Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximations of Causal Networks by Polytrees: an Empirical Study
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
Learning in belief networks and its application to distributed databases
Learning in belief networks and its application to distributed databases
A formal approach to using data distributions for building causal polytree structures
Information Sciences—Informatics and Computer Science: An International Journal
The correlation-triggered adaptive variance scaling IDEA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Learning Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
The gaussian polytree EDA for global optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
This paper introduces the Gaussian polytree estimation of distribution algorithm, a new construction method, and its application to estimation of distribution algorithms in continuous variables. The variables are assumed to be Gaussian. The construction of the tree and the edges orientation algorithm are based on information theoretic concepts such as mutual information and conditional mutual information. The proposed Gaussian polytree estimation of distribution algorithm is applied to a set of benchmark functions. The experimental results show that the approach is robust, comparisons are provided.