ISRES: Improved Stochastic Ranking Evolutionary Strategy

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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 = [0.99967706 0.99754428 0.99882445 0.99786593 0.99442271 0.98330967
 0.99667036 0.99859849 0.98577216 2.98825461 2.95822828 2.95195257
 0.9983846 ]
F = [-14.82521241]
CV = [0.]

API#

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

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.

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