cma_es_rosenbrock_optimization_quickstart_example.py

1import cma
2
3# help(cma)  # show the package documentation
4# help(cma.fmin)  # show the fmin function documentation
5
6# Optimize the 10-dimensional Rosenbrock function
7# starting at all zeros with a standard deviation (sigma) of 0.5
8res = cma.fmin(cma.ff.rosen, 10 * [0], 0.5)
9
10# Print the best found solution and its function value
11print("Best solution found: ", res[0])
12print("Best objective function value: ", res[1])
13
14# res[0] is the best found solution
15# res[1] is the corresponding function value
16# res[2] is the number of evaluations