Abstract
In this work we present a new proposal in order to model the probability distribution in the estimation of distribution algorithms. This approach is based on using dependency networks [1] instead of Bayesian networks or simpler models in which structure is limited. Dependency networks are probabilistic graphical models similar to Bayesian networks, but with a significant difference: they allow directed cycles in the graph. This difference can be an important advantage because of two main reasons. First, in some real problems cyclic relationships appear between variables an this fact cannot be represented in a Bayesian network. Secondly, dependency networks can be built easily due to the fact that there is no need to check the existence of cycles as in a Bayesian network.
In this paper we propose to use a general (multivariate) model in order to deal with a richer representation, however, in this initial approach to the problem we also propose to constraint the construction phase in order to use only bivariate statistics. The algorithm is compared with classical approaches with the same complexity order, i.e. bivariate models as chains and trees.
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References
Heckerman, D., Chickering, D.M., Meek, C.: Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research 1, 49–75 (2000)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Mühlenbein, H.: The equation for response to selection an its use for prediction. Evolutionary Computation 5, 303–346 (1998)
Bonet, J.S.D., Isbell, C.L., Viola, P.: MIMIC: Finding optima by estimating probability densities. In: Advances in Neural Information Processing Systems, vol. 9, pp. 424–430. MIT Press, Cambridge (1997)
Baluja, S., Davies, S.: Combining Multiple Optimization Runs with Optimal Dependency Trees. Technical Report CMU-CS-97-157, Carnegie Mellon University (1997)
Jensen, F.V.: Bayesian networks and decision graphs. Springer, Heidelberg (2001)
Mateo, J.L., de la Ossa, L.: LiO: an easy and flexible library of metaheuristics. Technical Report DIAB-06-04-1, Departamento de Sistemas Informáticos, Escuela Politécnica Superior de Albacete, Universidad de Castilla-La Mancha (2006)
Pelikan, M., Sastry, K., Butz, M.V., Goldberg, D.E.: Hierarchical BOA on Random Decomposable Problems. Technical Report MEDAL Report No. 2006001, Missouri Estimation of Distribution Algorithms Laboratory (2006)
Mühlenbein, H., Shlierkamp-Voosen, D.: The science of breeding and its application to the breeder generic algorithm (BGA). Evolutionary Computation 1, 335–360 (1993)
Baluja, S.: Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Funtion Optimization and Competitive Learning. Technical Report CMU-CS-94-163, Carnegie Mellon University - School of Computer Science (1994)
Mühlenbein, H., Mahnig, T., Ochoa-Rodriguez, A.: Schemata, Distributions and Graphical Models in Evolutionary Optimization. Journal of Heuristics 5, 215–247 (1999)
Pelikan, M., Sastry, K., Butz, M.V., Goldberg, D.E.: Generator and Interface for Random Decomposable Problems in C. Technical Report MEDAL Report No. 2006003, Missouri Estimation of Distribution Algorithms Laboratory (2006)
Alcobé, J.R.: An approximated MDL score for learning Bayesian networks. In: Proceedings of the Second European Workshop on Probabilistic Graphical Models 2004 (PGM’04), pp. 185–192 (2004)
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Gámez, J.A., Mateo, J.L., Puerta, J.M. (2007). EDNA: Estimation of Dependency Networks Algorithm. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_43
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DOI: https://doi.org/10.1007/978-3-540-73053-8_43
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