Observatorio de I+D+i UPM

Memorias de investigación
Book chapters:
Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks
Year:2012
Research Areas
  • Artificial intelligence (neuronal nets, expert systems, etc)
Information
Abstract
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.
International
Si
http://dx.doi.org/10.1007/978-3-642-28900-2_10
Book Edition
14
Book Publishing
Siddhartha Shakya and Roberto Santana
ISBN
978-3-642-28900-2
Series
Adaptation, Learning, and Optimization
Book title
Markov Networks in Evolutionary Computation
From page
157
To page
173
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: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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