Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Management Science
Easy problems for tree-decomposable graphs
Journal of Algorithms
Graph minors. XIII: the disjoint paths problem
Journal of Combinatorial Theory Series B
Partial abductive inference in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms
Statistics and Computing
Searching for the best elimination sequence in Bayesian networks by using ant colony optimization
Pattern Recognition Letters
Heuristic Algorithms for the Triangulation of Graphs
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Triangulation of Bayesian Networks: A Relational Database Perspective
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Optimal decomposition of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Pre-processing for Triangulation of Probabilistic Networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
The equation for response to selection and its use for prediction
Evolutionary Computation
A sufficiently fast algorithm for finding close to optimal junction trees
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A preliminary study on EDAs for permutation problems based on marginal-based models
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems
Computers and Industrial Engineering
Introducing the mallows model on estimation of distribution algorithms
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
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Bayesian networks can be used as a model to make inferences in domains with intrinsic uncertainty, that is, to determine the probability distribution of a set of variables given the instantiation of another set. The inference is an NP-hard problem. There are several algorithms to make exact and approximate inference. One of the most popular, and that is also an exact method, is the evidence propagation algorithm of Lauritzen and Spiegelhalter [S.L. Lauritzen, D.J. Spiegelhalter, Local computations with probabilities on graphical structures and their application on expert systems, Journal of the Royal Statistical Society B 50 (2) (1988) 157-224], improved later by Jensen et al. [F.V. Jensen, S.L. Lauritzen, K.G. Olesen, Bayesian updating in causal probalistic networks by local computations, In Computational Statistics Quaterly 4 (1990) 269-282]. This algorithm needs an ordering of the variables in order to make the triangulation of the moral graph associated with the original Bayesian network structure. The effectiveness of the inference depends on the variable ordering. In this paper, we will use a new paradigm for evolutionary computation, the estimation of distribution algorithms (EDAs), to get the optimal ordering of the variables to obtain the most efficient triangulation. We will also present a new type of evolutionary algorithm, the recursive EDAs (REDAs). We will prove that REDAs improve the behaviour of EDAs in this particular problem, and that their results are competitive with other triangulation techniques.