ISRES: Improved Stochastic Ranking Evolutionary Strategy¶

Improved Stochastic Ranking Evolutionary Strategy [19].

[1]:

from pymoo.algorithms.soo.nonconvex.isres import ISRES
from pymoo.problems import get_problem
from pymoo.optimize import minimize

problem = get_problem("g1")

algorithm = ISRES(n_offsprings=200, rule=1.0 / 7.0, gamma=0.85, alpha=0.2)

res = minimize(problem,
               algorithm,
               ("n_gen", 200),
               seed=1,
               verbose=False)

print("Best solution found: \nX = %s\nF = %s\nCV = %s" % (res.X, res.F, res.CV))

Best solution found:
X = [0.99117883 0.99876403 0.99778376 0.99808482 0.98913885 0.99960456
 0.99263521 0.99982859 0.98653671 2.98502345 2.95976567 2.97758264
 0.96736092]
F = [-14.78697343]
CV = [0.]

API¶

class pymoo.algorithms.soo.nonconvex.isres.ISRES(self, gamma=0.85, alpha=0.2, **kwargs)

Improved Stochastic Ranking Evolutionary Strategy (SRES)

Parameters

alphafloat: Length scale of the differentials during mutation.
PF: float: The stochastic ranking weight for choosing a random decision while doing the modified bubble sort.