A Memetic Approach to Bayesian Network Structure Learning

  • Alberto Tonda
  • Evelyne Lutton
  • Giovanni Squillero
  • Pierre-Henri Wuillemin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)


Bayesian networks are graphical statistical models that represent inference between data. For their effectiveness and versatility, they are widely adopted to represent knowledge in different domains. Several research lines address the NP-hard problem of Bayesian network structure learning starting from data: over the years, the machine learning community delivered effective heuristics, while different Evolutionary Algorithms have been devised to tackle this complex problem. This paper presents a Memetic Algorithm for Bayesian network structure learning, that combines the exploratory power of an Evolutionary Algorithm with the speed of local search. Experimental results show that the proposed approach is able to outperform state-of-the-art heuristics on two well-studied benchmarks.


Memetic Algorithms Evolutionary Algorithms Local Optimization Bayesian Networks Model Learning 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alberto Tonda
    • 1
  • Evelyne Lutton
    • 2
  • Giovanni Squillero
    • 3
  • Pierre-Henri Wuillemin
    • 4
  1. 1.INRA UMR 782 GMPAThiverval-GrignonFrance
  2. 2.INRIA Saclay-Ile-de-France, AVIZ teamOrsay CedexFrance
  3. 3.Politecnico di Torino, DAUINTorinoItaly
  4. 4.LIP6 - Département DÉSIRParis

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