\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for all functions and subgroups in #1-D. The targets are chosen from $10^{[-8..2]}$ such that the bestGECCO2009 artificial algorithm just not reached them within a given budget of $k$ $\times$ DIM, with $k\in \{0.5, 1.2, 3, 10, 50\}$. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each selected target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value) divided by dimension versus dimension. The target function value 
                is chosen such that the bestGECCO2009 artificial algorithm just failed to achieve 
                an \ERT\ of $10\times\DIM$. Different symbols correspond to different algorithms given in the legend of #1. Light symbols give the maximum number of function evaluations from the longest trial divided by dimension. Black stars indicate a statistically better result compared to all other algorithms with $p<0.01$ and Bonferroni correction number of dimensions (six).  
Legend: 
{\color{NavyBlue}$\circ$}:\algorithmA
, {\color{red}$\triangledown$}:\algorithmB
, {\color{Goldenrod}$\star$}:\algorithmC
, {\color{VioletRed}$\Box$}:\algorithmD
, {\color{Black}$\triangle$}:\algorithmE
}
\providecommand{\bbobpptablesmanylegend}[1]{%
    Expected running time (ERT in number of function 
    evaluations) divided by the respective best ERT measured during BBOB-2009 in
    #1.
    The ERT and in braces, as dispersion measure, the half difference between 90 and 
    10\%-tile of bootstrapped run lengths appear for each algorithm and 
    %
    run-length based target, the corresponding best ERT
    (preceded by the target \Df-value in \textit{italics}) in the first row. 
    \#succ is the number of trials that reached the target value of the last column.
    %
    The median number of conducted function evaluations is additionally given in 
    \textit{italics}, if the target in the last column was never reached. 
    Entries, succeeded by a star, are statistically significantly better (according to
    the rank-sum test) when compared to all other algorithms of the table, with
    $p = 0.05$ or $p = 10^{-k}$ when the number $k$ following the star is larger
    than 1, with Bonferroni correction by the number of instances.
    }
\providecommand{\algorithmA}{ HCMA}
\providecommand{\algorithmB}{ LSfminbnd posik noiseless}
\providecommand{\algorithmC}{ LSstep posik noiseless}
\providecommand{\algorithmD}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmE}{t2000ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for all functions and subgroups in #1-D. The targets are chosen from $10^{[-8..2]}$ such that the bestGECCO2009 artificial algorithm just not reached them within a given budget of $k$ $\times$ DIM, with $k\in \{0.5, 1.2, 3, 10, 50\}$. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each selected target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value) divided by dimension versus dimension. The target function value 
                is chosen such that the bestGECCO2009 artificial algorithm just failed to achieve 
                an \ERT\ of $10\times\DIM$. Different symbols correspond to different algorithms given in the legend of #1. Light symbols give the maximum number of function evaluations from the longest trial divided by dimension. Black stars indicate a statistically better result compared to all other algorithms with $p<0.01$ and Bonferroni correction number of dimensions (six).  
Legend: 
{\color{NavyBlue}$\circ$}:\algorithmA
, {\color{red}$\triangledown$}:\algorithmB
, {\color{Goldenrod}$\star$}:\algorithmC
, {\color{VioletRed}$\Box$}:\algorithmD
, {\color{Black}$\triangle$}:\algorithmE
, {\color{CornflowerBlue}$\diamondsuit$}:\algorithmF
}
\providecommand{\bbobpptablesmanylegend}[1]{%
    Expected running time (ERT in number of function 
    evaluations) divided by the respective best ERT measured during BBOB-2009 in
    #1.
    The ERT and in braces, as dispersion measure, the half difference between 90 and 
    10\%-tile of bootstrapped run lengths appear for each algorithm and 
    %
    run-length based target, the corresponding best ERT
    (preceded by the target \Df-value in \textit{italics}) in the first row. 
    \#succ is the number of trials that reached the target value of the last column.
    %
    The median number of conducted function evaluations is additionally given in 
    \textit{italics}, if the target in the last column was never reached. 
    Entries, succeeded by a star, are statistically significantly better (according to
    the rank-sum test) when compared to all other algorithms of the table, with
    $p = 0.05$ or $p = 10^{-k}$ when the number $k$ following the star is larger
    than 1, with Bonferroni correction by the number of instances.
    }
\providecommand{\algorithmA}{I0t2000ndsqistep}
\providecommand{\algorithmB}{I0ndsqistep}
\providecommand{\algorithmC}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmD}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmE}{PI0t2000ndsqistep}
\providecommand{\algorithmF}{t2000ndstep}
