IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on learning with Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Analyzing Directed Acyclic Graph Recombination
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning Bayesian networks from incomplete databases using a novel evolutionary algorithm
Decision Support Systems
Learning Transcriptional Regulatory Networks with Evolutionary Algorithms Enhanced with Niching
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Building a GA from design principles for learning Bayesian networks
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Specifying evolutionary algorithms in XML
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
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Bayesian networks (BNs) constitute a useful tool to model the joint distribution of a set of random variables of interest. This paper is concerned with the network induction problem. We propose a number of hybrid recombination operators for extracting BNs from data. These hybrid operators make use of phenotypic information in order to guide the processing of information during recombination. The performance of these new operators is analyzed with respect to that of their genotypic counterparts. It is shown that these hybrid operators provide notably improved and rather robust results. Some remarks on the future of the area are also laid out.