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Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization
Ali Reza Khanteymoori, Mohammad Bagher Menhaj, and Mohammad Mehdi Homayounpour
vol. 33, no. 1, Feb. 2011, pp. 39-49.
http://dx.doi.org/10.4218/etrij.11.0110.0114
Keywords : Bayesian networks, structure learning, evolutionary algorithms, genetic algorithms.
  • Abstract
    • Abstract.

      A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.
  • Authors
    • Authors

      Ali Reza Khanteymoori
      Amirkabir University of Technology
      khanteymoori@gmail.com
      Mohammad Bagher Menhaj
      Amirkabir University of Technology
      Menhaj@aut.ac.ir
      Mohammad Mehdi Homayounpour
      Amirkabir University of Technology
      homayoun@aut.ac.ir
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    • References

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