Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Complex Probabilistic Modeling with Recursive Relational Bayesian Networks
Annals of Mathematics and Artificial Intelligence
Schemata, Distributions and Graphical Models in Evolutionary Optimization
Journal of Heuristics
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
Relational Dependency Networks
The Journal of Machine Learning Research
The equation for response to selection and its use for prediction
Evolutionary Computation
EDNA: Estimation of Dependency Networks Algorithm
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Graph clustering based model building
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
A Markovianity based optimisation algorithm
Genetic Programming and Evolvable Machines
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One of the key points in Estimation of Distribution Algorithms (EDAs) is the learning of the probabilistic graphical model used to guide the search: the richer the model the more complex the learning task. Dependency networks-based EDAs have been recently introduced. On the contrary of Bayesian networks, dependency networks allow the presence of directed cycles in their structure. In a previous work the authors proposed EDNA, an EDA algorithm in which a multivariate dependency network is used but approximating its structure learning by considering only bivariate statistics. EDNA was compared with other models from the literature with the same computational complexity (e.g., univariate and bivariate models). In this work we propose a modified version of EDNA in which not only the structural learning phase is limited to bivariate statistics, but also the simulation and the parameter learning task. Now, we extend the comparison employing multivariate models based on Bayesian networks (EBNA and hBOA). Our experiments show that the modified EDNA is more accurate than the original one, being its accuracy comparable to EBNA and hBOA, but with the advantage of being faster specially in the more complex cases.