Título A Multi-Objective Evolutionary Algorithm for enhancing Bayesian Networks hybrid-based modeling
Autores GARCÍA ALONSO, CARLOS, CAMPOY MUÑOZ, MARÍA DEL PILAR, SALAZAR ORDÓÑEZ, MELANIA, CAMPOY MUÑOZ, MARÍA DEL PILAR, GARCÍA ALONSO, CARLOS, SALAZAR ORDÓÑEZ, MELANIA
Publicación externa No
Medio Comput. Math. Appl.
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 1.99600
Impacto SJR 1.29500
Ámbito Internacional
Fecha de publicacion 01/12/2013
ISI 000327415000021
Scopus Id 2-s2.0-84887016659
DOI 10.1016/j.camwa.2013.01.029
Abstract Bayesian Networks are increasingly being used to model complex socio-economic systems by expert knowledge elicitation even when data is scarce or does not exist. In this paper, a Multi-Objective Evolutionary Algorithm (MOEA) is presented for assessing the parameters (input relevance/weights) of fuzzy dependence relationships in a Bayesian Network (BN). The MOEA was designed to include a hybrid model that combines Monte-Carlo simulation and fuzzy inference. The MOEA-based prototype assesses the input weights of fuzzy dependence relationships by learning from available output data. In socio-economic systems, the determination of how a specific input variable affects the expected results can be critical and it is still one of the most important challenges in Bayesian modeling. The MOEA was checked by estimating the migrant stock as a relevant variable in a BN model for forecasting remittances. For a specific year, results showed similar input weights than those given by economists but it is very computationally demanding. The proposed hybrid-approach is an efficient procedure to estimate output values in BN. (C) 2013 Elsevier Ltd. All rights reserved.
Palabras clave Multi-Objective Evolutionary Algorithms; Fuzzy inference; Bayesian networks; Monte-Carlo simulation; Remittances
Miembros de la Universidad Loyola

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