A review on probabilistic graphical models in evolutionary computation

Larrañaga Múgica, Pedro and Karshenas, Hossein and Bielza Lozoya, Maria Concepcion and Santana, Roberto (2012). A review on probabilistic graphical models in evolutionary computation. "Journal of Heuristics", v. 18 (n. 5); pp. 795-819. ISSN 1381-1231.

Description

Title: A review on probabilistic graphical models in evolutionary computation
Author/s:
  • Larrañaga Múgica, Pedro
  • Karshenas, Hossein
  • Bielza Lozoya, Maria Concepcion
  • Santana, Roberto
Item Type: Article
Título de Revista/Publicación: Journal of Heuristics
Date: August 2012
Volume: 18
Subjects:
Freetext Keywords: Probabilistic graphical model, Bayesian network, Evolutionary computation, Estimation of distribution algorithm, Modelo gráfico porbabilístico, red Bayesiana, Computación evolutiva.
Faculty: Facultad de Informática (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Official URL: http://www.springer.com/?SGWID=5-102-0-0-0

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.

More information

Item ID: 15826
DC Identifier: http://oa.upm.es/15826/
OAI Identifier: oai:oa.upm.es:15826
Deposited by: Memoria Investigacion
Deposited on: 18 Jun 2013 14:08
Last Modified: 21 Apr 2016 16:07
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