Global optimization using differential evolution in Python [Storn97].
git clone https://github.com/hpparvi/PyDE.git cd PyDE python setup.py install [--user]
Import the class from the package
from pyde.de import DiffEvol
Create a DiffEvol instance
de = DiffEvol(minfun, bounds, npop)
where minfun is the function to be optimized, bounds is an initialization array,
and npop is the size of the parameter vector population.
Now, you can run the optimizer ngen generations:
res = de.optimize(ngen=100)
or run the optimizer as a generator:
for res in de(ngen=100):
do something
The PyDE parameter vector population can be used to initialize the affine-invariant MCMC sampler emcee when a simple point estimate of the function minimum (or maximum) is not sufficient:
de = DiffEvol(lnpost, bounds, npop, maximize=True) de.optimize(ngen) sampler = emcee.EnsembleSampler(npop, ndim, lnpost) sampler.run_mcmc(de.population, 1000)
| [Storn97] | Storn, R., Price, K., Journal of Global Optimization 11: 341--359, 1997 |
pyde.de.DiffEvol (minfun, bounds, npop, F=0.5, C=0.5,
seed=0, maximize=False)
Parameters
minfun: | Function to be minimized. |
|---|---|
bounds: | Parameter space bounds as [npar,2] array. |
npop: | Size of the parameter vector population. |
F: | Difference amplification factor. Values between 0.5-0.8 are good in most cases. |
C: | Cross-over probability. Use 0.9 to test for fast convergence, and smaller values (~0.1) for a more elaborate search. |
seed: | Random seed. |
maximize: | An optional switch telling whether we want maximize or minimize the function. Defaults to minimization. |