Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Annals of Mathematics and Artificial Intelligence
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
Estimation of distribution algorithm based on archimedean copulas
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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
Dependence trees with copula selection for continuous estimation of distribution algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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The main objective of this doctoral research is to study Estimation of Distribution Algorithms (EDAs) based on copula functions. This new class of EDAs has shown that it is possible to incorporate successfully copula functions in EDAs.