Particle swarm optimisation for learning Bayesian networks

J. Cowie, L. Oteniya, R. Coles

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper discusses the potential of Particle Swarm Optimisation (PSO) for inducing Bayesian Networks (BNs). Specifically, we detail two methods which adopt the search and score approach to BN learning. The two algorithms are similar in that they both use PSO as the search algorithm, and the K2 metric to score the resulting network. The difference lies in the way networks are constructed. The CONstruct And Repair (CONAR) algorithm generates structures, validates, and repairs if required, and the REstricted STructure (REST) algorithm, only permits valid structures to be developed. Initial experiments indicate that these approaches produce promising results when compared to other BN learning strategies.
Original languageEnglish
Title of host publicationProceedings of the World Congress on Engineering 2007
EditorsS.I. Ao, Len Gelman, David WL Hukins, Andrew Hunter, A. M. Korsunsky
Number of pages6
ISBN (Electronic)9789889867157, 9789889867126
Publication statusPublished - 30 Jun 2007


  • Particle Swarm Optimisation
  • Bayesian Network Construction

ASJC Scopus subject areas

  • Engineering(all)


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