the conventional shortest-path method- Greedy strategy both in the static traffic system where the volumes of all the given Origin-Destination pairs of road networks are constant and in the dynamic traffic system in which changing traffic volumes are constantly provided. The results demonstrate that the proposed method performs better than the conventional method in global perspective.

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Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with 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 systems, etc and GNP has obtained some outstanding results. In order to improve GNP's performance further, this paper proposes a new method called GNP with Rules. The aim of the proposed method is to balance exploitation and exploration of GNP, 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. These 4 steps are added to the conventional algorithm of GNP. In order to measure the performance of the proposed method, the tileworld was used as the simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.

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