Regular vine distributions which constitute a flexible class of multivariate dependence
models are discussed. Since multivariate copulae constructed through pair-copula …

This chapter introduces copula functions and the use of the Gaussian copula function to
model probabilistic dependencies in supervised classification tasks. A copula is a …

The use of copula-based models in EDAs (estimation of distribution algorithms) is currently
an active area of research. In this context, the copulaedas package for R provides a platform …

Typical testors are a useful tool for both feature selection and for determining feature
relevance in supervised classification problems. Nowadays, generating all typical testors of …

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 …

We develop multivariate copulas for modeling multiple joint distributions of wind speeds at a
wind farm site and neighboring wind source. A n-dimensional Gaussian copula and multiple …

The aim of this work is studying the use of copulas and vines in the optimization with
Estimation of Distribution Algorithms (EDAs). Two EDAs are built around the multivariate …

Estimation of distribution algorithms based on copula functions Page 1 Estimation of
Distribution Algorithms based on Copula Functions Rogelio Salinas-Gutiérrez ∗ …

This paper presents the use of graphical models and copula functions in Estimation of
Distribution Algorithms (EDAs) for solving multivariate optimization problems. It is shown in …

In this work a mathematical model for evolutionary optimization is proposed. It takes
advantages of the good properties of copula-vines to overcome many of the limitations of …