Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Dynamic flies: a new pattern recognition tool applied to stereo sequence processing
Pattern Recognition Letters
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Genetic Algorithm with Sharing for the Detection of 2D Geometric Primitives in Images
AE '95 Selected Papers from the European conference on Artificial Evolution
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Evolving dynamic Bayesian networks with Multi-objective genetic algorithms
Applied Intelligence
The cooperative royal road: avoiding hitchhiking
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
IEEE Transactions on Evolutionary Computation
Grammar-guided evolutionary construction of bayesian networks
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Bayesian network structure learning from limited datasets through graph evolution
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
A memetic approach to bayesian network structure learning
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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We propose a cooperative-coevolution - Parisian trend - algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It is based on an intermediate stage which consists of evaluating an independence model of the data to be modelled. The Parisian cooperative coevolution is particularly well suited to the structure of this intermediate problem, and allows to represent an independence model with help of a whole population, each individual being an independence statement, i.e. a component of the independence model. Once an independence model is estimated, a Bayesian network can be built. This two level resolution of the complex problem of Bayesian network structure estimation has the major advantage to avoid the difficult problem of direct acyclic graph representation within an evolutionary algorithm, which causes many troubles related to constraints handling and slows down algorithms. Comparative results with a deterministic algorithm, PC, on two test cases (including the Insurance BN benchmark), prove the efficiency of IMPEA, which provides better results than PC in a comparable computation time, and which is able to tackle more complex issues than PC.