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Memorias de investigación
Capítulo de libro:
Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks
Año:2012
Áreas de investigación
  • Inteligencia artificial (redes neuronales, lógica borrosa, sistemas expertos, etc)
Datos
Descripción
Because of their intrinsic properties, the majority of the estimation of distribution algorithms proposed for continuous optimization problems are based on the Gaussian distribution assumption for the variables. This paper looks over the relation between the general multivariate Gaussian distribution and the popular undirected graphical model of Markov networks and discusses how they can be employed in estimation of distribution algorithms for continuous optimization. A number of learning and sampling techniques for these models, including the promising regularized model learning, are also reviewed and their application for function optimization in the context of estimation of distribution algorithms is studied.
Internacional
Si
DOI
http://dx.doi.org/10.1007/978-3-642-28900-2_10
Edición del Libro
14
Editorial del Libro
Siddhartha Shakya and Roberto Santana
ISBN
978-3-642-28900-2
Serie
Adaptation, Learning, and Optimization
Título del Libro
Markov Networks in Evolutionary Computation
Desde página
157
Hasta página
173
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Roberto Santana Hermida (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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