Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
A review on probabilistic graphical models in evolutionary computation,
Year:2012
Research Areas
  • Environmental intelligence
Information
Abstract
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.
International
Si
JCR
Si
Title
Journal of Heuristics
ISBN
1381-1231
Impact factor JCR
1,262
Impact info
Datos JCR del año 2011
Volume
18
Journal number
5
From page
795
To page
819
Month
SIN MES
Ranking
Participants
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Autor: Hossein Karshenas Najafabadi (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Roberto Santana Hermida (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)