Learning Bayesian networks structures based on extending Evolutionary programming

Proceedings of 2004 International Conference on Machine Learning and Cybernetics

Volumn 3, Issue , 2004, Pages 1594-1598

  Li, Xiao Lin ^a   Yuan, Sen Miao ^a   He, Xiang Dong ^a  

^a JILIN UNIVERSITY   (China)

Author keywords

Bayesian networks; Evolutionary programming; Genetic algorithms; Minimum description length principle; Niche

Indexed keywords

COMBINATORIAL MATHEMATICS; COMPUTATIONAL METHODS; CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; LEARNING SYSTEMS; OPTIMIZATION;

BAYESIAN NETWORKS; MINIMUM DESCRIPTION LENGTH (MDL) PRINCIPLE; NICHE; VARIABLE ORDERING;

EVOLUTIONARY ALGORITHMS;

EID: 6344267195     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. J Suzuki. "A construction of Bayesian networks from databases based on a MDL scheme", Proc of the 9th Confon Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann, pp. 266-273, 1993.
2. Y Xiang, S K M Wong. "Learning conditional independence relations from a probabilistic model", Department of Computer Science, University of Regina, CA, Tech Rep: CS-94-03, 1994.
3. D Heckerman. "Learning Bayesian network: The combination of knowledge and statistica data", Machine Learning, Vol. 20, No. 2, pp. 197-243, 1995.
4. Cheng J, Greiner R, Kelly J. "Learning Bayesian networks from data: An efficient algorithm based on information theory", Artificial Intelligence. Vol.137, No.1-2, pp. 43-90, 2002.
5. Lam, W. and Bacchus, F., "Learning Bayesian belief networks: An algorithm based on the MDL principle", Computational Intelligence, Vol 10, No.4, 1994.
6. P. Larrañaga, M. Poza, Y. Yurramendi, R. Murga, and C. Kuijpers, "Structure Learning of Bayesian Network by Genetic Algorithms: A Performance Analysis of Control Parameters", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 912-926, Sept. 1996.
7. Liu Da-You, Wang Fei et al. "Research on learning Bayesian network structure based on genetic algorithm", Journal of Computer Research and Development, Vol. 38, No.8, pp: 916-922, 2001.
8. D.B. Fogel, "An Introduction to Simulated Evolutionary Optimization", IEEE Trans. Neural Network, vol. 5, pp. 3-14, 1994.
9. L. Fogel, A. Owens, and M. Walsh, Artificial Intelligence Through Simulated Evolution. New York: John Wiley and Sons, 1966.
10. Mahfound S.W., "Crowding and Preselection Revisited", Parallel Problem Solving from Nature - II, pp. 27-36, 1992.
11. W. Lam and F. Bacchus. "Learning Bayesian belief networks: an algorithm based on the MDL principle", Computational Intelligence, Vol.10, No.4, pp: 269-293, 1994.