Elevator Group Supervisory Control System Using Genetic Network Programming with Reinforcement Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{zhou:2005:CEC,

• author = "Jin Zhou and Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and Sandor Markon",
• title = "Elevator Group Supervisory Control System Using Genetic Network Programming with Reinforcement Learning",
• booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary Computation",
• year = "2005",
• editor = "David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer",
• volume = "1",
• pages = "336--342",
• address = "Edinburgh, UK",
• publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
• month = "2-5 " # sep,
• organisation = "IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)",
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7803-9363-5",
• DOI = "doi:10.1109/CEC.2005.1554703",
• 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 optimisation 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 tileworld models. As a basic study, Reinforcement Learning is introduced in this paper expecting to enhance EGSCS controller using GNP.",
• notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.",

}

Genetic Programming entries for Jin Zhou Toru Eguchi Kotaro Hirasawa Jinglu Hu Sandor Markon

Citations