This implements only a single class: DiffEvolOptimizer that follows the differential evolution optimization method by Storn & Price (Storn, R., Price, K., Journal of Global Optimization 11: 341--359, 1997, DE original paper)
Main assuption: the heuristic suppose a continuous parameter space.
Full documentation at: http://mfouesneau.github.io/docs/de/
def rosenbrock_fn(x):
""" Rosenbrock function
global minimum at x = [1, ..., 1], f(x) = 0
"""
_x = np.array(x)
return sum(100.0 * (_x[1:] - _x[:-1] ** 2) ** 2. + (1 - _x[:-1]) ** 2.)
from de import DiffEvolOptimizer
# setup the optimization
ngen, npop, ndim = 100, 100, 2
limits = [[-5, 5]] * ndim
de = DiffEvolOptimizer(rosenbrock_fn, limits, npop)
# store all the values during iterations for plotting.
pop = np.zeros([ngen, npop, ndim])
loc = np.zeros([ngen, ndim])
for i, res in enumerate(de(ngen)):
pop[i,:,:] = de.population.copy()
loc[i,:] = de.location.copy()
# plot all explored points
ax.scatter(pop[:, :, 0].ravel(), pop[:, :, 1].ravel(),
c=np.log10(vals + 1e-20), alpha=0.2, edgecolor='None')
# plot the final positions
plt.plot(loc[:, 0], loc[:, 1], 'k.-')
plt.show()Testing multiple common functions (code included in test.py)
