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

Jin Zhou, Toru Eguchi, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages336-342
Number of pages7
Publication statusPublished - 2005 Oct 31
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2005 Sep 22005 Sep 5

Publication series

Name2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume1

Conference

Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period05/9/205/9/5

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Zhou, J., Eguchi, T., Hirasawa, K., Hu, J., & Markon, S. (2005). Elevator group supervisory control system using genetic network programming with reinforcement learning. In 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings (pp. 336-342). (2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; Vol. 1).