Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.

Campos, Luis M. de, Gámez, José A., and Puerta, José M.. "Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.." Mathware and Soft Computing 9.2-3 (2002): 251-268. <http://eudml.org/doc/39292>.

@article{Campos2002,
abstract = {The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used.A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied to solve a great variety of problems, being remarkable the performance achieved in those problems related to path (permutation) searching in graphs, such as the Traveling Salesman Problem. In two previous works [13,12], the authors have approached the problem of learning Bayesian networks by means of the search+score methodology using ACO as the search engine.As in these articles the search was performed in different search spaces, in the space of orderings [13] and in the space of directed acyclic graphs [12]. In this paper we compare both approaches by analysing the results obtained and the differences in the design and implementation of both algorithms.},
author = {Campos, Luis M. de, Gámez, José A., Puerta, José M.},
journal = {Mathware and Soft Computing},
keywords = {Análisis bayesiano; Algoritmo de búsqueda; Problemas combinatorios; Optimización global; space of orderings; space of directed acyclic graphs},
language = {eng},
number = {2-3},
pages = {251-268},
title = {Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.},
url = {http://eudml.org/doc/39292},
volume = {9},
year = {2002},
}

TY - JOUR
AU - Campos, Luis M. de
AU - Gámez, José A.
AU - Puerta, José M.
TI - Learning Bayesian networks by Ant Colony Optimisation: searching in two different spaces.
JO - Mathware and Soft Computing
PY - 2002
VL - 9
IS - 2-3
SP - 251
EP - 268
AB - The most common way of automatically learning Bayesian networks from data is the combination of a scoring metric, the evaluation of the fitness of any given candidate network to the data base, and a search procedure to explore the search space. Usually, the search is carried out by greedy hill-climbing algorithms, although other techniques such as genetic algorithms, have also been used.A recent metaheuristic, Ant Colony Optimisation (ACO), has been successfully applied to solve a great variety of problems, being remarkable the performance achieved in those problems related to path (permutation) searching in graphs, such as the Traveling Salesman Problem. In two previous works [13,12], the authors have approached the problem of learning Bayesian networks by means of the search+score methodology using ACO as the search engine.As in these articles the search was performed in different search spaces, in the space of orderings [13] and in the space of directed acyclic graphs [12]. In this paper we compare both approaches by analysing the results obtained and the differences in the design and implementation of both algorithms.
LA - eng
KW - Análisis bayesiano; Algoritmo de búsqueda; Problemas combinatorios; Optimización global; space of orderings; space of directed acyclic graphs
UR - http://eudml.org/doc/39292
ER -