\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{I0t2000ndsqistep}
\providecommand{\algorithmE}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmF}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmG}{PI0t2000ndsqistep}
\providecommand{\algorithmH}{t2000ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{I0t2000ndsqistep}
\providecommand{\algorithmE}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmF}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmG}{PI0t2000ndsqistep}
\providecommand{\algorithmH}{t2000ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{I5ndsqistep}
\providecommand{\algorithmB}{I0t2000ndsqistep}
\providecommand{\algorithmC}{I0ndsqistep}
\providecommand{\algorithmD}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmE}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmF}{PI0t2000ndsqistep}
\providecommand{\algorithmG}{t2000ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{I5ndsqistep}
\providecommand{\algorithmB}{I5t2000ndsqistep}
\providecommand{\algorithmC}{I0t2000ndsqistep}
\providecommand{\algorithmD}{I0ndsqistep}
\providecommand{\algorithmE}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmF}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmG}{PI0t2000ndsqistep}
\providecommand{\algorithmH}{t2000ndstep}
\providecommand{\algorithmI}{g1ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
, {\color{SkyBlue}$\triangleleft$}:\algorithmJ
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{t2000ndstep}
\providecommand{\algorithmB}{g1ndstep}
\providecommand{\algorithmC}{I5ndsqistep}
\providecommand{\algorithmD}{I5t2000ndsqistep}
\providecommand{\algorithmE}{I0t2000ndsqistep}
\providecommand{\algorithmF}{I0ndsqistep}
\providecommand{\algorithmG}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmH}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmI}{PI0t2000ndsqistep}
\providecommand{\algorithmJ}{Pt2000ndsqistep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{t2000ndstep}
\providecommand{\algorithmB}{g1ndstep}
\providecommand{\algorithmC}{I5ndsqistep}
\providecommand{\algorithmD}{I5t2000ndsqistep}
\providecommand{\algorithmE}{I0t2000ndsqistep}
\providecommand{\algorithmF}{I0ndsqistep}
\providecommand{\algorithmG}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmH}{minsqit2000ndsqistep}
\providecommand{\algorithmI}{minsqiPg0I5t2000ndsqistep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
, {\color{SkyBlue}$\triangleleft$}:\algorithmJ
, {\color{Lavender}$\Diamond$}:\algorithmK
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{t2000ndstep}
\providecommand{\algorithmB}{g1ndstep}
\providecommand{\algorithmC}{I5ndsqistep}
\providecommand{\algorithmD}{I5t2000ndsqistep}
\providecommand{\algorithmE}{I0t2000ndsqistep}
\providecommand{\algorithmF}{I0ndsqistep}
\providecommand{\algorithmG}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmH}{minsqit2000ndsqistep}
\providecommand{\algorithmI}{PI0t2000ndsqistep}
\providecommand{\algorithmJ}{minsqiPt2000ndsqistep}
\providecommand{\algorithmK}{minsqiPg0I5t2000ndsqistep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
, {\color{SkyBlue}$\triangleleft$}:\algorithmJ
, {\color{Lavender}$\Diamond$}:\algorithmK
, {\color{ForestGreen}$\downY$}:\algorithmL
, {\color{LimeGreen}$\upY$}:\algorithmM
, {\color{YellowGreen}$\leftY$}:\algorithmN
, {\color{GreenYellow}$\rightY$}:\algorithmO
, {\color{NavyBlue}$\triangledown$}:\algorithmP
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{t2000ndstep}
\providecommand{\algorithmB}{g1ndstep}
\providecommand{\algorithmC}{I5ndsqistep}
\providecommand{\algorithmD}{I5t2000ndsqistep}
\providecommand{\algorithmE}{I0t2000ndsqistep}
\providecommand{\algorithmF}{I0ndsqistep}
\providecommand{\algorithmG}{minsqiI0t2000ndsqistep}
\providecommand{\algorithmH}{minsqiPI0t2000ndsqistep}
\providecommand{\algorithmI}{minsqiPg0I0t2000ndsqistep}
\providecommand{\algorithmJ}{minsqiPg0I5t2000ndsqistep}
\providecommand{\algorithmK}{minsqiPt2000ndsqistep}
\providecommand{\algorithmL}{minsqig0I0t2000ndsqistep}
\providecommand{\algorithmM}{minsqig0t2000ndsqistep}
\providecommand{\algorithmN}{minsqit2000ndsqistep}
\providecommand{\algorithmO}{PI0t2000ndsqistep}
\providecommand{\algorithmP}{Pt2000ndsqistep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{t2000ndstep}
\providecommand{\algorithmB}{g1ndstep}
\providecommand{\algorithmC}{I5ndsqistep}
\providecommand{\algorithmD}{I5t2000ndsqistep}
\providecommand{\algorithmE}{I0t2000ndsqistep}
\providecommand{\algorithmF}{I0ndsqistep}
\providecommand{\algorithmG}{minsqiI0t2000ndsqistep}
\providecommand{\algorithmH}{minsqit2000ndsqistep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{t2000ndstep}
\providecommand{\algorithmB}{g1ndstep}
\providecommand{\algorithmC}{I5ndsqistep}
\providecommand{\algorithmD}{I5t2000ndsqistep}
\providecommand{\algorithmE}{I0t2000ndsqistep}
\providecommand{\algorithmF}{I0ndsqistep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{ndstep seq}
\providecommand{\algorithmB}{ndstep seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\algorithmE}{I5ndsqistep}
\providecommand{\algorithmF}{I5ndsqistep seq}
\providecommand{\algorithmG}{I5t2000ndsqistep}
\providecommand{\algorithmH}{t2000ndsqistep}
\providecommand{\algorithmI}{ndsqistep}
\providecommand{\algorithmA}{ndstep seq}
\providecommand{\algorithmB}{ndstep seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\algorithmE}{I5ndsqistep}
\providecommand{\algorithmF}{I5ndsqistep seq}
\providecommand{\algorithmG}{I5t2000ndsqistep}
\providecommand{\algorithmH}{t2000ndsqistep}
\providecommand{\algorithmI}{ndsqistep}
\providecommand{\algorithmJ}{ndsqistep seq}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
, {\color{SkyBlue}$\triangleleft$}:\algorithmJ
, {\color{Lavender}$\Diamond$}:\algorithmK
, {\color{ForestGreen}$\downY$}:\algorithmL
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{ndstep seq}
\providecommand{\algorithmB}{ndstep seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\algorithmE}{mindifft2000g0.5ndstep}
\providecommand{\algorithmF}{maxdifft2000g0.5ndstep}
\providecommand{\algorithmG}{historyt2000g0.5ndstep}
\providecommand{\algorithmH}{historyRAt2000g0.5ndstep}
\providecommand{\algorithmI}{diffpdt2000g0ndstep}
\providecommand{\algorithmJ}{rdiffpdt2000g0ndstep}
\providecommand{\algorithmK}{ndsqistep}
\providecommand{\algorithmL}{ndsqistep seq}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
, {\color{Orange}$\varhexagon$}:\algorithmG
, {\color{Magenta}$\pentagon$}:\algorithmH
, {\color{Gray}$\hexagon$}:\algorithmI
, {\color{SkyBlue}$\triangleleft$}:\algorithmJ
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{ndstep seq}
\providecommand{\algorithmB}{ndstep seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\algorithmE}{mindifft2000g0.5ndstep}
\providecommand{\algorithmF}{maxdifft2000g0.5ndstep}
\providecommand{\algorithmG}{historyt2000g0.5ndstep}
\providecommand{\algorithmH}{historyRAt2000g0.5ndstep}
\providecommand{\algorithmI}{diffpdt2000g0ndstep}
\providecommand{\algorithmJ}{rdiffpdt2000g0ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{ndstep seq}
\providecommand{\algorithmB}{ndstep seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\bbobECDFslegend}[1]{
Bootstrapped empirical cumulative distribution of the number of objective function evaluations divided by dimension (FEvals/DIM) for 50 targets in $10^{[-8..2]}$ for all functions and subgroups in #1-D. The ``best 2009'' line corresponds to the best \ERT\ observed during BBOB 2009 for each single target.
}
\providecommand{\bbobppfigslegend}[1]{
Expected running time (\ERT\ in number of $f$-evaluations 
                as $\log_{10}$ value), divided by dimension for target function value $10^{-8}$ 
                versus dimension. Slanted grid lines indicate quadratic scaling with the dimension. 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
}
\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 
    %
    target, the corresponding best ERT
    in the first row. The different target \Df-values are shown in the top row. 
    \#succ is the number of trials that reached the (final) target $\fopt + 10^{-8}$.
    %
    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}{ndstep seq}
\providecommand{\algorithmB}{ndstep seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\bbobpptablesmanylegend}[1]{Expected running time (ERT in number of function evaluations)
                     divided by the respective best ERT measured during BBOB-2009 (given in 
                     the respective first row) for different $\Df$ values in #1. 
                     The central 80\% range divided by two is given in braces. 
                     The median number of conducted function evaluations is additionally given in 
                     \textit{italics}, if $\ERT(10^{-7}) = \infty$.
                     \#succ is the number of trials that reached the final target $\fopt + 10^{-8}$.
                     Best results are printed in bold. }
\providecommand{\algorithmA}{ndstep\_seq}
\providecommand{\algorithmB}{ndstep\_seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{g1ndstep}
\providecommand{\bbobpptablesmanylegend}[1]{Expected running time (ERT in number of function evaluations)
                     divided by the respective best ERT measured during BBOB-2009 (given in 
                     the respective first row) for different $\Df$ values in #1. 
                     The central 80\% range divided by two is given in braces. 
                     The median number of conducted function evaluations is additionally given in 
                     \textit{italics}, if $\ERT(10^{-7}) = \infty$.
                     \#succ is the number of trials that reached the final target $\fopt + 10^{-8}$.
                     Best results are printed in bold. }
\providecommand{\algorithmA}{ndstep\_seq}
\providecommand{\algorithmB}{ndstep\_seq-u200}
\providecommand{\algorithmC}{t2000ndstep}
\providecommand{\algorithmD}{mindifft2000g0.5ndstep}
\providecommand{\algorithmE}{maxdifft2000g0.5ndstep}
\providecommand{\algorithmF}{diffpdt2000g0ndstep}
\providecommand{\algorithmG}{rdiffpdt2000g0ndstep}
\providecommand{\algorithmH}{historyRAt2000g0.5ndstep}
\providecommand{\algorithmI}{historyt2000g0.5ndstep}
\providecommand{\algorithmJ}{g1ndstep}
\providecommand{\algorithmA}{ndstep\_seq}
\providecommand{\algorithmB}{ndstep\_seq-u200}
\providecommand{\algorithmC}{ndstep}
\providecommand{\algorithmD}{t2000ndstep}
\providecommand{\algorithmE}{mindifft2000g0.5ndstep}
\providecommand{\algorithmF}{maxdifft2000g0.5ndstep}
\providecommand{\algorithmG}{diffpdt2000g0ndstep}
\providecommand{\algorithmH}{rdiffpdt2000g0ndstep}
\providecommand{\algorithmI}{historyRAt2000g0.5ndstep}
\providecommand{\algorithmJ}{historyt2000g0.5ndstep}
\providecommand{\algorithmK}{g1ndstep}
\providecommand{\bbobpptablesmanylegend}[1]{Expected running time (ERT in number of function evaluations)
                     divided by the respective best ERT measured during BBOB-2009 (given in 
                     the respective first row) for different $\Df$ values in #1. 
                     The central 80\% range divided by two is given in braces. 
                     The median number of conducted function evaluations is additionally given in 
                     \textit{italics}, if $\ERT(10^{-7}) = \infty$.
                     \#succ is the number of trials that reached the final target $\fopt + 10^{-8}$.
                     Best results are printed in bold. }
\providecommand{\algorithmA}{ndstep\_seq}
\providecommand{\algorithmB}{t2000ndstep}
\providecommand{\algorithmC}{mindifft2000g0.5ndstep}
\providecommand{\algorithmD}{maxdifft2000g0.5ndstep}
\providecommand{\algorithmE}{diffpdt2000g0ndstep}
\providecommand{\algorithmF}{rdiffpdt2000g0ndstep}
\providecommand{\algorithmG}{historyRAt2000g0.5ndstep}
\providecommand{\algorithmH}{historyt2000g0.5ndstep}
\providecommand{\algorithmI}{g1ndstep}
