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\section{Paradis\-EO-MOEO Class List}
Here are the classes, structs, unions and interfaces with brief descriptions:\begin{CompactList}
\item\contentsline{section}{{\bf moeo\-Additive\-Binary\-Epsilon\-Indicator$<$ EOFitness $>$} (Functor Additive binary epsilon indicator for {\bf eo\-Pareto\-Fitness} )}{\pageref{classmoeoAdditiveBinaryEpsilonIndicator}}{}
\item\contentsline{section}{{\bf moeo\-Archive$<$ EOT $>$} (An archive is a secondary population that stores non-dominated solutions )}{\pageref{classmoeoArchive}}{}
\item\contentsline{section}{{\bf moeo\-Archive\-Fitness\-Saving\-Updater$<$ EOT $>$} (This class allows to save the fitnesses of solutions contained in an archive into a file at each generation )}{\pageref{classmoeoArchiveFitnessSavingUpdater}}{}
\item\contentsline{section}{{\bf moeo\-Archive\-Updater$<$ EOT $>$} (This class allows to update the archive at each generation with newly found non-dominated solutions )}{\pageref{classmoeoArchiveUpdater}}{}
\item\contentsline{section}{{\bf moeo\-Binary\-Hypervolume\-Indicator$<$ EOFitness $>$} (Functor Binary hypervolume indicator for {\bf eo\-Pareto\-Fitness} )}{\pageref{classmoeoBinaryHypervolumeIndicator}}{}
\item\contentsline{section}{{\bf moeo\-Binary\-Metric\-Saving\-Updater$<$ EOT $>$} (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 )}{\pageref{classmoeoBinaryMetricSavingUpdater}}{}
\item\contentsline{section}{{\bf moeo\-Binary\-Quality\-Indicator$<$ EOFitness $>$} (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 {\bf eo\-Pareto\-Fitness} object )}{\pageref{classmoeoBinaryQualityIndicator}}{}
\item\contentsline{section}{{\bf moeo\-Binary\-Quality\-Indicator$<$ EOFitness $>$::Range} (Private class to represent the bounds )}{\pageref{classmoeoBinaryQualityIndicator_1_1Range}}{}
\item\contentsline{section}{{\bf moeo\-BM$<$ A1, A2, R $>$} (Base class for binary metrics )}{\pageref{classmoeoBM}}{}
\item\contentsline{section}{{\bf moeo\-Combined\-MOLS$<$ EOT $>$} (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{classmoeoCombinedMOLS}}{}
\item\contentsline{section}{{\bf moeo\-Contribution\-Metric$<$ EOT $>$} (The contribution metric evaluates the proportion of non-dominated solutions given by a Pareto set relatively to another Pareto set )}{\pageref{classmoeoContributionMetric}}{}
\item\contentsline{section}{{\bf moeo\-Disctinct\-Elitist\-Replacement$<$ EOT, Worth\-T $>$} (Same than \doxyref{moeo\-Elitist\-Replacement}{p.}{classmoeoElitistReplacement} except that distinct individuals are privilegied )}{\pageref{classmoeoDisctinctElitistReplacement}}{}
\item\contentsline{section}{{\bf moeo\-Elitist\-Replacement$<$ EOT, Worth\-T $>$} (Keep all the best individuals (almost cut-and-pasted from {\bf eo\-NDPlus\-Replacement}, (c) Maarten Keijzer, Marc Schoenauer and Ge\-Neura Team, 2002) )}{\pageref{classmoeoElitistReplacement}}{}
\item\contentsline{section}{{\bf moeo\-Entropy\-Metric$<$ EOT $>$} (The entropy gives an idea of the diversity of a Pareto set relatively to another Pareto set )}{\pageref{classmoeoEntropyMetric}}{}
\item\contentsline{section}{{\bf moeo\-Hybrid\-MOLS$<$ EOT $>$} (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{classmoeoHybridMOLS}}{}
\item\contentsline{section}{{\bf moeo\-IBEA$<$ EOT, Fitness $>$} (Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) )}{\pageref{classmoeoIBEA}}{}
\item\contentsline{section}{{\bf moeo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$} (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 \char`\"{}Searching for Robust Pareto-Optimal Solutions in Multi-Objective Optimization\char`\"{}, 2005 Of course, the fitness of an individual needs to be an eo\-Stochastic\-Pareto\-Fitness object )}{\pageref{classmoeoIBEAAvgSorting}}{}
\item\contentsline{section}{{\bf moeo\-IBEASorting$<$ EOT, Fitness $>$} (Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) without uncertainty Adapted from the Zitzler and K\~{A}¼nzli paper \char`\"{}Indicator-Based Selection in Multiobjective Search\char`\"{} (2004) Of course, Fitness needs to be an {\bf eo\-Pareto\-Fitness} object )}{\pageref{classmoeoIBEASorting}}{}
\item\contentsline{section}{{\bf moeo\-IBEAStoch\-Sorting$<$ EOT, Fitness\-Eval $>$} (Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty Adapted from the Basseur and Zitzler paper \char`\"{}Handling Uncertainty in Indicator-Based Multiobjective Optimization\char`\"{} (2006) Of course, the fitness of an individual needs to be an eo\-Stochastic\-Pareto\-Fitness object )}{\pageref{classmoeoIBEAStochSorting}}{}
\item\contentsline{section}{{\bf moeo\-Metric} (Base class for performance metrics (also called quality indicators) )}{\pageref{classmoeoMetric}}{}
\item\contentsline{section}{{\bf moeo\-MOLS$<$ EOT $>$} (Abstract class for local searches applied to multi-objective optimization )}{\pageref{classmoeoMOLS}}{}
\item\contentsline{section}{{\bf moeo\-NDSorting\_\-II$<$ EOT $>$} (Fast Elitist Non-Dominant Sorting Genetic Algorithm assignment strategie Note : This is a corrected version of the original {\bf eo\-NDSorting\_\-II} class )}{\pageref{classmoeoNDSorting__II}}{}
\item\contentsline{section}{{\bf moeo\-NDSorting\_\-II$<$ EOT $>$::compare\_\-nodes} (A class to compare the nodes )}{\pageref{classmoeoNDSorting__II_1_1compare__nodes}}{}
\item\contentsline{section}{{\bf moeo\-NSGA\_\-II$<$ EOT $>$} }{\pageref{classmoeoNSGA__II}}{}
\item\contentsline{section}{{\bf moeo\-Pareto\-Euclid\-Dist$<$ EOT, Dist\-Type $>$} }{\pageref{classmoeoParetoEuclidDist}}{}
\item\contentsline{section}{{\bf moeo\-Pareto\-Phen\-Dist$<$ EOT, Dist\-Type $>$} }{\pageref{classmoeoParetoPhenDist}}{}
\item\contentsline{section}{{\bf moeo\-Pareto\-Sharing$<$ EOT, worth\-T $>$} }{\pageref{classmoeoParetoSharing}}{}
\item\contentsline{section}{{\bf moeo\-Pareto\-Sharing$<$ EOT, worth\-T $>$::d\-Matrix} }{\pageref{classmoeoParetoSharing_1_1dMatrix}}{}
\item\contentsline{section}{{\bf moeo\-Replacement$<$ EOT, Worth\-T $>$} (Replacement strategy for multi-objective optimization )}{\pageref{classmoeoReplacement}}{}
\item\contentsline{section}{{\bf moeo\-Select\-One\-From\-Pop\-And\-Arch$<$ EOT $>$} (Elitist selection process that consists in choosing individuals in the archive as well as in the current population )}{\pageref{classmoeoSelectOneFromPopAndArch}}{}
\item\contentsline{section}{{\bf moeo\-Solution\-UM$<$ EOT, R, EOFitness $>$} (Base class for unary metrics dedicated to the performance evaluation of a single solution's Pareto fitness )}{\pageref{classmoeoSolutionUM}}{}
\item\contentsline{section}{{\bf moeo\-Solution\-Vs\-Solution\-BM$<$ EOT, R, EOFitness $>$} (Base class for binary metrics dedicated to the performance comparison between two solutions's Pareto fitnesses )}{\pageref{classmoeoSolutionVsSolutionBM}}{}
\item\contentsline{section}{{\bf moeo\-UM$<$ A, R $>$} (Base class for unary metrics )}{\pageref{classmoeoUM}}{}
\item\contentsline{section}{{\bf moeo\-Vector\-UM$<$ EOT, R, EOFitness $>$} (Base class for unary metrics dedicated to the performance evaluation of a Pareto set (a vector of Pareto fitnesses) )}{\pageref{classmoeoVectorUM}}{}
\item\contentsline{section}{{\bf moeo\-Vector\-Vs\-Solution\-BM$<$ EOT, R, EOFitness $>$} (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 )}{\pageref{classmoeoVectorVsSolutionBM}}{}
\item\contentsline{section}{{\bf moeo\-Vector\-Vs\-Vector\-BM$<$ EOT, R, EOFitness $>$} (Base class for binary metrics dedicated to the performance comparison between two Pareto sets (two vectors of Pareto fitnesses) )}{\pageref{classmoeoVectorVsVectorBM}}{}
\end{CompactList}