Enrique Raúl Villa Diharce

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We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "m-perturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in Particle Swarm(More)
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A new Estimation of Distribution Algorithm is presented. The proposed algorithm, called D-vine EDA, uses a graphical model which is based on pair copula decomposition. By means of copula functions it is possible to model the dependence structure in a joint distribution with marginals of different type. Thus, this paper introduces the D-vine EDA and performs(More)
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(More)
This paper introduces copula functions and the use of the Gaussian copula function to model probabilistic dependencies in supervised classification tasks. A copula is a distribution function with the implicit capacity to model non linear dependencies via concordance measures , such as Kendall's τ. Hence, this work studies the performance of a simple(More)
We introduce the PESO (particle evolutionary swarm optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "m-perturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in particle swarm(More)
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 distribution function with the implicit capacity to model non linear dependencies via concordance measures , such as Kendall's τ. Hence, this chapter studies the performance of a simple(More)