Elevator group supervisory control system using genetic network programming with reinforcement learning

Since genetic network programming (GNP) has been proposed as a new method of evolutionary computation, many studies have been done on its applications which cover not only virtual world problems but also real world systems like elevator group supervisory control system (EGSCS) which is a very large scale stochastic dynamic optimization problem. From those researches, most of the significant features of GNP have been verified comparing to genetic algorithm (GA) and genetic programming (GP). Especially, the improvement of the performances on EGSCS using GNP showed an interesting and promising prospect in this field. On the other hand, some studies based on GNP with reinforcement learning (RL) revealed a better performance over conventional GNP on some problems such as tile-world models. As a basic study, reinforcement learning is introduced in this paper expecting to enhance EGSCS controller using GNP