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README.md

@@ -124,11 +124,11 @@ Most exercises consists in adding a single function in an existing module
 
 1. Implement a simulated annealing.
     
-    You can use `num_simulated_annealing` or `bit_simulated_annealing` solver
+    You can use `num_simulated_annealing` or `bit_simulated_annealing` solvers
 
 2. Implement an evolutionary algorithm.
     
-    You can use `num_genetic` or `bit_genetic`
+    You can use `num_genetic` or `bit_genetic` solvers
 
 3. Implement an expected run time empirical cumulative density function.
     
@@ -137,13 +137,13 @@ Most exercises consists in adding a single function in an existing module
 
 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)
+    You can use `try_solver.py` to list the different parameter you want to try and get the best parameter (you can chose the alogorithms at the begenin of 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` :
+    I have had the following penalisation to `num.py` at the end of `cover_sum` :
     ```python3
     if len(sol) > dim:
         s *= 0.5