IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
A Hybrid Data Mining Approach To Discover Bayesian Networks Using Evolutionary Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Journal of Artificial Intelligence Research
Building a GA from design principles for learning Bayesian networks
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Fuzzy intervals for designing structural signature: an application to graphic symbol recognition
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
A cooperative coevolutionary genetic algorithm for learning bayesian network structures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multiple hypothesis testing and quasi essential graph for comparing two sets of bayesian networks
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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This paper describes two approaches based on evolutionary algorithms for determining Bayesian networks structures from a database of cases. One major difficulty when tackling the problem of structure learning with evolutionary strategies is to avoid the premature convergence of the population to a local optimum.In this paper, we propose two methods in order to overcome this obstacle.The first method is a hybridization of a genetic algorithm with a tabu search principle whilst the second method consists in the application of a dynamic mutation rate. For both methods, a repair operator based on the mutual information between the variables was defined to ensure the closeness of the genetic operators. Finally, we evaluate the influence of our methods over the search for known networks.