Echegoyen Arruti, Carlos

Profile Picture

Email Address

person.page.identifierURI

https://academica-e.unavarra.es/handle/2454/48923

Birth Date

Job Title

Last Name

Echegoyen Arruti

First Name

Carlos

person.page.departamento

Estadística, Informática y Matemáticas

person.page.instituteName

InaMat2. Instituto de Investigación en Materiales Avanzados y Matemáticas

ORCID

0000-0001-6704-7012

person.page.observainves

751490

person.page.upna

812413

Name

Filters

2008 - 20102
Reset filters

Settings

Author: Larrañaga, Pedro × End date: 2019 ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationOpen Access
    The impact of exact probabilistic learning algorithms in EDAs based on bayesian networks
    (Springer, 2008) Echegoyen Arruti, Carlos; Santana, Roberto; Lozano, José Antonio; Larrañaga, Pedro; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    This paper discusses exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Secondly, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished. The results obtained reveal that the quality of the problem information captured by the probability model can improve when the accuracy of the learning algorithm employed is increased. However, improvements in model accuracy do not always imply a more efficient search.
  • Thumbnail Image
    PublicationOpen Access
    Mateda-2.0: estimation of distribution algorithms in MATLAB
    (Foundation for Open Access Statistics, 2010-07-26) Larrañaga, Pedro; Santana, Roberto; Bielza, Concha; Lozano, José Antonio; Echegoyen Arruti, Carlos; Mendiburu, Alexander; Armañanzas, Rubén; Shakya, Siddartha; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
    This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.