Service area-based elevator group supervisory control system using GNP with RL

Jin Zhou*, Lu Yu, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

*Corresponding author for this work

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


Genetic Network Programming (GNP) was proposed several years ago as a new evolutionary computation method. Its unique features, such as highly compact structure, potential memory function, etc, are verified by many studies mainly on virtual world problems. Recently, GNP is also applied to some complicated real world problems like Elevator Group Supervisory Control Systems (EGSCS) and stock price prediction systems. As we know, EGSCS is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. In this paper, we propose an enhanced algorithm of EGSCS using GNP with Reinforcement Learning (RL) where an importance weight tuning method and a car assignment policy based on service area are introduced.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages6
Publication statusPublished - 2006 Dec 1
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference


Conference2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of


  • Elevator group supervisory control system
  • Genetic network programming
  • Importance weight
  • Reinforcement learning
  • Service area

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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