\section{Paradis\-EO-MOEO Class List}
Here are the classes, structs, unions and interfaces with brief descriptions:\begin{CompactList}
\item\contentsline{section}{\bf{MOEO$<$ MOEOObjective\-Vector, MOEOFitness, MOEODiversity $>$} (Base class allowing to represent a solution (an individual) for multi-objective optimization )}{\pageref{classMOEO}}{}
\item\contentsline{section}{\bf{moeo\-Achievement\-Fitness\-Assignment$<$ MOEOT $>$} (Fitness assignment sheme based on the achievement scalarizing function propozed by Wiersbicki (1980) )}{\pageref{classmoeoAchievementFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Additive\-Epsilon\-Binary\-Metric$<$ Objective\-Vector $>$} (Additive epsilon binary metric allowing to compare two objective vectors as proposed in Zitzler E., Thiele L., Laumanns M., Fonseca C )}{\pageref{classmoeoAdditiveEpsilonBinaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-Aggregative\-Comparator$<$ MOEOT $>$} (Functor allowing to compare two solutions according to their fitness and diversity values, each according to its aggregative value )}{\pageref{classmoeoAggregativeComparator}}{}
\item\contentsline{section}{\bf{moeo\-Algo} (Abstract class for multi-objective algorithms )}{\pageref{classmoeoAlgo}}{}
\item\contentsline{section}{\bf{moeo\-Archive$<$ MOEOT $>$} (An archive is a secondary population that stores non-dominated solutions )}{\pageref{classmoeoArchive}}{}
\item\contentsline{section}{\bf{moeo\-Archive\-Objective\-Vector\-Saving\-Updater$<$ MOEOT $>$} (This class allows to save the objective vectors of the solutions contained in an archive into a file at each generation )}{\pageref{classmoeoArchiveObjectiveVectorSavingUpdater}}{}
\item\contentsline{section}{\bf{moeo\-Archive\-Updater$<$ MOEOT $>$} (This class allows to update the archive at each generation with newly found non-dominated solutions )}{\pageref{classmoeoArchiveUpdater}}{}
\item\contentsline{section}{\bf{moeo\-Binary\-Indicator\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Indicator\-Based\-Fitness\-Assignment for binary indicators )}{\pageref{classmoeoBinaryIndicatorBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Binary\-Metric$<$ A1, A2, R $>$} (Base class for binary metrics )}{\pageref{classmoeoBinaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-Binary\-Metric\-Saving\-Updater$<$ MOEOT $>$} (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 )}{\pageref{classmoeoBinaryMetricSavingUpdater}}{}
\item\contentsline{section}{\bf{moeo\-Bit\-Vector$<$ MOEOObjective\-Vector, MOEOFitness, MOEODiversity $>$} (This class is an implementationeo of a simple bit-valued \doxyref{moeo\-Vector}{p.}{classmoeoVector} )}{\pageref{classmoeoBitVector}}{}
\item\contentsline{section}{\bf{moeo\-Combined\-LS$<$ MOEOT, Type $>$} (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 )}{\pageref{classmoeoCombinedLS}}{}
\item\contentsline{section}{\bf{moeo\-Comparator$<$ MOEOT $>$} (Functor allowing to compare two solutions )}{\pageref{classmoeoComparator}}{}
\item\contentsline{section}{\bf{moeo\-Contribution\-Metric$<$ Objective\-Vector $>$} (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 )}{\pageref{classmoeoContributionMetric}}{}
\item\contentsline{section}{\bf{moeo\-Convert\-Pop\-To\-Objective\-Vectors$<$ MOEOT, Objective\-Vector $>$} (Functor allowing to get a vector of objective vectors from a population )}{\pageref{classmoeoConvertPopToObjectiveVectors}}{}
\item\contentsline{section}{\bf{moeo\-Criterion\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Criterion\-Based\-Fitness\-Assignment is a \doxyref{moeo\-Fitness\-Assignment}{p.}{classmoeoFitnessAssignment} for criterion-based strategies )}{\pageref{classmoeoCriterionBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Crowding\-Diversity\-Assignment$<$ MOEOT $>$} (Diversity assignment sheme based on crowding proposed in: K )}{\pageref{classmoeoCrowdingDiversityAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Det\-Tournament\-Select$<$ MOEOT $>$} (Selection strategy that selects ONE individual by deterministic tournament )}{\pageref{classmoeoDetTournamentSelect}}{}
\item\contentsline{section}{\bf{moeo\-Distance$<$ MOEOT, Type $>$} (The base class for distance computation )}{\pageref{classmoeoDistance}}{}
\item\contentsline{section}{\bf{moeo\-Distance\-Matrix$<$ MOEOT, Type $>$} (A matrix to compute distances between every pair of individuals contained in a population )}{\pageref{classmoeoDistanceMatrix}}{}
\item\contentsline{section}{\bf{moeo\-Diversity\-Assignment$<$ MOEOT $>$} (Functor that sets the diversity values of a whole population )}{\pageref{classmoeoDiversityAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Diversity\-Then\-Fitness\-Comparator$<$ MOEOT $>$} (Functor allowing to compare two solutions according to their diversity values, then according to their fitness values )}{\pageref{classmoeoDiversityThenFitnessComparator}}{}
\item\contentsline{section}{\bf{moeo\-Dummy\-Diversity\-Assignment$<$ MOEOT $>$} (Moeo\-Dummy\-Diversity\-Assignment is a \doxyref{moeo\-Diversity\-Assignment}{p.}{classmoeoDiversityAssignment} that gives the value '0' as the individual's diversity for a whole population if it is invalid )}{\pageref{classmoeoDummyDiversityAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Dummy\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Dummy\-Fitness\-Assignment is a \doxyref{moeo\-Fitness\-Assignment}{p.}{classmoeoFitnessAssignment} that gives the value '0' as the individual's fitness for a whole population if it is invalid )}{\pageref{classmoeoDummyFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-EA$<$ MOEOT $>$} (Abstract class for multi-objective evolutionary algorithms )}{\pageref{classmoeoEA}}{}
\item\contentsline{section}{\bf{moeo\-Easy\-EA$<$ MOEOT $>$} (An easy class to design multi-objective evolutionary algorithms )}{\pageref{classmoeoEasyEA}}{}
\item\contentsline{section}{\bf{moeo\-Easy\-EA$<$ MOEOT $>$::eo\-Dummy\-Eval} (\doxyref{Dummy} eval )}{\pageref{classmoeoEasyEA_1_1eoDummyEval}}{}
\item\contentsline{section}{\bf{moeo\-Easy\-EA$<$ MOEOT $>$::eo\-Dummy\-Select} (\doxyref{Dummy} select )}{\pageref{classmoeoEasyEA_1_1eoDummySelect}}{}
\item\contentsline{section}{\bf{moeo\-Easy\-EA$<$ MOEOT $>$::eo\-Dummy\-Transform} (\doxyref{Dummy} transform )}{\pageref{classmoeoEasyEA_1_1eoDummyTransform}}{}
\item\contentsline{section}{\bf{moeo\-Elitist\-Replacement$<$ MOEOT $>$} (Elitist replacement strategy that consists in keeping the N best individuals )}{\pageref{classmoeoElitistReplacement}}{}
\item\contentsline{section}{\bf{moeo\-Elitist\-Replacement$<$ MOEOT $>$::Cmp} (This object is used to compare solutions in order to sort the population )}{\pageref{classmoeoElitistReplacement_1_1Cmp}}{}
\item\contentsline{section}{\bf{moeo\-Entropy\-Metric$<$ Objective\-Vector $>$} (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 )}{\pageref{classmoeoEntropyMetric}}{}
\item\contentsline{section}{\bf{moeo\-Environmental\-Replacement$<$ MOEOT $>$} (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 )}{\pageref{classmoeoEnvironmentalReplacement}}{}
\item\contentsline{section}{\bf{moeo\-Environmental\-Replacement$<$ MOEOT $>$::Cmp} (This object is used to compare solutions in order to sort the population )}{\pageref{classmoeoEnvironmentalReplacement_1_1Cmp}}{}
\item\contentsline{section}{\bf{moeo\-Euclidean\-Distance$<$ MOEOT $>$} (A class allowing to compute an euclidian distance between two solutions in the objective space with normalized objective values (i.e )}{\pageref{classmoeoEuclideanDistance}}{}
\item\contentsline{section}{\bf{moeo\-Eval\-Func$<$ MOEOT $>$} }{\pageref{classmoeoEvalFunc}}{}
\item\contentsline{section}{\bf{moeo\-Exp\-Binary\-Indicator\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Fitness assignment sheme based on an indicator proposed in: E )}{\pageref{classmoeoExpBinaryIndicatorBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Fast\-Non\-Dominated\-Sorting\-Fitness\-Assignment$<$ MOEOT $>$} (Fitness assignment sheme based on Pareto-dominance count proposed in: N )}{\pageref{classmoeoFastNonDominatedSortingFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Fast\-Non\-Dominated\-Sorting\-Fitness\-Assignment$<$ MOEOT $>$::Objective\-Comparator} (Functor allowing to compare two solutions according to their first objective value, then their second, and so on )}{\pageref{classmoeoFastNonDominatedSortingFitnessAssignment_1_1ObjectiveComparator}}{}
\item\contentsline{section}{\bf{moeo\-Fitness\-Assignment$<$ MOEOT $>$} (Functor that sets the fitness values of a whole population )}{\pageref{classmoeoFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Fitness\-Then\-Diversity\-Comparator$<$ MOEOT $>$} (Functor allowing to compare two solutions according to their fitness values, then according to their diversity values )}{\pageref{classmoeoFitnessThenDiversityComparator}}{}
\item\contentsline{section}{\bf{moeo\-Front\-By\-Front\-Crowding\-Diversity\-Assignment$<$ MOEOT $>$} (Diversity assignment sheme based on crowding proposed in: K )}{\pageref{classmoeoFrontByFrontCrowdingDiversityAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Front\-By\-Front\-Sharing\-Diversity\-Assignment$<$ MOEOT $>$} (Sharing assignment scheme on the way it is used in NSGA )}{\pageref{classmoeoFrontByFrontSharingDiversityAssignment}}{}
\item\contentsline{section}{\bf{moeo\-GDominance\-Objective\-Vector\-Comparator$<$ Objective\-Vector $>$} (This functor class allows to compare 2 objective vectors according to g-dominance )}{\pageref{classmoeoGDominanceObjectiveVectorComparator}}{}
\item\contentsline{section}{\bf{moeo\-Generational\-Replacement$<$ MOEOT $>$} (Generational replacement: only the new individuals are preserved )}{\pageref{classmoeoGenerationalReplacement}}{}
\item\contentsline{section}{\bf{moeo\-Hybrid\-LS$<$ MOEOT $>$} (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 )}{\pageref{classmoeoHybridLS}}{}
\item\contentsline{section}{\bf{moeo\-Hypervolume\-Binary\-Metric$<$ Objective\-Vector $>$} (Hypervolume binary metric allowing to compare two objective vectors as proposed in Zitzler E., K\~{A}žnzli S )}{\pageref{classmoeoHypervolumeBinaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-IBEA$<$ MOEOT $>$} (IBEA (Indicator-Based Evolutionary Algorithm) as described in: E )}{\pageref{classmoeoIBEA}}{}
\item\contentsline{section}{\bf{moeo\-IBMOLS$<$ MOEOT, Move $>$} (Indicator-Based Multi-Objective Local Search (IBMOLS) as described in Basseur M., Burke K )}{\pageref{classmoeoIBMOLS}}{}
\item\contentsline{section}{\bf{moeo\-Indicator\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Indicator\-Based\-Fitness\-Assignment is a \doxyref{moeo\-Fitness\-Assignment}{p.}{classmoeoFitnessAssignment} for Indicator-based strategies )}{\pageref{classmoeoIndicatorBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Iterated\-IBMOLS$<$ MOEOT, Move $>$} (Iterated version of IBMOLS as described in Basseur M., Burke K )}{\pageref{classmoeoIteratedIBMOLS}}{}
\item\contentsline{section}{\bf{moeo\-LS$<$ MOEOT, Type $>$} (Abstract class for local searches applied to multi-objective optimization )}{\pageref{classmoeoLS}}{}
\item\contentsline{section}{\bf{moeo\-Manhattan\-Distance$<$ MOEOT $>$} (A class allowing to compute the Manhattan distance between two solutions in the objective space normalized objective values (i.e )}{\pageref{classmoeoManhattanDistance}}{}
\item\contentsline{section}{\bf{moeo\-Metric} (Base class for performance metrics (also known as quality indicators) )}{\pageref{classmoeoMetric}}{}
\item\contentsline{section}{\bf{moeo\-Move\-Incr\-Eval$<$ Move $>$} }{\pageref{classmoeoMoveIncrEval}}{}
\item\contentsline{section}{\bf{moeo\-Normalized\-Distance$<$ MOEOT, Type $>$} (The base class for double distance computation with normalized objective values (i.e )}{\pageref{classmoeoNormalizedDistance}}{}
\item\contentsline{section}{\bf{moeo\-Normalized\-Solution\-Vs\-Solution\-Binary\-Metric$<$ Objective\-Vector, R $>$} (Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors using normalized values )}{\pageref{classmoeoNormalizedSolutionVsSolutionBinaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-NSGA$<$ MOEOT $>$} (NSGA (Non-dominated Sorting Genetic Algorithm) as described in: N )}{\pageref{classmoeoNSGA}}{}
\item\contentsline{section}{\bf{moeo\-NSGAII$<$ MOEOT $>$} (NSGA-II (Non-dominated Sorting Genetic Algorithm II) as described in: Deb, K., S )}{\pageref{classmoeoNSGAII}}{}
\item\contentsline{section}{\bf{moeo\-Objective\-Objective\-Vector\-Comparator$<$ Objective\-Vector $>$} (Functor allowing to compare two objective vectors according to their first objective value, then their second, and so on )}{\pageref{classmoeoObjectiveObjectiveVectorComparator}}{}
\item\contentsline{section}{\bf{moeo\-Objective\-Vector$<$ Objective\-Vector\-Traits, Objective\-Vector\-Type $>$} (Abstract class allowing to represent a solution in the objective space (phenotypic representation) )}{\pageref{classmoeoObjectiveVector}}{}
\item\contentsline{section}{\bf{moeo\-Objective\-Vector\-Comparator$<$ Objective\-Vector $>$} (Abstract class allowing to compare 2 objective vectors )}{\pageref{classmoeoObjectiveVectorComparator}}{}
\item\contentsline{section}{\bf{moeo\-Objective\-Vector\-Traits} (A traits class for \doxyref{moeo\-Objective\-Vector}{p.}{classmoeoObjectiveVector} to specify the number of objectives and which ones have to be minimized or maximized )}{\pageref{classmoeoObjectiveVectorTraits}}{}
\item\contentsline{section}{\bf{moeo\-One\-Objective\-Comparator$<$ MOEOT $>$} (Functor allowing to compare two solutions according to one objective )}{\pageref{classmoeoOneObjectiveComparator}}{}
\item\contentsline{section}{\bf{moeo\-Pareto\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Pareto\-Based\-Fitness\-Assignment is a \doxyref{moeo\-Fitness\-Assignment}{p.}{classmoeoFitnessAssignment} for Pareto-based strategies )}{\pageref{classmoeoParetoBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Pareto\-Objective\-Vector\-Comparator$<$ Objective\-Vector $>$} (This functor class allows to compare 2 objective vectors according to Pareto dominance )}{\pageref{classmoeoParetoObjectiveVectorComparator}}{}
\item\contentsline{section}{\bf{moeo\-Random\-Select$<$ MOEOT $>$} (Selection strategy that selects only one element randomly from a whole population )}{\pageref{classmoeoRandomSelect}}{}
\item\contentsline{section}{\bf{moeo\-Real\-Objective\-Vector$<$ Objective\-Vector\-Traits $>$} (This class allows to represent a solution in the objective space (phenotypic representation) by a std::vector of real values, i.e )}{\pageref{classmoeoRealObjectiveVector}}{}
\item\contentsline{section}{\bf{moeo\-Real\-Vector$<$ MOEOObjective\-Vector, MOEOFitness, MOEODiversity $>$} (This class is an implementation of a simple double-valued \doxyref{moeo\-Vector}{p.}{classmoeoVector} )}{\pageref{classmoeoRealVector}}{}
\item\contentsline{section}{\bf{moeo\-Reference\-Point\-Indicator\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Fitness assignment sheme based a Reference Point and a Quality Indicator )}{\pageref{classmoeoReferencePointIndicatorBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Replacement$<$ MOEOT $>$} (Replacement strategy for multi-objective optimization )}{\pageref{classmoeoReplacement}}{}
\item\contentsline{section}{\bf{moeo\-Roulette\-Select$<$ MOEOT $>$} (Selection strategy that selects ONE individual by using roulette wheel process )}{\pageref{classmoeoRouletteSelect}}{}
\item\contentsline{section}{\bf{moeo\-Scalar\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Scalar\-Fitness\-Assignment is a \doxyref{moeo\-Fitness\-Assignment}{p.}{classmoeoFitnessAssignment} for scalar strategies )}{\pageref{classmoeoScalarFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Select\-From\-Pop\-And\-Arch$<$ MOEOT $>$} (Elitist selection process that consists in choosing individuals in the archive as well as in the current population )}{\pageref{classmoeoSelectFromPopAndArch}}{}
\item\contentsline{section}{\bf{moeo\-Select\-One$<$ MOEOT $>$} (Selection strategy for multi-objective optimization that selects only one element from a whole population )}{\pageref{classmoeoSelectOne}}{}
\item\contentsline{section}{\bf{moeo\-Sharing\-Diversity\-Assignment$<$ MOEOT $>$} (Sharing assignment scheme originally porposed by: D )}{\pageref{classmoeoSharingDiversityAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Solution\-Unary\-Metric$<$ Objective\-Vector, R $>$} (Base class for unary metrics dedicated to the performance evaluation of a single solution's objective vector )}{\pageref{classmoeoSolutionUnaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-Solution\-Vs\-Solution\-Binary\-Metric$<$ Objective\-Vector, R $>$} (Base class for binary metrics dedicated to the performance comparison between two solutions's objective vectors )}{\pageref{classmoeoSolutionVsSolutionBinaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-Stoch\-Tournament\-Select$<$ MOEOT $>$} (Selection strategy that selects ONE individual by stochastic tournament )}{\pageref{classmoeoStochTournamentSelect}}{}
\item\contentsline{section}{\bf{moeo\-Unary\-Indicator\-Based\-Fitness\-Assignment$<$ MOEOT $>$} (Moeo\-Indicator\-Based\-Fitness\-Assignment for unary indicators )}{\pageref{classmoeoUnaryIndicatorBasedFitnessAssignment}}{}
\item\contentsline{section}{\bf{moeo\-Unary\-Metric$<$ A, R $>$} (Base class for unary metrics )}{\pageref{classmoeoUnaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-Vector$<$ MOEOObjective\-Vector, MOEOFitness, MOEODiversity, Gene\-Type $>$} (Base class for fixed length chromosomes, just derives from \doxyref{MOEO}{p.}{classMOEO} and std::vector and redirects the smaller than operator to MOEO (objective vector based comparison) )}{\pageref{classmoeoVector}}{}
\item\contentsline{section}{\bf{moeo\-Vector\-Unary\-Metric$<$ Objective\-Vector, R $>$} (Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of objective vectors) )}{\pageref{classmoeoVectorUnaryMetric}}{}
\item\contentsline{section}{\bf{moeo\-Vector\-Vs\-Vector\-Binary\-Metric$<$ Objective\-Vector, R $>$} (Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of objective vectors) )}{\pageref{classmoeoVectorVsVectorBinaryMetric}}{}
\end{CompactList}
