Genetic Network Programming (GNP) has been proposed as a new method of evolutionary computations and its basic characteristics are studied. Recently, GNP is also applied to Elevator Group Supervisory Control System (EGSCS) which is a benchmark for real world problems and its applicability and effectiveness are clarified. The elevator system is probabilistic and has some uncertain factors because of the different building specification, traffic volume, traffic flow and so on. Therefore, it is difficult to control the elevator system effectively due to such factors. EGSCS using GNP in the previous studies can control the elevator system using the conventional node functions. However, it does not have enough flexibility in various conditions of the elevator system because of its structure. In this paper, several new frameworks of GNP for EGSCS are proposed in order to overcome the above problem considering ranking calculations with regard to various evaluation items and node function optimization based on Real-coded GA. These frameworks can contribute to generate a flexible and effective structure of GNP for various conditions. From the simulation results, it is clarified that the proposed method can obtain better performances than conventional methods.