Add informations

README.md

@@ -127,9 +127,21 @@ Most exercises consists in adding a single function in an existing module
 2. Implement an evolutionary algorithm.
     You can use `num_genetic` or `bit_genetic`
 3. Implement an expected run time empirical cumulative density function.
-    You can call `draw_ert.py` with -N option for the number of run and -v option to choose a target value
+    You can execute `ert.py` which will plot 3 ert for two algorithms.
+    You can also use `draw_ert.py` with -N option for the number of run and optionally -v option to choose a target value (if not use will draw a surface for all target)
 4. Implement a simple design of experiment to determine the best solver.
-    You can use `try_solver.py` to list the different parameter you want to try and get the best parameter
+    You can use `try_solver.py` to list the different parameter you want to try and get the best parameter (parameter inside the script)
 5. Provide a solver for a competition.
-    For the competion, symply use my `num_greedy`
+    For the competion, symply use my `snp.py`.
+
+    I have had the following penalisation to `num.py` :
+    ```python3
+    if len(sol) > dim:
+        s *= 0.5
+    for v in sol:
+        if v < 0:
+            s += v/domain_width
+        if v >= domain_width:
+            s -= (v-domain_width)/domain_width
+    ```