Abstract
This paper demonstrates how genetic algorithms can be used to discover the structure of a Bayesian network from a given database with cases. The results presented, were obtained by applying four different types of genetic algorithms — SSGA (Steady State Genetic Algorithm), GAeλ (Genetic Algorithm elistist of degree λ), hSSGA (hybrid Steady State Genetic Algorithm) and the hGAeλ (hybrid Genetic Algorithm elitist of degree λ) — to simulations of the ALARM Network. The behaviour of these algorithms is studied as their parameters are varied.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Beinlich, I.A., Suermondt, H.J., Chavez, R.M., & Cooper, G.F. (1989). The ALARM monitoring system: A case study with two probabilistic inferences techniques for belief networks. Proceedings of the Second European Conference on Artificial Intelligence in Medicine (pp. 247–256 ).
Bouckaert, R.R. (1993). Probabilistic network construction using the minimum description length principle. In M. Clarke, R. Kruse & S. Moral (Eds.) Symbolic and Quantitative Approaches to Reasoning and Uncertainty — ECSQARU-93, No. 747, Lectures Notes in Computing Science, pp. 41–48, Springer-Verlag.
Bouckaert, R.R. (1994). Properties of Bayesian belief network learning algorithms. Uncertainty in Artificial Intelligence, Tenth Annual Conference (pp. 102–109 ). San Francisco, CA: Morgan Kaufmann.
Chakraborty, U.K., & Dastidar, D.G. (1993). Using reliability analysis to estimate the number of generations to convergence in genetic algorithms. Information Processing Letters, 46, 199–209.
Chickering, D.M., Geiger, D., & Heckerman, D. (1995). Learning Bayesian networks: Search methods and experimental results. Preliminary Papers of the Fifth International Workshop on Artificial Intelligence and Statistics (pp. 112–128 ).
Chow, C.K., & Liu, C.N. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14, 462–467.
Cooper, G.F., & Herskovits, E.H. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309–347.
Davis, L. (Ed.) (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.
Eiben, A.E., Aarts, E.H.L., & Van Hee, K.M. (1990). Global convergence of genetic algorithms: An infinite Markov chain analysis. Computing Science Notes, Eindhoven University of Technology, The Netherlands.
Fogel, L.J. (1962). Atonomous automata. Ind. Res, 4, 14–19.
Fung, R.M., & Crawford, S.L. (1990). CONSTRUCTOR: A system for the induction of probabilistic models. Proceedings of AAAI (pp. 762–769).
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley.
Henrion, M. (1988). Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In J. Lemmer & L. Kanal (Eds.) Uncertainty in Artificial Intelligence, 2, pp. 149–163, North-Holland.
Herskovits, E. H. (1991). Computer based probabilistic-network construction. Doctoral dissertation, Medical Information Sciences, Stanford University.
Herskovits, E. H., & Cooper, G.F. (1990). Kutatô: An entropy-driven system for construction of probabilistic expert systems from databases Report KSL-9022, Knowledge Systems Laboratory, Medical Computer Science, Stanford University.
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. The University of Michigan Press.
Lauritzen, S.L., Thiesson, B., & Spiegelhalter, D.J. (1993). Diagnostic systems created by model selection methods-A case study. Fourth International Workshop on Artificial Intelligence and Statistics (pp. 93–105 ).
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan Kaufmann.
Rebane, G., & Pearl, J. (1989). The recovery of causal polytrees from statistical data. In L. Kanal, T. Levitt & J. Lemmer (Eds.) Uncertainty in Artificial Intelligence, 3, pp. 175–182, North-Holland.
Rudolph, G. (1994). Convergence analysis of canonical genetic algoritms. IEEE Transactions on Neural Networks, vol. 5, no. 1, 96–101.
Schwefel, H.-P. (1967). Numerische Optimierung von Computer-Modellen mittels der Evolutionsstrategie. Basel: Birkhäuser.
Wedelin, D. (1993). Efficient algorithms for probabilistic inference combinatorial optimization and the discovery of causal structure from data. Doctoral dissertation, Chalmers University of Technology, Göteborg.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag New York, Inc.
About this chapter
Cite this chapter
Larrañaga, P., Murga, R., Poza, M., Kuijpers, C. (1996). Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_16
Download citation
DOI: https://doi.org/10.1007/978-1-4612-2404-4_16
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
eBook Packages: Springer Book Archive