Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
A review on probabilistic graphical models in evolutionary computation,
Año:2012
Áreas de investigación
  • Inteligencia ambiental
Datos
Descripción
Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.
Internacional
Si
JCR del ISI
Si
Título de la revista
Journal of Heuristics
ISSN
1381-1231
Factor de impacto JCR
1,262
Información de impacto
Datos JCR del año 2011
Volumen
18
DOI
Número de revista
5
Desde la página
795
Hasta la página
819
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Roberto Santana Hermida (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
S2i 2021 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
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