b0351ae298
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@157 331e1502-861f-0410-8da2-ba01fb791d7f
103 lines
5.5 KiB
TeX
103 lines
5.5 KiB
TeX
\section{moeo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$ Class Template Reference}
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\label{classmoeoIBEAAvgSorting}\index{moeoIBEAAvgSorting@{moeoIBEAAvgSorting}}
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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.
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{\tt \#include $<$moeo\-IBEA.h$>$}
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Inheritance diagram for moeo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$::\begin{figure}[H]
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\begin{center}
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\leavevmode
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\includegraphics[height=6cm]{classmoeoIBEAAvgSorting}
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\end{center}
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\end{figure}
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\subsection*{Public Member Functions}
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\begin{CompactItemize}
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\item
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{\bf moeo\-IBEAAvg\-Sorting} ({\bf moeo\-Binary\-Quality\-Indicator}$<$ Fitness\-Eval $>$ $\ast$\_\-I, const double \_\-kappa)
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\begin{CompactList}\small\item\em constructor \item\end{CompactList}\end{CompactItemize}
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\subsection*{Private Member Functions}
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\begin{CompactItemize}
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\item
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void {\bf set\-Bounds} (const {\bf eo\-Pop}$<$ EOT $>$ \&\_\-pop)
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\begin{CompactList}\small\item\em computation and setting of the bounds for each objective \item\end{CompactList}\item
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void {\bf fitnesses} (const {\bf eo\-Pop}$<$ EOT $>$ \&\_\-pop)
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\begin{CompactList}\small\item\em computation and setting of the fitness for each individual of the population \item\end{CompactList}\end{CompactItemize}
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\subsection*{Private Attributes}
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\begin{CompactItemize}
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\item
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double {\bf kappa}\label{classmoeoIBEAAvgSorting_89375a49f85c93492b59dc8450b8a983}
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\begin{CompactList}\small\item\em scaling factor kappa \item\end{CompactList}\end{CompactItemize}
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\subsection{Detailed Description}
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\subsubsection*{template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ class moeo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$}
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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.
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Definition at line 361 of file moeo\-IBEA.h.
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\subsection{Constructor \& Destructor Documentation}
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\index{moeoIBEAAvgSorting@{moeo\-IBEAAvg\-Sorting}!moeoIBEAAvgSorting@{moeoIBEAAvgSorting}}
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\index{moeoIBEAAvgSorting@{moeoIBEAAvgSorting}!moeoIBEAAvgSorting@{moeo\-IBEAAvg\-Sorting}}
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\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ {\bf moeo\-IBEAAvg\-Sorting}$<$ EOT, Fitness\-Eval $>$::{\bf moeo\-IBEAAvg\-Sorting} ({\bf moeo\-Binary\-Quality\-Indicator}$<$ Fitness\-Eval $>$ $\ast$ {\em \_\-I}, const double {\em \_\-kappa})\hspace{0.3cm}{\tt [inline]}}\label{classmoeoIBEAAvgSorting_02056e5794eb5c1d0e3d9d1cbb347c41}
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constructor
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\begin{Desc}
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\item[Parameters:]
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\begin{description}
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\item[{\em eo\-Binary\-Quality\-Indicator$<$EOT$>$$\ast$}]\_\-I the binary quality indicator to use in the selection process \item[{\em double}]\_\-kappa scaling factor kappa \end{description}
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\end{Desc}
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Definition at line 373 of file moeo\-IBEA.h.
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References moeo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$::kappa.
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\subsection{Member Function Documentation}
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\index{moeoIBEAAvgSorting@{moeo\-IBEAAvg\-Sorting}!setBounds@{setBounds}}
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\index{setBounds@{setBounds}!moeoIBEAAvgSorting@{moeo\-IBEAAvg\-Sorting}}
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\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ void {\bf moeo\-IBEAAvg\-Sorting}$<$ EOT, Fitness\-Eval $>$::set\-Bounds (const {\bf eo\-Pop}$<$ EOT $>$ \& {\em \_\-pop})\hspace{0.3cm}{\tt [inline, private, virtual]}}\label{classmoeoIBEAAvgSorting_b62fcfda9ac75352479fa06952754f90}
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computation and setting of the bounds for each objective
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\begin{Desc}
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\item[Parameters:]
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\begin{description}
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\item[{\em const}]eo\-Pop$<$EOT$>$\& \_\-pop the population \end{description}
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\end{Desc}
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Implements {\bf moeo\-IBEA$<$ EOT, Fitness\-Eval $>$} \doxyref{}{p.}{classmoeoIBEA}.
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Definition at line 398 of file moeo\-IBEA.h.
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References moeo\-IBEA$<$ EOT, Fitness\-Eval $>$::I, and moeo\-Binary\-Quality\-Indicator$<$ EOFitness $>$::set\-Bounds().\index{moeoIBEAAvgSorting@{moeo\-IBEAAvg\-Sorting}!fitnesses@{fitnesses}}
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\index{fitnesses@{fitnesses}!moeoIBEAAvgSorting@{moeo\-IBEAAvg\-Sorting}}
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\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ void {\bf moeo\-IBEAAvg\-Sorting}$<$ EOT, Fitness\-Eval $>$::fitnesses (const {\bf eo\-Pop}$<$ EOT $>$ \& {\em \_\-pop})\hspace{0.3cm}{\tt [inline, private, virtual]}}\label{classmoeoIBEAAvgSorting_e88f9280e5c81cd0b54d738b7863dc1d}
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computation and setting of the fitness for each individual of the population
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\begin{Desc}
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\item[Parameters:]
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\begin{description}
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\item[{\em const}]eo\-Pop$<$EOT$>$\& \_\-pop the population \end{description}
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\end{Desc}
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Implements {\bf moeo\-IBEA$<$ EOT, Fitness\-Eval $>$} \doxyref{}{p.}{classmoeoIBEA}.
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Definition at line 431 of file moeo\-IBEA.h.
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References moeo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$::kappa, and eo\-Value\-Param$<$ std::vector$<$ Worth\-T $>$ $>$::value().
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The documentation for this class was generated from the following file:\begin{CompactItemize}
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\item
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moeo\-IBEA.h\end{CompactItemize}
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