Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement

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Backward time related association rule mining in trafficprediction using genetic network programming withdatabase rearrangement

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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents

Montreal, Québec, Canada

POSTER SESSION: Track 10: genetic programming table of contents

Pages: 1897-1898

Year of Publication: 2009

ISBN:978-1-60558-325-9

Authors

Huiyu Zhou	Waseda University, IPS, Kitakyushu, Japan
Shingo Mabu	Waseda University, IPS, Kitakyushu, Japan
Kaoru Shimada	Waseda University, IPS, Kitakyushu, Japan
Kotaro Hirasawa	Waseda University, IPS, Kitakyushu, Japan

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

In this paper, we introduce Backward Time Related Association Rule Mining using Genetic Network Programming (GNP) with Database Rearrangement in order to find time related sequential association from time related databases effectively and efficiently. The proposed algorithm and experimental results are described using a traffic prediction problem.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Shingo Mabu , Kotaro Hirasawa , Jinglu Hu, A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning, Evolutionary Computation, v.15 n.3, p.369-398, Fall 2007 [doi>10.1162/evco.2007.15.3.369]
	2	T. Eguchi, K. Hirasawa, J. Hu and N. Ota, "A study of Evolutionary Multiagent Models Based on Symbiosis", IEEE Trans. on Syst., Man and Cybernetics -- Part B --, Vol.36, No.1, pp.179--193, 2006.
	3	K. Hirasawa, T. Eguchi, J. Zhou, L. Yu and S. Markon, A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming, IEEE Trans. on Systems, Man and Cybernetics, Part C, Vol. 38, No. 4, pp. 535--550, 2008/7.