Merge branch 'master' of localhost:olivier/projet_optim_m2

Conflicts:
	main.tex

main.tex

@@ -283,38 +283,51 @@ rates for fully random search with 400 iterations.
 
 \begin{figure}
  \begin{mdframed}
-  \label{rand_flow}
 \includegraphics[scale=0.4]{crossover_flow.pdf}
 \caption{Flowchart of the crossover algorithm.}
+  \label{cross_flow}
 \end{mdframed}
 \end{figure}
 
+\subsubsection{Dependence on the parameter p}
+First experiments were made to select the value for the crossover parameter
+p. Results are compiled in graphs~\ref{res_gen2},~\ref{res_gen2z},\ref{res_gen3}
+and~\ref{res_gen4}.
+Graph~\ref{res_gen2}, represents the results obtained
+in dimension 2 between 10 and 500 points. The curve obtained is, with no
+surprise again, 
+the characteristic curve of the average evolution of the discrepancy we already 
+saw with the previous experiments.
+The most interesting part of these results are concentrated --- once again ---
+between 80 and 160 points were the different curves splits. 
+The graph~\ref{res_gen2z} is a zoom  of~\ref{res_gen2} in this window, and 
+graphs~\ref{res_gen3} and~\ref{res_gen4} are focused directly into it too.
+
 \begin{figure}
-  \label{rand_flow}
 \includegraphics[scale=0.3]{Results/res_gen_2.png}
-\caption{Dependence on iterations number: D=3}
+\caption{Dependence on parameter p: D=2}
+  \label{res_gen2}
 \end{figure}
 
 \begin{figure}
-  \label{rand_flow}
 \includegraphics[scale=0.3]{Results/res_gen_2_zoom.png}
-\caption{Dependence on iterations number: D=3}
+\caption{Dependence on parameter p (zoom): D=2}
+\label{res_gen2z}
 \end{figure}
 \begin{figure}
-  \label{rand_flow}
 \includegraphics[scale=0.3]{Results/res_gen_3_zoom.png}
-\caption{Dependence on iterations number: D=3}
+\caption{Dependence on parameter p: D=3}
+  \label{res_gen3}
 \end{figure}
 
 \begin{figure}
-  \label{rand_flow}
 \includegraphics[scale=0.3]{Results/res_gen_4_zoom.png}
-\caption{Dependence on iterations number: D=3}
+\caption{Dependence on parameter p: D=4}
+  \label{res_gen4}
 \end{figure}
 
-As previously we investigated the stability
-of the algorithm with regards to the number of iterations. We present here
-the result in dimension 3 in the graph~\ref{iter_sa}. Once again we
+Once again we investigated the stability
+of the algorithm with regards to the number of iterations.  Once again we
 restricted the window between 80 and 180 points were curves are split.
 An interesting phenomena can be observed: the error rates are somehow 
 invariant w.r.t.\ the number of iteration and once again the 1000 iterations

src/res_genetic_i22

@@ -1 +0,0 @@
-0.0192831

src/wrapper_genetic_iter.py

@@ -17,7 +17,7 @@ def gen_generate(dim, l, mu = 5, la = 5, c = 0.5, iter=1000):
     resu = []
     resu_bars_mini = []
     resu_bars_maxi = []
-    NB_IT = 8
+    NB_IT = 4
     for i in range(80, 200, 25):
         results[i] = []
         axis.append(i)
@@ -43,12 +43,10 @@ def gen_generate(dim, l, mu = 5, la = 5, c = 0.5, iter=1000):
     errorfill(axis, resu, resu_bars)
 
 dim = 2
-gen_generate(dim, 3, 5, 5, 0.5, 200)
-gen_generate(dim, 3, 5, 5, 0.5, 600)
+gen_generate(dim, 3, 5, 5, 0.5, 400)
 gen_generate(dim, 3, 5, 5, 0.5,1000)
 gen_generate(dim, 3, 5, 5, 0.5, 1200)
 gen_generate(dim, 3, 5, 5, 0.5, 1400)
 
-errorfill(axis, resu, resu_bars)
 
 plt.show()