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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="classMOEO.html">MOEO< MOEOObjectiveVector, MOEOFitness, MOEODiversity ></a></td><td class="indexvalue">Base class allowing to represent a solution (an individual) for multi-objective optimization </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoAdditiveEpsilonBinaryMetric.html">moeoAdditiveEpsilonBinaryMetric< ObjectiveVector ></a></td><td class="indexvalue">Additive epsilon binary metric allowing to compare two objective vectors as proposed in Zitzler E., Thiele L., Laumanns M., Fonseca C </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoArchive.html">moeoArchive< MOEOT ></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="classmoeoArchiveObjectiveVectorSavingUpdater.html">moeoArchiveObjectiveVectorSavingUpdater< 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< MOEOT ></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="classmoeoBinaryMetric.html">moeoBinaryMetric< 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="classmoeoBinaryMetricSavingUpdater.html">moeoBinaryMetricSavingUpdater< MOEOT ></a></td><td class="indexvalue">This class allows to save the progression of a binary metric comparing the objective vectors of the current population (or archive) with the objective vectors 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="classmoeoBitVector.html">moeoBitVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity ></a></td><td class="indexvalue">This class is an implementationeo of a simple bit-valued <a class="el" href="classmoeoVector.html">moeoVector</a> </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoCombinedLS.html">moeoCombinedLS< MOEOT, Type ></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="classmoeoComparator.html">moeoComparator< MOEOT ></a></td><td class="indexvalue">Functor allowing to compare two solutions </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoContributionMetric.html">moeoContributionMetric< ObjectiveVector ></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 (Meunier, Talbi, Reininger: 'A multiobjective genetic algorithm for radio network optimization', in Proc </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoConvertPopToObjectiveVectors.html">moeoConvertPopToObjectiveVectors< MOEOT, ObjectiveVector ></a></td><td class="indexvalue">Functor allowing to get a vector of objective vectors from a population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoCriterionBasedFitnessAssignment.html">moeoCriterionBasedFitnessAssignment< MOEOT ></a></td><td class="indexvalue">MoeoCriterionBasedFitnessAssignment is a <a class="el" href="classmoeoFitnessAssignment.html">moeoFitnessAssignment</a> for criterion-based strategies </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoCrowdingDistanceDiversityAssignment.html">moeoCrowdingDistanceDiversityAssignment< MOEOT ></a></td><td class="indexvalue">Diversity assignment sheme based on crowding distance proposed in: K </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoDetTournamentSelect.html">moeoDetTournamentSelect< MOEOT ></a></td><td class="indexvalue">Selection strategy that selects ONE individual by deterministic tournament </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoDiversityAssignment.html">moeoDiversityAssignment< MOEOT ></a></td><td class="indexvalue">Functor that sets the diversity values of a whole population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoDiversityThenFitnessComparator.html">moeoDiversityThenFitnessComparator< MOEOT ></a></td><td class="indexvalue">Functor allowing to compare two solutions according to their diversity values, then according to their fitness values </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoDummyDiversityAssignment.html">moeoDummyDiversityAssignment< MOEOT ></a></td><td class="indexvalue">MoeoDummyDiversityAssignment is a <a class="el" href="classmoeoDiversityAssignment.html">moeoDiversityAssignment</a> that gives the value '0' as the individual's diversity for a whole population if it is invalid </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoDummyFitnessAssignment.html">moeoDummyFitnessAssignment< MOEOT ></a></td><td class="indexvalue">MoeoDummyFitnessAssignment is a <a class="el" href="classmoeoFitnessAssignment.html">moeoFitnessAssignment</a> that gives the value '0' as the individual's fitness for a whole population if it is invalid </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEA.html">moeoEA< MOEOT ></a></td><td class="indexvalue">Abstract class for multi-objective evolutionary algorithms </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEasyEA.html">moeoEasyEA< MOEOT ></a></td><td class="indexvalue">An easy class to design multi-objective evolutionary algorithms </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoElitistReplacement.html">moeoElitistReplacement< MOEOT ></a></td><td class="indexvalue">Elitist replacement strategy that consists in keeping the N best individuals </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoElitistReplacement_1_1Cmp.html">moeoElitistReplacement< MOEOT >::Cmp</a></td><td class="indexvalue">This class is used to compare solutions in order to sort the population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEntropyMetric.html">moeoEntropyMetric< ObjectiveVector ></a></td><td class="indexvalue">The entropy gives an idea of the diversity of a Pareto set relatively to another (Basseur, Seynhaeve, Talbi: 'Design of Multi-objective Evolutionary Algorithms: Application to the Flow-shop Scheduling Problem', in Proc </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEnvironmentalReplacement.html">moeoEnvironmentalReplacement< MOEOT ></a></td><td class="indexvalue">Environmental replacement strategy that consists in keeping the N best individuals by deleting individuals 1 by 1 and by updating the fitness and diversity values after each deletion </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEnvironmentalReplacement_1_1Cmp.html">moeoEnvironmentalReplacement< MOEOT >::Cmp</a></td><td class="indexvalue">This class is used to compare solutions in order to sort the population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoEvalFunc.html">moeoEvalFunc< MOEOT ></a></td><td class="indexvalue"></td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoFastNonDominatedSortingFitnessAssignment.html">moeoFastNonDominatedSortingFitnessAssignment< MOEOT ></a></td><td class="indexvalue">Fitness assignment sheme based on Pareto-dominance count proposed in: N </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoFitnessAssignment.html">moeoFitnessAssignment< MOEOT ></a></td><td class="indexvalue">Functor that sets the fitness values of a whole population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoFitnessThenDiversityComparator.html">moeoFitnessThenDiversityComparator< MOEOT ></a></td><td class="indexvalue">Functor allowing to compare two solutions according to their fitness values, then according to their diversity values </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoGDominanceObjectiveVectorComparator.html">moeoGDominanceObjectiveVectorComparator< ObjectiveVector ></a></td><td class="indexvalue">This functor class allows to compare 2 objective vectors according to g-dominance </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoGenerationalReplacement.html">moeoGenerationalReplacement< MOEOT ></a></td><td class="indexvalue">Generational replacement: only the new individuals are preserved </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoHybridLS.html">moeoHybridLS< MOEOT ></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="classmoeoHypervolumeBinaryMetric.html">moeoHypervolumeBinaryMetric< ObjectiveVector ></a></td><td class="indexvalue">Hypervolume binary metric allowing to compare two objective vectors as proposed in Zitzler E., Künzli S </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoIndicatorBasedFitnessAssignment.html">moeoIndicatorBasedFitnessAssignment< MOEOT ></a></td><td class="indexvalue">Fitness assignment sheme based an Indicator proposed in: E </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoLS.html">moeoLS< MOEOT, Type ></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="classmoeoMetric.html">moeoMetric</a></td><td class="indexvalue">Base class for performance metrics (also known as quality indicators) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoNormalizedSolutionVsSolutionBinaryMetric.html">moeoNormalizedSolutionVsSolutionBinaryMetric< ObjectiveVector, R ></a></td><td class="indexvalue">Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoNSGAII.html">moeoNSGAII< MOEOT ></a></td><td class="indexvalue">The NSGA-II algorithm as described in: Deb, K., S </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoObjectiveComparator.html">moeoObjectiveComparator< MOEOT ></a></td><td class="indexvalue">Functor allowing to compare two solutions according to their first objective value, then their second, and so on </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoObjectiveVector.html">moeoObjectiveVector< ObjectiveVectorTraits ></a></td><td class="indexvalue">Abstract class allowing to represent a solution in the objective space (phenotypic representation) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoObjectiveVectorComparator.html">moeoObjectiveVectorComparator< ObjectiveVector ></a></td><td class="indexvalue">Abstract class allowing to compare 2 objective vectors </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoObjectiveVectorDouble.html">moeoObjectiveVectorDouble< ObjectiveVectorTraits ></a></td><td class="indexvalue">This class allows to represent a solution in the objective space (phenotypic representation) by a std::vector of doubles, i.e </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoObjectiveVectorTraits.html">moeoObjectiveVectorTraits</a></td><td class="indexvalue">A traits class for <a class="el" href="classmoeoObjectiveVector.html">moeoObjectiveVector</a> to specify the number of objectives and which ones have to be minimized or maximized </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoOneObjectiveComparator.html">moeoOneObjectiveComparator< MOEOT ></a></td><td class="indexvalue">Functor allowing to compare two solutions according to one objective </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoParetoBasedFitnessAssignment.html">moeoParetoBasedFitnessAssignment< MOEOT ></a></td><td class="indexvalue">MoeoParetoBasedFitnessAssignment is a <a class="el" href="classmoeoFitnessAssignment.html">moeoFitnessAssignment</a> for Pareto-based strategies </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoParetoObjectiveVectorComparator.html">moeoParetoObjectiveVectorComparator< ObjectiveVector ></a></td><td class="indexvalue">This functor class allows to compare 2 objective vectors according to Pareto dominance </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoRandomSelect.html">moeoRandomSelect< MOEOT ></a></td><td class="indexvalue">Selection strategy that selects only one element randomly from a whole population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoRealVector.html">moeoRealVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity ></a></td><td class="indexvalue">This class is an implementationeo of a simple double-valued <a class="el" href="classmoeoVector.html">moeoVector</a> </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoReplacement.html">moeoReplacement< MOEOT ></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="classmoeoRouletteSelect.html">moeoRouletteSelect< MOEOT ></a></td><td class="indexvalue">Selection strategy that selects ONE individual by using roulette wheel process </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoScalarFitnessAssignment.html">moeoScalarFitnessAssignment< MOEOT ></a></td><td class="indexvalue">MoeoScalarFitnessAssignment is a <a class="el" href="classmoeoFitnessAssignment.html">moeoFitnessAssignment</a> for scalar strategies </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoSelectFromPopAndArch.html">moeoSelectFromPopAndArch< MOEOT ></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="classmoeoSelectOne.html">moeoSelectOne< MOEOT ></a></td><td class="indexvalue">Selection strategy for multi-objective optimization that selects only one element from a whole population </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoSolutionUnaryMetric.html">moeoSolutionUnaryMetric< ObjectiveVector, R ></a></td><td class="indexvalue">Base class for unary metrics dedicated to the performance evaluation of a single solution's objective vector </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoSolutionVsSolutionBinaryMetric.html">moeoSolutionVsSolutionBinaryMetric< ObjectiveVector, R ></a></td><td class="indexvalue">Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoStochTournamentSelect.html">moeoStochTournamentSelect< MOEOT ></a></td><td class="indexvalue">Selection strategy that selects ONE individual by stochastic tournament </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoUnaryMetric.html">moeoUnaryMetric< 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="classmoeoVector.html">moeoVector< MOEOObjectiveVector, MOEOFitness, MOEODiversity, GeneType ></a></td><td class="indexvalue">Base class for fixed length chromosomes, just derives from <a class="el" href="classMOEO.html">MOEO</a> and std::vector and redirects the smaller than operator to MOEO (objective vector based comparison) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoVectorUnaryMetric.html">moeoVectorUnaryMetric< ObjectiveVector, R ></a></td><td class="indexvalue">Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of objective vectors) </td></tr>
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<tr><td class="indexkey"><a class="el" href="classmoeoVectorVsVectorBinaryMetric.html">moeoVectorVsVectorBinaryMetric< ObjectiveVector, R ></a></td><td class="indexvalue">Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of objective vectors) </td></tr>
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