Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Distributed revision of composite beliefs
Artificial Intelligence
A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Abductive reasoning in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
Partial abductive inference in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Ant algorithms for discrete optimization
Artificial Life
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
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
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
Optimal decomposition of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Axioms for probability and belief-function proagation
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
A sufficiently fast algorithm for finding close to optimal junction trees
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Belief updating in Bayesian networks by using a criterion of minimum time
Pattern Recognition Letters
Computers and Industrial Engineering
Triangulation of Bayesian networks with recursive estimation of distribution algorithms
International Journal of Approximate Reasoning
A hybrid method for learning Bayesian networks based on ant colony optimization
Applied Soft Computing
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Information Sciences: an International Journal
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The knowledge base of a probabilistic expert system is usually represented as a Bayesian network. Most of the knowledge engineering tools used in the development of probabilistic expert systems do not carry out the inference process directly over the network, but in a secondary graphical structure called a junction tree. The efficiency of inference (propagation) algorithms depends on the size of the junction tree obtained, and this size depends on the elimination sequence used during the compilation/transformation of the Bayesian network into a junction tree. The problem of searching for the best elimination sequence is an NP-hard problem [W. Wen, in: P. Bonissone, M. Henrion, L. Kanal and Z. Lemmer (Eds.), Uncertainty in Artificial Intelligence, vol. 6, North-Holland, Amsterdam, 1991, pp. 209-224], and this has motivated the proliferation of approximate methods to approach it (based variously on greedy heuristics, genetic algorithms, simulated annealing, etc.). In this paper we investigate the applicability to this problem of a new combinatorial optimization technique, inspired by a natural model, which has appeared recently: ant colony optimization [M. Dorigo, Optimization, learning and natural algorithms, Ph.D. thesis, Politecnico di Milano, Italy, 1992; M. Dorigo and L. Gambardella, IEEE Trans. Evol. Comput. 1 (1997) 53; M. Dorigo and G. Di Caru, in: New Ideas in Optimization, McGraw-Hill, New York, 1999]. Our approach is validated by using a set of complex networks obtained from a repository.