Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning belief networks from data: an information theory based approach
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
A Permutation Genetic Algorithm For Variable Ordering In Learning Bayesian Networks From Data
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
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
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
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We propose a new Hybrid Genetic Algorithm (HGA) developed from the domain of evolutionary algorithms to evolve optimal Bayesian networks from datasets. For its learning process, it uses genetic operators engineered from information theoretic and mathematical fields including Mutual Information (MI), Extended Dependency Analysis (EDA), Mathematical Power Sets and Minimum Description Length (MDL). Unlike our HGA, existing genetic algorithms (GAs) use genetic operators that usually use backtracking, which is an overhead for a learning algorithm. In our research, we prevented backtracking using an inner-loop and carried out several evaluation experiments. Our empirical results and structural evaluations showed that a HGA can discover optimal networks from datasets that we selected from different domains.