Soft Computing

, Volume 22, Issue 9, pp 3049–3060 | Cite as

A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm

  • Ali Reza Khanteymoori
  • Mohammad-H. Olyaee
  • Omid Abbaszadeh
  • Maryam Valian
Methodologies and Application
  • 138 Downloads

Abstract

Bayesian networks (BNs) are widely used as one of the most effective models in bioinformatics, artificial intelligence, text analysis, medical diagnosis, etc. Learning the structure of BNs from data can be viewed as an optimization problem and is proved that this problem is NP-hard. Therefore, heuristic methods can be used as powerful tools to find high-quality networks. In this paper, an interesting approach which is based on Breeding Swarm has been used to learn BNs. Breeding Swarm is a hybrid GA/PSO which enable us to benefits the strengths of particle swarm optimization with genetic algorithms. In order to assess the proposed method, several real-world and benchmark applications are used. Results show that our method is a clear improvement on genetic algorithm and particle swarm optimization.

Keywords

Graphical models Bayesian networks Structure learning Bio-inspired algorithm Particle Swarm optimization Breeding Swarm 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Ali Reza Khanteymoori
    • 1
  • Mohammad-H. Olyaee
    • 1
  • Omid Abbaszadeh
    • 1
  • Maryam Valian
    • 2
  1. 1.Department of Computer EngineeringUniversity of ZanjanZanjanIran
  2. 2.Islamic Azad University, Karaj BranchKarajIran

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