Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
The equation for response to selection and its use for prediction
Evolutionary Computation
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Estimation of distribution algorithm based on archimedean copulas
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
Estimation of distribution algorithm based on copula theory
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Using Copulas in Estimation of Distribution Algorithms
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
D-vine EDA: a new estimation of distribution algorithm based on regular vines
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Estimation of distribution algorithms based on copula functions
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A Boltzmann based estimation of distribution algorithm
Information Sciences: an International Journal
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In this paper, a new Estimation of Distribution Algorithm (EDA) is presented. The proposed algorithm employs a dependency tree as a graphical model and bivariate copula functions for modeling relationships between pairwise variables. By selecting copula functions it is possible to build a very flexible joint distribution as a probabilistic model. The experimental results show that the proposed algorithm has a better performance than EDAs based on Gaussian assumptions.