Commit 92975345 authored by Grégoire Grzeczkowicz's avatar Grégoire Grzeczkowicz
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parent 451de15a
......@@ -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
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