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<title>ParadisEO-MOEO: Class List</title>
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<h1>ParadisEO-MOEO Class List</h1>Here are the classes, structs, unions and interfaces with brief descriptions:<table>
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<tr><td class="indexkey"><a class="el" href="classmoeoAdditiveBinaryEpsilonIndicator.html">moeoAdditiveBinaryEpsilonIndicator< EOFitness ></a></td><td class="indexvalue">Functor Additive binary epsilon indicator for <a class="elRef" doxygen="eo.doxytag:../../../paradiseo-eo/doc/html/" href="../../../paradiseo-eo/doc/html/classeo_pareto_fitness.html">eoParetoFitness</a> </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoArchive.html">moeoArchive< EOT ></a></td><td class="indexvalue">An archive is a secondary population that stores non-dominated solutions </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoArchiveFitnessSavingUpdater.html">moeoArchiveFitnessSavingUpdater< EOT ></a></td><td class="indexvalue">This class allows to save the fitnesses of solutions contained in an archive into a file at each generation </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoArchiveUpdater.html">moeoArchiveUpdater< EOT ></a></td><td class="indexvalue">This class allows to update the archive at each generation with newly found non-dominated solutions </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoBinaryHypervolumeIndicator.html">moeoBinaryHypervolumeIndicator< EOFitness ></a></td><td class="indexvalue">Functor Binary hypervolume indicator for <a class="elRef" doxygen="eo.doxytag:../../../paradiseo-eo/doc/html/" href="../../../paradiseo-eo/doc/html/classeo_pareto_fitness.html">eoParetoFitness</a> </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoBinaryMetricSavingUpdater.html">moeoBinaryMetricSavingUpdater< EOT ></a></td><td class="indexvalue">This class allows to save the progression of a binary metric comparing the fitness values of the current population (or archive) with the fitness values of the population (or archive) of the generation (n-1) into a file </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoBinaryQualityIndicator.html">moeoBinaryQualityIndicator< EOFitness ></a></td><td class="indexvalue">Functor Binary quality indicator Binary performance measure to use in the replacement selection process of IBEA (Indicator-Based Evolutionary Algorithm) Of course, EOFitness needs to be an <a class="elRef" doxygen="eo.doxytag:../../../paradiseo-eo/doc/html/" href="../../../paradiseo-eo/doc/html/classeo_pareto_fitness.html">eoParetoFitness</a> object </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoBinaryQualityIndicator_1_1Range.html">moeoBinaryQualityIndicator< EOFitness >::Range</a></td><td class="indexvalue">Private class to represent the bounds </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoBM.html">moeoBM< A1, A2, R ></a></td><td class="indexvalue">Base class for binary metrics </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoCombinedMOLS.html">moeoCombinedMOLS< EOT ></a></td><td class="indexvalue">This class allows to embed a set of local searches that are sequentially applied, and so working and updating the same archive of non-dominated solutions </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoContributionMetric.html">moeoContributionMetric< EOT ></a></td><td class="indexvalue">The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoDisctinctElitistReplacement.html">moeoDisctinctElitistReplacement< EOT, WorthT ></a></td><td class="indexvalue">Same than <a class="el" href="classmoeoElitistReplacement.html">moeoElitistReplacement</a> except that distinct individuals are privilegied </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoElitistReplacement.html">moeoElitistReplacement< EOT, WorthT ></a></td><td class="indexvalue">Keep all the best individuals (almost cut-and-pasted from <a class="elRef" doxygen="eo.doxytag:../../../paradiseo-eo/doc/html/" href="../../../paradiseo-eo/doc/html/classeo_n_d_plus_replacement.html">eoNDPlusReplacement</a>, (c) Maarten Keijzer, Marc Schoenauer and GeNeura Team, 2002) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEntropyMetric.html">moeoEntropyMetric< EOT ></a></td><td class="indexvalue">The entropy gives an idea of the diversity of a Pareto set relatively to another Pareto set </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoHybridMOLS.html">moeoHybridMOLS< EOT ></a></td><td class="indexvalue">This class allows to apply a multi-objective local search to a number of selected individuals contained in the archive at every generation until a stopping criteria is verified </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoIBEA.html">moeoIBEA< EOT, Fitness ></a></td><td class="indexvalue">Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoIBEAAvgSorting.html">moeoIBEAAvgSorting< EOT, FitnessEval ></a></td><td class="indexvalue">Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty using averaged values for each objective Follow the idea presented in the Deb & Gupta paper "Searching for Robust Pareto-Optimal Solutions in Multi-Objective Optimization", 2005 Of course, the fitness of an individual needs to be an eoStochasticParetoFitness object </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoIBEASorting.html">moeoIBEASorting< EOT, Fitness ></a></td><td class="indexvalue">Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) without uncertainty Adapted from the Zitzler and Künzli paper "Indicator-Based Selection in Multiobjective Search" (2004) Of course, Fitness needs to be an <a class="elRef" doxygen="eo.doxytag:../../../paradiseo-eo/doc/html/" href="../../../paradiseo-eo/doc/html/classeo_pareto_fitness.html">eoParetoFitness</a> object </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoIBEAStochSorting.html">moeoIBEAStochSorting< EOT, FitnessEval ></a></td><td class="indexvalue">Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty Adapted from the Basseur and Zitzler paper "Handling Uncertainty in Indicator-Based Multiobjective Optimization" (2006) Of course, the fitness of an individual needs to be an eoStochasticParetoFitness object </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoMetric.html">moeoMetric</a></td><td class="indexvalue">Base class for performance metrics (also called quality indicators) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoMOLS.html">moeoMOLS< EOT ></a></td><td class="indexvalue">Abstract class for local searches applied to multi-objective optimization </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoNDSorting__II.html">moeoNDSorting_II< EOT ></a></td><td class="indexvalue">Fast Elitist Non-Dominant Sorting Genetic Algorithm assignment strategie Note : This is a corrected version of the original <a class="elRef" doxygen="eo.doxytag:../../../paradiseo-eo/doc/html/" href="../../../paradiseo-eo/doc/html/classeo_n_d_sorting___i_i.html">eoNDSorting_II</a> class </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoNDSorting__II_1_1compare__nodes.html">moeoNDSorting_II< EOT >::compare_nodes</a></td><td class="indexvalue">A class to compare the nodes </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoNSGA__II.html">moeoNSGA_II< EOT ></a></td><td class="indexvalue"></td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoParetoEuclidDist.html">moeoParetoEuclidDist< EOT, DistType ></a></td><td class="indexvalue"></td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoParetoPhenDist.html">moeoParetoPhenDist< EOT, DistType ></a></td><td class="indexvalue"></td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoParetoSharing.html">moeoParetoSharing< EOT, worthT ></a></td><td class="indexvalue"></td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoParetoSharing_1_1dMatrix.html">moeoParetoSharing< EOT, worthT >::dMatrix</a></td><td class="indexvalue"></td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoReplacement.html">moeoReplacement< EOT, WorthT ></a></td><td class="indexvalue">Replacement strategy for multi-objective optimization </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoSelectOneFromPopAndArch.html">moeoSelectOneFromPopAndArch< EOT ></a></td><td class="indexvalue">Elitist selection process that consists in choosing individuals in the archive as well as in the current population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoSolutionUM.html">moeoSolutionUM< EOT, R, EOFitness ></a></td><td class="indexvalue">Base class for unary metrics dedicated to the performance evaluation of a single solution's Pareto fitness </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoSolutionVsSolutionBM.html">moeoSolutionVsSolutionBM< EOT, R, EOFitness ></a></td><td class="indexvalue">Base class for binary metrics dedicated to the performance comparison between two solutions's Pareto fitnesses </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoUM.html">moeoUM< A, R ></a></td><td class="indexvalue">Base class for unary metrics </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoVectorUM.html">moeoVectorUM< EOT, R, EOFitness ></a></td><td class="indexvalue">Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of Pareto fitnesses) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoVectorVsSolutionBM.html">moeoVectorVsSolutionBM< EOT, R, EOFitness ></a></td><td class="indexvalue">Base class for binary metrics dedicated to the performance comparison between a Pareto set (a vector of Pareto fitnesses) and a single solution's Pareto fitness </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoVectorVsVectorBM.html">moeoVectorVsVectorBM< EOT, R, EOFitness ></a></td><td class="indexvalue">Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of Pareto fitnesses) </td></tr>
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