<?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>S2227-18992020000300059</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Ajuste de parámetros de algoritmos genéticos: propuesta de método compuesto]]></article-title>
<article-title xml:lang="en"><![CDATA[Parameter tuning of genetic algorithms: composite method proposal]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Negrin Díaz]]></surname>
<given-names><![CDATA[Iván A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Negrin Hernández]]></surname>
<given-names><![CDATA[Luis I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chagoyén Méndez]]></surname>
<given-names><![CDATA[Ernesto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,UCLV Facultad de Construcciones Departamento de Ing. Civil]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,UCLV FIMI Departamento de Ing. Mecánica]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<volume>14</volume>
<numero>3</numero>
<fpage>59</fpage>
<lpage>82</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2227-18992020000300059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2227-18992020000300059&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2227-18992020000300059&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El ajuste de parámetros permite encontrar la mejor configuración de los métodos de optimización ante un determinado problema. Este es un proceso extremadamente costoso desde el punto de vista computacional. Algunos procesos como la optimización estructural requieren un enorme consumo de recursos computacionales, por lo que resulta casi imposible ajustar los parámetros del método utilizado. Una manera de evitar este inconveniente es utilizar funciones analíticas (o de referencia) que simulen las características principales de las funciones objetivo reales. En este artículo se utiliza la función Eggholder para ajustar los parámetros de los algoritmos genéticos (GA), utilizando como utilidad la representación gráfica de la curva de rendimiento promedio y su correspondiente valor MBF. Los resultados obtenidos plantean que, para optimizar este tipo de funciones de alta complejidad, y obtener resultados satisfactorios utilizando GA simple, es necesario establecer tamaños de población grandes (300 o más). La mejor configuración resultó ser realizar la selección uniforme, el cruzamiento heurístico, la reproducción estableciendo un elitismo del 15 % de la población total y una fracción de cruzamiento de 0.60. Debido a la incapacidad del GA simple de encontrar el óptimo global de manera regular se plantean otras soluciones. La primera es optimizar utilizando variables enteras. La segunda consiste en generar una población inicial utilizando el propio algoritmo de GA simple, lo cual denominamos método compuesto. Esta estrategia fue capaz de encontrar el óptimo global el 100% de las pruebas, a costa de un incremento del costo computacional en un 16%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Parameter tuning deals with finding the best configuration of an optimization method in a given problem. It is an extremely high computing process. Some problems, such as structural optimization, need enormous resource consumption, so tuning the method´s parameters in these processes is certainly expensive. One way to avoid this drawback is to use analytical functions (or benchmark functions), simulating the main features of real ones. In this paper, the Eggholder function is used as case study to tune the parameters of the Genetic Algorithms (GA), using as utility the graphical visualization of the average performance curve, and its correspondent MBF value. The results showed that for optimizing these high-complexity functions, it is necessary to establish high population sizes (300 or more). The best configuration reached was using uniform selection, heuristic crossover, reproduction establishing an elitism of 15 % of the population size and a crossover fraction of 0.60. Due to the inability of simple GA of finding the global optimum regularly, other solutions are recommended. The first one is using discrete variables in the optimization process. The second one consists on creating an initial population, using the simple GA itself. This is what we call compound method, and it was capable to find the global optimum the 100% of the tests, demanding only 16% more computational consumption.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[optimización]]></kwd>
<kwd lng="es"><![CDATA[ajuste de parámetros]]></kwd>
<kwd lng="es"><![CDATA[Algoritmos Genéticos]]></kwd>
<kwd lng="es"><![CDATA[población]]></kwd>
<kwd lng="es"><![CDATA[selección]]></kwd>
<kwd lng="es"><![CDATA[cruzamiento]]></kwd>
<kwd lng="en"><![CDATA[optimization]]></kwd>
<kwd lng="en"><![CDATA[parameter tuning]]></kwd>
<kwd lng="en"><![CDATA[Genetic Algorithms]]></kwd>
<kwd lng="en"><![CDATA[population]]></kwd>
<kwd lng="en"><![CDATA[selection]]></kwd>
<kwd lng="en"><![CDATA[crossover]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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