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 this work how the incorporation of copula functions and graphical models for modeling the dependencies among variables provides some theoretical advantages over traditional EDAs. By means of copula functions and two w...

In evolutionary continuous optimization by building and using probabilistic models, the multivariate Gaussian distribution and their variants or extensions such as the mixture of Gaussians have been used popularly. However, this Gaussian assumption is often violated in many real problems. In this paper, we propose a new continuous estimation of distribution algorithms (EDAs) with the variationa...

EDAs guide search by building and sampling an explicit probabilitic model of high-quality solutions. EDAs can solve broad classes of hard problems scalably, often in low-order polynomial time. But scalable performance is sometimes not enough.

This chapter serves as an introduction to estimation of distribution algorithms (EDAs). Estimation of distribution algorithms are a new paradigm in evolutionary computation. They combine statistical learning with population-based search in order to automatically identify and exploit certain structural properties of optimization problems. State-of-the-art EDAs consistently outperform classical g...

The aim of this work is studying the use of copulas and vines in numerical optimization with Estimation of Distribution Algorithms (EDAs). Two EDAs built around the multivariate product and normal copulas, and other two based on pair-copula decomposition of vine models are studied. We analyze empirically the effect of both marginal distributions and dependence structure in order to show that bo...

Estimation of Distribution Algorithms (EDAs) (Mühlenbein et al., 1996; Mühlenbein & PaaB, 1996) are a promising area of research in evolutionary computation. EDAs propose to create models that can capture the dependencies among the decision variables. The widely known Genetic Algorithm could benefit from the available dependencies if the building blocks of the solution were correlated. However,...

In this paper, we discuss a curious relationship between Cooperative Coevolutionary Algorithms (CCEAs) and Univariate EDAs. Inspired by the theory of CCEAs, we also present a new EDA with theoretical convergence guarantees, and some preliminary experimental results in comparison with existing Univariate EDAs.

The aim of this work was to update the validation of the Social Anxiety Screening Scale (SASS/EDAS) in a sample of Spanish adolescents. To achieve this, one study with a sample of 1489 students in secondary education, of ages 14 to 17 years, were carried out. The psychometric properties of EDAS were examined through confirmatory factor analysis, reliability (Cronbach's alpha), temporal stabilit...

This chapter serves as an introduction to estimation of distribution algorithms. Estimation of distribution algorithms are a new paradigm in evolutionary computation. State-of-the-art EDAs consistently outperform classical genetic algorithms on a broad range of problems. We review the fundamental principles and algorithms that are necessary to understand EDA research. We focus on EDAs for the d...