Add legends, add better biblio style, add text

main.tex

@@ -2,7 +2,8 @@
 \documentclass{llncs}
 \input{prelude}
 \begin{document} 
-\title{$\mbox{\EightStarBold}$ Discrepancies for generalized Halton points}
+\title{$\mbox{\EightStarBold}$ Discrepancies for generalized Halton points\\
+Comparison of three heuristics for generating points set}
 % \titlerunning{}  % abbreviated title (for running head)
 %                                     also used for the TOC unless
 %                                     \toctitle is used
@@ -19,11 +20,25 @@
 \maketitle
 
 \makeatletter
-
+\renewcommand\bibsection%
+{
+  \section*{\refname
+    \@mkboth{\MakeUppercase{\refname}}{\MakeUppercase{\refname}}}
+  }
 \makeatother
 
 
 \begin{abstract}
+  Geometric discrepancies are standard measures to quantify the irregularity of 
+  distributions. They are an important notion in numerical integration. 
+  One of the most important discrepancy notions is the so-called star 
+  discrepancy. Roughly speaking, a point set of low star discrepancy value 
+  allows for a small approximation error in quasi-Monte Carlo integration.
+  In this work we present a tool realizing the implantation of three 
+  basics heuristics for construction low discrepancy points sets 
+  in the generalized Halton model: fully random search, local search with
+  simmulated annealing and genetic $(5+5)$ search with a ad-hoc 
+  crossover function. 
 \end{abstract}
 
 
@@ -351,9 +366,9 @@ annealing and genetic search --- is clear over fully random search.
 Both curves for these heuristics are way below the error band of random 
 search. As a result \emph{worse average results of non trivial heuristics are
 better than best average results when sampling points at random}.
-In dimension 2~\ref{wrap2z}, the best results are given by the genetic search,
+In dimension 2~\ref{wrap2z}, the best results are given by the simulated annealing,
 whereas in dimension 3 and 4~\ref{wrap3z},~\ref{wrap4z}, best results are
-given by simulated annealing. It is also noticeable that in that range
+given by genetic search. It is also noticeable that in that range
 of points the error rates are roughly the same for all heuristics: 
 \emph{for 1000 iteration, the stability of the results is globally the
 same for each heuristic}.
@@ -384,6 +399,13 @@ same for each heuristic}.
 
 \section{Conclusion}
 
+\section*{Acknoledgments}
+We would like to thank Magnus Wahlstrom from the Max Planck Institute for Informatics 
+for providing an implementation of the DEM algorithm [DEM96]. 
+We would also like to thank Christoff Durr and Carola Doerr 
+for several very helpful talks on the topic of this work.
+Both Thomas Espitau and Olivier Marty  supported by the French Ministry for
+Research and Higher Education, trough the Ecole Normale Supérieure.
   \bibliographystyle{alpha}
   \bibliography{bi}
 \end{document}

src/legend.py

@@ -0,0 +1,11 @@
+import matplotlib.pyplot as plt
+
+plt.subplot(223)
+plt.plot([1,2,3], label="400")
+plt.plot([1,2,3], label="1000")
+plt.plot([1,2,3], label="1200")
+plt.plot([1,2,3], label="1400")
+# Place a legend to the right of this smaller figure.
+plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+
+plt.show()

src/res_genetic_i22

@@ -0,0 +1 @@
+0.0181765