A review on probabilistic graphical models in evolutionary computation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Larranaga:2012:JH,
-
author = "Pedro Larranaga and Hossein Karshenas and
Concha Bielza and Roberto Santana",
-
title = "A review on probabilistic graphical models in
evolutionary computation",
-
journal = "Journal of Heuristics",
-
year = "2012",
-
volume = "18",
-
number = "5",
-
pages = "795--819",
-
publisher = "Springer",
-
keywords = "genetic algorithms, genetic programming, EDA,
Probabilistic graphical model, Bayesian network,
Evolutionary computation, Estimation of distribution
algorithm, model based GP, PIPE, ECGP, GP-EDA",
-
ISSN = "1381-1231",
-
language = "English",
-
URL = "http://dx.doi.org/10.1007/s10732-012-9208-4",
-
DOI = "doi:10.1007/s10732-012-9208-4",
-
size = "25 pages",
-
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 modelling in these algorithms.",
-
notes = "Stuff on GP mostly on page 813",
- }
Genetic Programming entries for
Pedro Larranaga
Hossein Karshenas
Concha Bielza
Roberto Santana
Citations