Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Year:2013
Research Areas
  • Artificial intelligence (neuronal nets, expert systems, etc)
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. Bayesian networks are one of the most widely used class of these models. Some of the inference and learning tasks in Bayesian networks involve complex optimization problems that require the use of meta-heuristic algorithms. Evolutionary algorithms, as successful problem solvers, are promising candidates for this purpose. This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning.
International
Si
JCR
Si
Title
Information Sciences
ISBN
0020-0255
Impact factor JCR
2,833
Impact info
Datos JCR del año 2011
Volume
233
Journal number
0
From page
109
To page
125
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: 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)