b0351ae298
git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@157 331e1502-861f-0410-8da2-ba01fb791d7f
99 lines
5.2 KiB
TeX
99 lines
5.2 KiB
TeX
\section{eo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$ Class Template Reference}
|
|
\label{classeoIBEAAvgSorting}\index{eoIBEAAvgSorting@{eoIBEAAvgSorting}}
|
|
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.
|
|
|
|
|
|
{\tt \#include $<$eo\-IBEA.h$>$}
|
|
|
|
Inheritance diagram for eo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$::\begin{figure}[H]
|
|
\begin{center}
|
|
\leavevmode
|
|
\includegraphics[height=6cm]{classeoIBEAAvgSorting}
|
|
\end{center}
|
|
\end{figure}
|
|
\subsection*{Public Member Functions}
|
|
\begin{CompactItemize}
|
|
\item
|
|
{\bf eo\-IBEAAvg\-Sorting} ({\bf eo\-Binary\-Quality\-Indicator}$<$ Fitness\-Eval $>$ $\ast$\_\-I, const double \_\-kappa)
|
|
\begin{CompactList}\small\item\em constructor \item\end{CompactList}\end{CompactItemize}
|
|
\subsection*{Private Member Functions}
|
|
\begin{CompactItemize}
|
|
\item
|
|
void {\bf set\-Bounds} (const {\bf eo\-Pop}$<$ EOT $>$ \&\_\-pop)
|
|
\begin{CompactList}\small\item\em computation and setting of the bounds for each objective \item\end{CompactList}\item
|
|
void {\bf fitnesses} (const {\bf eo\-Pop}$<$ EOT $>$ \&\_\-pop)
|
|
\begin{CompactList}\small\item\em computation and setting of the fitness for each individual of the population \item\end{CompactList}\end{CompactItemize}
|
|
\subsection*{Private Attributes}
|
|
\begin{CompactItemize}
|
|
\item
|
|
double {\bf kappa}\label{classeoIBEAAvgSorting_6f467d673861830d6a0708f61cecc3f7}
|
|
|
|
\begin{CompactList}\small\item\em scaling factor kappa \item\end{CompactList}\end{CompactItemize}
|
|
|
|
|
|
\subsection{Detailed Description}
|
|
\subsubsection*{template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ class eo\-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.
|
|
|
|
|
|
|
|
Definition at line 373 of file eo\-IBEA.h.
|
|
|
|
\subsection{Constructor \& Destructor Documentation}
|
|
\index{eoIBEAAvgSorting@{eo\-IBEAAvg\-Sorting}!eoIBEAAvgSorting@{eoIBEAAvgSorting}}
|
|
\index{eoIBEAAvgSorting@{eoIBEAAvgSorting}!eoIBEAAvgSorting@{eo\-IBEAAvg\-Sorting}}
|
|
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ {\bf eo\-IBEAAvg\-Sorting}$<$ EOT, Fitness\-Eval $>$::{\bf eo\-IBEAAvg\-Sorting} ({\bf eo\-Binary\-Quality\-Indicator}$<$ Fitness\-Eval $>$ $\ast$ {\em \_\-I}, const double {\em \_\-kappa})\hspace{0.3cm}{\tt [inline]}}\label{classeoIBEAAvgSorting_eb0cfda626e1e5cac6750f0598610f82}
|
|
|
|
|
|
constructor
|
|
|
|
\begin{Desc}
|
|
\item[Parameters:]
|
|
\begin{description}
|
|
\item[{\em eo\-Binary\-Quality\-Indicator$<$EOT$>$$\ast$}]\_\-I the binary quality indicator to use in the selection process \item[{\em double}]\_\-kappa scaling factor kappa \end{description}
|
|
\end{Desc}
|
|
|
|
|
|
Definition at line 385 of file eo\-IBEA.h.
|
|
|
|
References eo\-IBEAAvg\-Sorting$<$ EOT, Fitness\-Eval $>$::kappa.
|
|
|
|
\subsection{Member Function Documentation}
|
|
\index{eoIBEAAvgSorting@{eo\-IBEAAvg\-Sorting}!setBounds@{setBounds}}
|
|
\index{setBounds@{setBounds}!eoIBEAAvgSorting@{eo\-IBEAAvg\-Sorting}}
|
|
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ void {\bf eo\-IBEAAvg\-Sorting}$<$ EOT, Fitness\-Eval $>$::set\-Bounds (const {\bf eo\-Pop}$<$ EOT $>$ \& {\em \_\-pop})\hspace{0.3cm}{\tt [inline, private, virtual]}}\label{classeoIBEAAvgSorting_b365a1eab0da5211c59369416642780d}
|
|
|
|
|
|
computation and setting of the bounds for each objective
|
|
|
|
\begin{Desc}
|
|
\item[Parameters:]
|
|
\begin{description}
|
|
\item[{\em const}]eo\-Pop$<$EOT$>$\& \_\-pop the population \end{description}
|
|
\end{Desc}
|
|
|
|
|
|
Implements {\bf eo\-IBEA$<$ EOT, Fitness\-Eval $>$} {\rm (p.\,\pageref{classeoIBEA})}.
|
|
|
|
Definition at line 413 of file eo\-IBEA.h.\index{eoIBEAAvgSorting@{eo\-IBEAAvg\-Sorting}!fitnesses@{fitnesses}}
|
|
\index{fitnesses@{fitnesses}!eoIBEAAvgSorting@{eo\-IBEAAvg\-Sorting}}
|
|
\subsubsection{\setlength{\rightskip}{0pt plus 5cm}template$<$class EOT, class Fitness\-Eval = typename EOT::Fitness::Fitness\-Eval$>$ void {\bf eo\-IBEAAvg\-Sorting}$<$ EOT, Fitness\-Eval $>$::fitnesses (const {\bf eo\-Pop}$<$ EOT $>$ \& {\em \_\-pop})\hspace{0.3cm}{\tt [inline, private, virtual]}}\label{classeoIBEAAvgSorting_4bc8c46d151d7935d7b2ac8bfbdd7ee6}
|
|
|
|
|
|
computation and setting of the fitness for each individual of the population
|
|
|
|
\begin{Desc}
|
|
\item[Parameters:]
|
|
\begin{description}
|
|
\item[{\em const}]eo\-Pop$<$EOT$>$\& \_\-pop the population \end{description}
|
|
\end{Desc}
|
|
|
|
|
|
Implements {\bf eo\-IBEA$<$ EOT, Fitness\-Eval $>$} {\rm (p.\,\pageref{classeoIBEA})}.
|
|
|
|
Definition at line 445 of file eo\-IBEA.h.
|
|
|
|
The documentation for this class was generated from the following file:\begin{CompactItemize}
|
|
\item
|
|
eo\-IBEA.h\end{CompactItemize}
|