@@ -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` :