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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)


Elevator Group Supervisory Control System (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. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and an improvement of the EGSCS' performances is expected since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods. Furthermore, as a further study, an importance weight optimization algorithm is employed based on GNP with RL and its efficiency is also verified with the better performances.

Original languageEnglish
Pages (from-to)1234-1242+15
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number8
Publication statusPublished - 2007 Jan 1



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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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