Abstract
This paper introduces the Gaussian polytree estimation of distribution algorithm, a new construction method, and its application to estimation of distribution algorithms in continuous variables. The variables are assumed to be Gaussian. The construction of the tree and the edges orientation algorithm are based on information theoretic concepts such as mutual information and conditional mutual information. The proposed Gaussian polytree estimation of distribution algorithm is applied to a set of benchmark functions. The experimental results show that the approach is robust, comparisons are provided.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Acid, S., de Campos, L.M.: Approximations of Causal Networks by Polytrees: An Empirical Study. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds.) IPMU 1994. LNCS, vol. 945, pp. 149–158. Springer, Heidelberg (1995)
Bosman, P.A.N., Grahl, J., Thierens, D.: Enhancing the performance of maximum-likelihood gaussian edas using anticipated mean shift. In: Proceedings of BNAIC 2008, the Twentieth Belgian-Dutch Artificial Intelligence Conference, pp. 285–286. BNVKI (2008)
Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory IT-14(3), 462–467 (1968)
Darwiche, A.: Modeling and Reasoning with Bayesian Networks. Cambridge University Press (2009)
Dasgupta, S.: Learning polytrees. In: Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI 1999), pp. 134–141. Morgan Kaufmann, San Francisco (1999)
Edwards, D.: Introduction to Graphical Modelling. Springer, Berlin (1995)
Grahl, P.A.B.J., Rothlauf, F.: The correlation-triggered adaptive variance scaling idea. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 397–404. ACM (2006)
Lauritzen, S.L.: Graphical models. Clarendon Press (1996)
Ouerd, B.J.O.M., Matwin, S.: A formal approach to using data distributions for building causal polytree structures. Information Sciences, an International Journal 168, 111–132 (2004)
Neapolitan, R.E.: Learning Bayesian Networks. Prentice Hall series in Artificial Intelligence (2004)
Ortiz, M.S.: Un estudio sobre los Algoritmos Evolutivos con Estimacion de Distribuciones basados en poliarboles y su costo de evaluacion. PhD thesis, Instituto de Cibernetica, Matematica y Fisica, La Habana, Cuba (2003)
Ouerd, M.: Learning in Belief Networks and its Application to Distributed Databases. PhD thesis, University of Ottawa, Ottawa, Ontario, Canada (2000)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
de Campos, L.M., Moteos, J., Molina, R.: Using bayesian algorithms for learning causal networks in classification problems. In: Proceedings of the Fourth International Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), pp. 395–398 (1993)
Rebane, G., Pearl, J.: The recovery of causal poly-trees from statistical data. In: Proceedings, 3rd Workshop on Uncertainty in AI, Seattle, WA, pp. 222–228 (1987)
Segovia-Dominguez Ignacio, H.-A.A., Enrique, V.-D.: The gaussian polytree eda for global optimization. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2011, pp. 69–70. ACM, New York (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Segovia Domínguez, I., Hernández Aguirre, A., Villa Diharce, E. (2011). Global Optimization with the Gaussian Polytree EDA. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-25330-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25329-4
Online ISBN: 978-3-642-25330-0
eBook Packages: Computer ScienceComputer Science (R0)