%0 Conference Paper %1 zhou:2005:CEC %A Zhou, Jin %A Eguchi, Toru %A Hirasawa, Kotaro %A Hu, Jinglu %A Markon, Sandor %B Proceedings of the 2005 IEEE Congress on Evolutionary Computation %C Edinburgh, UK %D 2005 %E Corne, David %E Michalewicz, Zbigniew %E Dorigo, Marco %E Eiben, Gusz %E Fogel, David %E Fonseca, Carlos %E Greenwood, Garrison %E Chen, Tan Kay %E Raidl, Guenther %E Zalzala, Ali %E Lucas, Simon %E Paechter, Ben %E Willies, Jennifier %E Guervos, Juan J. Merelo %E Eberbach, Eugene %E McKay, Bob %E Channon, Alastair %E Tiwari, Ashutosh %E Volkert, L. Gwenn %E Ashlock, Dan %E Schoenauer, Marc %I IEEE Press %K algorithms, genetic programming %P 336--342 %T Elevator Group Supervisory Control System Using Genetic Network Programming with Reinforcement Learning %V 1 %X 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. %@ 0-7803-9363-5

@inproceedings{zhou:2005:CEC, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {Edinburgh, UK}, author = {Zhou, Jin and Eguchi, Toru and Hirasawa, Kotaro and Hu, Jinglu and Markon, Sandor}, biburl = {https://www.bibsonomy.org/bibtex/2126f0c5bd612ed370bd8a6f0bde7f701/brazovayeye}, booktitle = {Proceedings of the 2005 IEEE Congress on Evolutionary Computation}, editor = {Corne, David and Michalewicz, Zbigniew and Dorigo, Marco and Eiben, Gusz and Fogel, David and Fonseca, Carlos and Greenwood, Garrison and Chen, Tan Kay and Raidl, Guenther and Zalzala, Ali and Lucas, Simon and Paechter, Ben and Willies, Jennifier and Guervos, Juan J. Merelo and Eberbach, Eugene and McKay, Bob and Channon, Alastair and Tiwari, Ashutosh and Volkert, L. Gwenn and Ashlock, Dan and Schoenauer, Marc}, interhash = {4e696ed05fcc80198a88e232aafc5703}, intrahash = {126f0c5bd612ed370bd8a6f0bde7f701}, isbn = {0-7803-9363-5}, keywords = {algorithms, genetic programming}, month = {2-5 September}, notes = {CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.}, organisation = {IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)}, pages = {336--342}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, timestamp = {2008-06-19T17:55:52.000+0200}, title = {Elevator Group Supervisory Control System Using Genetic Network Programming with Reinforcement Learning}, volume = 1, year = 2005 }