Structural learning of Bayesian networks by bacterial foraging optimization

Cuicui Yang, Junzhong Ji*, Jiming LIU, Jinduo Liu, Baocai Yin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Algorithms inspired by swarm intelligence have been used for many optimization problems and their effectiveness has been proven in many fields. We propose a new swarm intelligence algorithm for structural learning of Bayesian networks, BFO-B, based on bacterial foraging optimization. In the BFO-B algorithm, each bacterium corresponds to a candidate solution that represents a Bayesian network structure, and the algorithm operates under three principal mechanisms: chemotaxis, reproduction, and elimination and dispersal. The chemotaxis mechanism uses four operators to randomly and greedily optimize each solution in a bacterial population, then the reproduction mechanism simulates survival of the fittest to exploit superior solutions and speed convergence of the optimization. Finally, an elimination and dispersal mechanism controls the exploration processes and jumps out of a local optima with a certain probability. We tested the individual contributions of four algorithm operators and compared with two state of the art swarm intelligence based algorithms and seven other well-known algorithms on many benchmark networks. The experimental results verify that the proposed BFO-B algorithm is a viable alternative to learn the structures of Bayesian networks, and is also highly competitive compared to state of the art algorithms.

Original languageEnglish
Pages (from-to)147-167
Number of pages21
JournalInternational Journal of Approximate Reasoning
Volume69
DOIs
Publication statusPublished - 1 Feb 2016

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Bacterial foraging optimization
  • Bayesian networks
  • Structural learning
  • Swarm intelligence

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