<?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>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462024000200739</article-id>
<article-id pub-id-type="doi">10.13053/cys-28-2-5018</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Novel Dynamic Decomposition-Based Multi-Objective Evolutionary Algorithm Using Reinforcement Learning Adaptive Operator Selection (DMOEA/D-SL)]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Brambila-Hernández]]></surname>
<given-names><![CDATA[José Alfredo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-Morales]]></surname>
<given-names><![CDATA[Miguel Ángel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fraire-Huacuja]]></surname>
<given-names><![CDATA[Héctor Joaquín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz-Reyes]]></surname>
<given-names><![CDATA[Laura]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez-Santillán]]></surname>
<given-names><![CDATA[Claudia G.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rangel-Valdez]]></surname>
<given-names><![CDATA[Nelson]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Puga-Soberanes]]></surname>
<given-names><![CDATA[Héctor José]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Balderas]]></surname>
<given-names><![CDATA[Fausto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Ciudad Madero ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de León ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2024</year>
</pub-date>
<volume>28</volume>
<numero>2</numero>
<fpage>739</fpage>
<lpage>749</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462024000200739&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462024000200739&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462024000200739&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Within the multi-objective (static) optimization field, various works related to the adaptive selection of genetic operators can be found. These include multi-armed bandit-based methods and probability-based methods. For dynamic multi-objective optimization, finding this type of work is very difficult. The main characteristic of dynamic multi-objective optimization is that its problems do not remain static over time; on the contrary, its objective functions and constraints change over time. Adaptive operator selection is responsible for selecting the best variation operator at a given time within a multi-objective evolutionary algorithm process. This work proposes incorporating a new adaptive operator selection method into a Dynamic Multi-objective Evolutionary Algorithm Based on Decomposition algorithm, which we call DMOEA/D-SL. This new adaptive operator selection method is based on a reinforcement learning algorithm called State-Action-Reward-State-Action Lambda or SARSA (&#955;). SARSA Lambda trains an Agent in an environment to make sequential decisions and learn to maximize an accumulated reward over time; in this case, select the best operator at a given moment. Eight dynamic multi-objective benchmark problems have been used to evaluate algorithm performance as test instances. Each problem produces five Pareto fronts. Three metrics were used: Inverted Generational Distance, Generalized Spread, and Hypervolume. The non-parametric statistical test of Wilcoxon was applied with a statistical significance level of 5% to validate the results.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Adaptive]]></kwd>
<kwd lng="en"><![CDATA[operator]]></kwd>
<kwd lng="en"><![CDATA[selection]]></kwd>
<kwd lng="en"><![CDATA[dynamic]]></kwd>
<kwd lng="en"><![CDATA[multi-objective]]></kwd>
<kwd lng="en"><![CDATA[optimization]]></kwd>
</kwd-group>
</article-meta>
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