Grammar-Guided Evolutionary Construction of Bayesian Networks
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.
KeywordsEvolutionary computation Bayesian network grammar- guided genetic programming
Unable to display preview. Download preview PDF.
- 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.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
- 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
- 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.Davidson, C.: Identifying gene regulatory networks using evolutionary algorithms. Journal of Computing Sciences in Colleges 25(5), 231–237 (2010)Google Scholar
- 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
- 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
- 20.Frank, A., Asuncion, A.: UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences (2010), http://archive.ics.uci.edu/ml