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JACIII Vol.14 No.5 pp. 497-509
doi: 10.20965/jaciii.2010.p0497
(2010)

Paper:

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Genetic Network Programming with Estimation of Distribution Algorithms for Class Association Rule Mining in Traffic Prediction

Xianneng Li, Shingo Mabu, Huiyu Zhou,
Kaoru Shimada, and Kotaro Hirasawa

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:
November 10, 2009
Accepted:
April 26, 2010
Published:
July 20, 2010
Creative Commons License
Keywords:
genetic network programming, estimation of distribution algorithms, class association rule mining, traffic prediction
Abstract
Genetic Network Programming (GNP) is one of the evolutionary optimization algorithms, which uses directed-graph structures to represent its solutions. It has been clarified that GNP works well to find class association rules in traffic prediction systems. In this paper, a novel evolutionary paradigm named GNP with Estimation of Distribution Algorithms (GNP-EDAs) is proposed and used to find important class association rules in traffic prediction systems. In GNP-EDAs, a probabilistic model replaces crossover and mutation to enhance the evolution. The new population of individuals is produced from the probabilistic distribution estimated from the selected elite individuals of the previous generation. The probabilistic information on the connections and transitions of GNP-EDAs is extracted from its population to construct the probabilistic model. In this paper, two methods are described to build the probabilistic model for producing the offspring. In addition, a classification mechanism is introduced to estimate the traffic prediction based on the extracted class association rules. We compared GNPEDAs with the conventional GNP and the simulation results showed that GNP-EDAs can extract the class association rules more effectively, when the number of the candidate class association rules increase. And the classification accuracy of the proposed method shows good results in traffic prediction systems.
Cite this article as:
X. Li, S. Mabu, H. Zhou, K. Shimada, and K. Hirasawa, “Genetic Network Programming with Estimation of Distribution Algorithms for Class Association Rule Mining in Traffic Prediction,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.5, pp. 497-509, 2010.
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