Grammar-Guided Evolutionary Construction of Bayesian Networks

  • José M. Font
  • Daniel Manrique
  • Eduardo Pascua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)


This paper proposes the EvoBANE system. EvoBANE automatically generates Bayesian networks for solving special-purpose problems. EvoBANE evolves a population of individuals that codify Bayesian networks until it finds near optimal individual that solves a given classification problem. EvoBANE has the flexibility to modify the constraints that condition the solution search space, self-adapting to the specifications of the problem to be solved. The system extends the GGEAS architecture. GGEAS is a general-purpose grammar-guided evolutionary automatic system, whose modular structure favors its application to the automatic construction of intelligent systems. EvoBANE has been applied to two classification benchmark datasets belonging to different application domains, and statistically compared with a genetic algorithm performing the same tasks. Results show that the proposed system performed better, as it manages different complexity constraints in order to find the simplest solution that best solves every problem.


Evolutionary computation Bayesian network grammar- guided genetic programming 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wong, M.L., Guo, Y.Y.: Learning bayesian networks from incomplete databases using a novel evolutionary algorithm. Decision Support Systems 45, 368–383 (2008)CrossRefGoogle Scholar
  2. 2.
    Darwiche, A.: Bayesian networks. Communications of the ACM 53(12), 80–90 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Niedermayer, D.: An Introduction to Bayesian Networks and Their Contemporary Applications. In: Innovations in Bayesian Networks. SCI, pp. 117–130. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Chen, X., Anantha, G., Lin, X.: Improving bayesian network structure learning with mutual information-based node ordering in the k2 algorithm. IEEE Transactions on Knowledge and Data Engineering 20(5), 1–13 (2008)CrossRefGoogle Scholar
  5. 5.
    Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R.H., Kuijpers, C.M.H.: Structure learning of bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 912–926 (1996)CrossRefGoogle Scholar
  6. 6.
    Barriére, O., Lutton, E., Wuillemin, P.: Bayesian network structure learning using cooperative coevolution. In: GECCO 2009, Montréal, Canada, July 2009, pp. 755–762 (2009)Google Scholar
  7. 7.
    Alonso-Barba, J.I., delaOssa, L., Puerta, J.M.: Structural learning of bayesian networks by using variable neighbourhood search based on the space of orderings. In: Ninth Conference on Intelligent Systems Design and Applications, pp. 1435–1440 (2009)Google Scholar
  8. 8.
    Font, J.M., Manrique, D., Ríos, J.: Evolutionary construction and adaptation of intelligent systems. Expert Systems with Applications 37, 7711–7720 (2010)CrossRefGoogle Scholar
  9. 9.
    Koza, J.: Genetic programming: On the programming of computers by means of natural selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  10. 10.
    Dong, L., Liu, G., Yuan, S., Li, Y., Li, Z.: Classifier learning algorithm based on genetic algorithms. In: ICICIC 2007 Second International Conference on Innovative Computing, Information and Control, September 2007, pp. 126–129 (2007)Google Scholar
  11. 11.
    Larrañaga, P., Kuijpers, C.M.H., Murga, R.H., Yurramendi, Y.: Learning bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans 26(4), 487–493 (1996)CrossRefGoogle Scholar
  12. 12.
    De Stefano, C., Fontanella, F., Scotto di Freca, A., Marcelli, A.: Learning bayesian networks by evolution for classifier combination. In: 10th International Conference on Document Analysis and Recognition, pp. 966–970 (2009)Google Scholar
  13. 13.
    Davidson, C.: Identifying gene regulatory networks using evolutionary algorithms. Journal of Computing Sciences in Colleges 25(5), 231–237 (2010)Google Scholar
  14. 14.
    Wong, M.L., Lee, Y.L., Leung, K.S.: A hybrid approach to learn bayesian networks using evolutionary programming. In: WCCI 2002 Proceedings of the 2002 World Congress on Computational Intelligence, vol. 2, pp. 1314–1319 (2002)Google Scholar
  15. 15.
    Lee, J., Chung, W., Kim, E.: Structure learning of bayesian networks using dual genetic algorithm. IEICE Transactions on Informations and Systems E91-D(1), 32–43 (2008)CrossRefGoogle Scholar
  16. 16.
    Palacios-Alonso, M.A., Brizuela, C.A., Sucar, L.E.: Evolutionary learning of dynamic naive bayesian classifiers. Journal on Automated Reasononing 45, 21–37 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explorations 11(1), 10–18 (2009)CrossRefGoogle Scholar
  18. 18.
    Font, J.M., Manrique, D.: Grammar-guided evolutionary automatic system for autonomously building biological oscillators. IEEE Congress on Evolutionary Computation, 1–7 (July 2010)Google Scholar
  19. 19.
    Couchet, J., Manrique, D., Ríos, J., Rodríguez-Patón, A.: Crossover and mutation operators for grammar-guided genetic programming. Soft Computing: A Fusion of Foundations, Methodologies and Applications 11(10), 943–955 (2007)CrossRefGoogle Scholar
  20. 20.
    Frank, A., Asuncion, A.: UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences (2010),

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José M. Font
    • 1
  • Daniel Manrique
    • 1
  • Eduardo Pascua
    • 1
  1. 1.Departamento de Inteligencia ArtificialUniversidad Politécnica de Madrid. Campus de MontegancedoBoadilla del MonteSpain

Personalised recommendations