NDLI: Genetic Network Programming with Estimation of Distribution Algorithms and its application to association rule mining for traffic prediction

Content Provider	IEEE Xplore Digital Library
Author	Xianneng Li Mabu, S. Huiyu Zhou Shimada, K. Hirasawa, K.
Copyright Year	2009
Description	Author affiliation: Graduate School of Information, Production and Systems, Waseda University, Japan (Xianneng Li; Mabu, S.; Huiyu Zhou; Shimada, K.; Hirasawa, K.)
Abstract	In this paper, a novel evolutionary paradigm combining Genetic Network Programming (GNP) and Estimation of Distribution Algorithms (EDAs) is proposed and used to find important association rules in time-related applications, especially in traffic prediction. GNP is one of the evolutionary optimization algorithms, which uses directed-graph structures. EDAs is a novel algorithm, where the new population of individuals is produced from a probabilistic distribution estimated from the selected individuals from the previous generation. This model replaces random crossover and mutation to generate offspring. Instead of generating the candidate association rules using conventional GNP, the proposed method can obtain a large number of important association rules more effectively. The purpose of this paper is to compare the proposed method with conventional GNP in traffic prediction systems in terms of the number of rules obtained.
Starting Page	3457
Ending Page	3462
File Size	227612
Page Count	6
File Format	PDF
ISBN	9784907764340
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-18
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Association rules Data mining Telecommunication traffic Economic indicators Electronic design automation and methodology Traffic control Databases Genetic mutations Genetic programming Evolutionary computation time-related association rule mining Genetic Network Programming (GNP) Estimation of Distribution Algorithms (EDAs)
Content Type	Text
Resource Type	Article

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