Elevator group supervisory control systems using genetic network programming

Genetic Network Programming (GNP) has been proposed as a new method of evolutionary computation. Until now, GNP has been applied to various problems and its effectiveness was clarified. However, these problems were virtual models, so the applicability and availability of GNP to the real-world applications have not been studied. In this paper, as a first step of applying GNP to the real-world applications, Elevator Group Supervisory Control Systems (EGSCSs) are considered. Generally, EGSCSs are complex and difficult problems to solve because they are too dynamic and probabilistic. So the design of a useful controller of EGSCSs was very difficult. Recently, the design of such a controller of EGSCSs has been tried actively using Artificial Intelligence (AI) technologies. In this paper, it is reported that the design of a controller of EGSCSs has been studied using GNP whose characteristic is to use directed graph as its gene instead of bit strings and trees of GA and GP. From simulations, it is clarified that better solutions are obtained by using GNP than other conventional methods and the availability of GNP to real-world applications is confirmed.