More informations

README.md

@@ -123,15 +123,24 @@ Most exercises consists in adding a single function in an existing module
 (or your own module) and use assemble it in the main executable.
 
 1. Implement a simulated annealing.
+    
     You can use `num_simulated_annealing` or `bit_simulated_annealing` solver
+
 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 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 (parameter inside the script)
+
 5. Provide a solver for a competition.
+    
     For the competion, symply use my `snp.py`.
 
     I have had the following penalisation to `num.py` :
@@ -144,4 +153,3 @@ Most exercises consists in adding a single function in an existing module
         if v >= domain_width:
             s -= (v-domain_width)/domain_width
     ```
-