DocumentCode :
2454239
Title :
Evolutionary Algorithm Using Random Multi-point Crossover Operator for Learning Bayesian Network Structures
Author :
dos Santos, Edimilson B. ; Hruschka, Estevam R., Jr. ; Ebecken, Nelson F F
Author_Institution :
COPPE, UFRJ- Fed. Univ. of Rio de Janeiro, Rio de Janeiro, Brazil
fYear :
2010
fDate :
12-14 Dec. 2010
Firstpage :
430
Lastpage :
435
Abstract :
Variable Ordering plays an important role when inducing Bayesian Networks. Previous works in the literature suggest that the use of genetic/evolutionary algorithms (EAs) for dealing with VO, when learning a Bayesian Network structure from data, is worth pursuing. This work proposes a new crossover operator, named Random Multi-point Crossover Operator (RMX), to be used with the Variable Ordering Evolutionary Algorithm (VOEA). Empirical results obtained by VOEA are compared to the ones achieved by VOGA (Variable Ordering Genetic Algorithm), and indicated improvement in the quality of VO and the induced BN structure.
Keywords :
Bayes methods; data structures; evolutionary computation; learning (artificial intelligence); Bayesian network structure; crossover operator; random multipoint crossover operator; variable ordering evolutionary algorithm; Algorithm design and analysis; Bayesian methods; Biological cells; Convergence; Evolutionary computation; Genetics; Search problems; Bayesian Networks; Evolutionary Algorithms; Variable Orderings;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
Type :
conf
DOI :
10.1109/ICMLA.2010.70
Filename :
5708867
Link To Document :
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