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 = [0.99967577 0.99971414 0.99973311 0.999764   0.99870792 0.99942824
 0.99916441 0.9989322  0.99811175 2.9964412  2.99609017 2.99323582
 0.9966935 ]
F = [-14.97124186]
CV = [0.]

API#

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

Initialize ISRES algorithm.

Parameters:

  • gamma – Differential weight for elite individuals.

  • alpha – Length scale of the differentials during mutation.

  • **kwargs – Additional arguments passed to parent class.

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