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Memorias de investigación
Artículos en revistas:
Regularized continuous estimation of distribution algorithms
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Regularization is a well-known technique in statistics for model estimation which is used to improve the generalization ability of the estimated model. Some of the regularization methods can also be used for variable selection that is especially useful in high-dimensional problems. This paper studies the use of regularized model learning in estimation of distribution algorithms (EDAs) for continuous optimization based on Gaussian distributions. We introduce two approaches to the regularized model estimation and analyze their effect on the accuracy and computational complexity of model learning in EDAs. We then apply the proposed algorithms to a number of continuous optimization functions and compare their results with other Gaussian distribution-based EDAs. The results show that the optimization performance of the proposed RegEDAs is less affected by the increase in the problem size than other EDAs, and they are able to obtain significantly better optimization values for many of the functions in high-dimensional settings.
Internacional
Si
JCR del ISI
Si
Título de la revista
Applied Soft Computing
ISSN
1568-4946
Factor de impacto JCR
2,14
Información de impacto
Volumen
13
DOI
Número de revista
5
Desde la página
2412
Hasta la página
2432
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: H. Karshenas
  • Autor: R. Santana
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: P. Larrañaga
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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