Genetic network programming with estimation of distribution algorithms for class association rule mining in traffic prediction

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)497-509
Number of pages13
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume14
Issue number5
Publication statusPublished - 2010 Jul

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Keywords

  • Class association rule mining
  • Estimation of distribution algorithms
  • Genetic network programming
  • Traffic prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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