ISRES: Improved Stochastic Ranking Evolutionary Strategy¶

Improved Stochastic Ranking Evolutionary Strategy [15].

[1]:

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

problem = get_problem("g01")

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 = [0.99982238 0.99874802 0.99888751 0.99942657 0.99757724 0.99868055
 0.99707856 0.99687596 0.996203   2.99400712 2.98887874 2.97707148
 0.9949878 ]
F = [-14.92579868]
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.