Abstract
In this paper we evaluate a new Estimation of Distribution Algorithm (EDA) constructed on top of a very successful Bayesian network learning procedure, Max-Min Hill-Climbing (MMHC). The aim of this paper is to check whether the excellent properties reported for this algorithm in machine learning papers, have some impact on the efficiency and efficacy of EDA based optimization. Our experiments show that the proposed algorithm outperform well-known state of the art EDA like BOA and EBNA in a test bed based on B-functions. On the basis of these results we conclude that the proposed scheme is a promising candidate for challenging real-world applications, specifically, problems related to the areas of Data Mining, Patter Recognition and Artificial Intelligence.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ding, C., Ding, L., Peng, W.: Comparison of effects of different learning methods on estimation of distribution algorithms. J. Softw. Eng. 9(3), 451–468 (2015)
Gao, S., Qiu, L., Cao, C.: Solving 0–1 integer programming problem by estimation of distribution algorithms. J. Comput. Intell. Electr. Syst. 3(1), 65–68 (2014)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Optimization. Kluwer Academic, Boston (2002)
Madera, J.: Hacia una Generación Eficiente de Algoritmos Evolutivos con Estimación de Distribuciones: Pruebas de (In)dependencia +Paralelismo. Ph.D. thesis, Instituto de Cibernética, Matemática y Física, La Habana. Adviser: A. Ochoa (2009). (in Spanish)
Ochoa, A.: Opportunities for expensive optimization with estimation of distribution algorithms. In: Tenne, Y., Goh, C.-K. (eds.) Computational Intelligence in Expensive Optimization Problems. ALO, vol. 2, pp. 193–218. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-10701-6_8
Ochoa, A., Soto, M.: Linking entropy to estimation of distribution algorithms. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation: Advances in Estimation of Distribution Algorithms. STUDFUZZ, vol. 192, pp. 1–38. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32494-1_1
Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: the Bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, pp. 525–532. Morgan Kaufmann, San Francisco (1999)
Pérez-Rodríguez, R., Hernández-Aguirre, A.: An estimation of distribution algorithm-based approach for the order batching problem: an experimental study. In: Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations. IGI Global (2016)
Preetam, N., Hauser, A., Maathuis, M.H.: High-dimensional consistency in score-based and hybrid structure learning. arXiv preprint arXiv:1507.02608 (2018)
Scutari, M., Ness, R.: bnlearn: Bayesian network structure learning, parameter learning and inference. R package version 3 (2012)
Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65(1), 31–78 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Madera, J., Ochoa, A. (2018). Evaluating the Max-Min Hill-Climbing Estimation of Distribution Algorithm on B-Functions. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-01132-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01131-4
Online ISBN: 978-3-030-01132-1
eBook Packages: Computer ScienceComputer Science (R0)