<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2227-1899</journal-id>
<journal-title><![CDATA[Revista Cubana de Ciencias Informáticas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev cuba cienc informat]]></abbrev-journal-title>
<issn>2227-1899</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ediciones Futuro]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2227-18992020000400001</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Algoritmo de estimación de distribución basado en el aprendizaje de redes bayesianas con análisis de dependencias para problemas de optimización en enteros]]></article-title>
<article-title xml:lang="en"><![CDATA[Estimation of distribution algorithm based on Bayesian networks learning with dependency analysis for integer optimization problems]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Madera Quintana]]></surname>
<given-names><![CDATA[Julio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez López]]></surname>
<given-names><![CDATA[Yoan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fernández Pardo]]></surname>
<given-names><![CDATA[José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Camagüey Facultad de Informática y Ciencias Exactas Departamento de Informática]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>14</volume>
<numero>4</numero>
<fpage>1</fpage>
<lpage>19</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992020000400001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992020000400001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992020000400001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN A partir del estudio del algoritmo de estimación de distribuciones (EDA) basado en poliárboles se propone e investiga la clase de algoritmos EDA que utilizan pruebas de independencias en el aprendizaje de la estructura probabilística. Estos algoritmos se conocen como EDA basados en restricciones los que definen una clase de EDA llamada algoritmos de estimación de distribuciones con restricciones (CBEDA). Como resultado se propone un nuevo algoritmo CBEDATPDA que utiliza el método de detección de dependencias de tres fases para el aprendizaje de redes Bayesianas. Los resultados experimentales demuestran que la nueva propuesta exhibe adecuadas cualidades numéricas para la solución de problemas con codificación entera como son las funciones decepcionantes y el problema de la predicción de estructuras de proteínas (PSP, del inglés, Protein Structure Prediction). Los resultados son comparados con otros algoritmos del estado del arte de la computación evolutiva, incluyendo propuestas del campo de los EDA.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT From the study of the Estimation of Distribution Algorithms (EDA) based on polytrees, we propose and evaluate the class of EDA algorithms that use independence tests for learning the probabilistic model. These algorithms are known as constraint-based EDA which define a class of EDA called constraint-based estimation of distribution algorithms (CBEDA). As a result, a new CBEDA TPDA algorithm is proposed using the three-phase dependence detection method for learning Bayesian networks. The experimental results show that the new proposal has adequate numerical qualities for the solution of optimization problems with integer representation such as the deceptive functions and the problem of protein structure prediction (PSP). The results are compared with other state-of-the-art algorithms in evolutionary computation, including proposals from the EDA field.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Algoritmos de estimación de distribuciones]]></kwd>
<kwd lng="es"><![CDATA[optimización entera]]></kwd>
<kwd lng="es"><![CDATA[predicción de la estructura de proteínas]]></kwd>
<kwd lng="es"><![CDATA[pruebas de independencia]]></kwd>
<kwd lng="en"><![CDATA[Estimation of distribution algorithms]]></kwd>
<kwd lng="en"><![CDATA[integer optimization]]></kwd>
<kwd lng="en"><![CDATA[protein structure prediction]]></kwd>
<kwd lng="en"><![CDATA[independence tests]]></kwd>
</kwd-group>
</article-meta>
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