Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Evolutionary algorithms: from recombination to search distributions
Theoretical aspects of evolutionary computing
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
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
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The equation for response to selection and its use for prediction
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Permutation Optimization by Iterated Estimation of Random Keys Marginal Product Factorizations
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Evolutionary flexible neural networks for intrusion detection system
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A Markovianity based optimisation algorithm
Genetic Programming and Evolvable Machines
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Single connected Factorized Distribution Algorithms (FDA-SC) use factorizations of the joint distribution, which are trees, forests or polytrees. At each stage of the evolution they build a polytree from which new points are sampled. We study empirically the relation between the accuracy of the learned model and the quality of the new search points generated. We show that a change of the learned model before sampling might reduce the population size requirements of sampling.