Readme

README.md

@@ -123,8 +123,13 @@ 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 call draw_ert.py with -N option for the number of run and -v option to choose a target value
 4. Implement a simple design of experiment to determine the best solver.
+    You can use try_solver to list the different parameter you want to try and get the best parameter
 5. Provide a solver for a competition.
+    For the competion, symply use my num_greedy