International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Matrix-Based Genetic Algorithm for Structure Learning of Bayesian Networks

  • Ko, Song (School of Computer Science and Engineering Chung-Ang University) ;
  • Kim, Dae-Won (School of Computer Science and Engineering Chung-Ang University) ;
  • Kang, Bo-Yeong (School of Mechanical Engineering Kyungpook National University)
  • Received : 2011.08.03
  • Accepted : 2011.09.08
  • Published : 2011.09.25
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Abstract

Unlike using the sequence-based representation for a chromosome in previous genetic algorithms for Bayesian structure learning, we proposed a matrix representation-based genetic algorithm. Since a good chromosome representation helps us to develop efficient genetic operators that maintain a functional link between parents and their offspring, we represent a chromosome as a matrix that is a general and intuitive data structure for a directed acyclic graph(DAG), Bayesian network structure. This matrix-based genetic algorithm enables us to develop genetic operators more efficient for structuring Bayesian network: a probability matrix and a transpose-based mutation operator to inherit a structure with the correct edge direction and enhance the diversity of the offspring. To show the outstanding performance of the proposed method, we analyzed the performance between two well-known genetic algorithms and the proposed method using two Bayesian network scoring measures.

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