Por favor, use este identificador para citar o enlazar este ítem: https://repositorio.uci.cu/jspui/handle/123456789/9481
Título : Evaluating the Max-Min Hill-Climbing Estimation of Distribution Algorithm on B-Functions
Autor : Madera, Julio
Ochoa, Alberto
Fecha de publicación : 2018
Editorial : Springer
Citación : 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
Resumen : 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.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9481
Aparece en las colecciones: Eventos

Ficheros en este ítem:
Fichero Tamaño Formato  
A029.pdf101.8 kBAdobe PDFVisualizar/Abrir

Los ítems del Repositorio están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.