Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Regularized continuous estimation of distribution algorithms
Year:2013
Research Areas
  • Artificial intelligence (neuronal nets, expert systems, etc)
Information
Abstract
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 signi?cantly better optimization values for many of the functions in high-dimensional settings.
International
Si
JCR
Si
Title
Applied Soft Computing
ISBN
1568-4946
Impact factor JCR
2,612
Impact info
Datos JCR del año 2011
Volume
13
Journal number
5
From page
2412
To page
2432
Month
SIN MES
Ranking
Participants
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Roberto Santana Hermida (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Inteligencia Artificial
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