def test_griewank_works_proportional_fine(self):
de_griewank = AgingNpDifferentialEvolution(NP=10,
CR=0.5,
F=0.9,
age=proportional,
seed=self.seed)
de_griewankc = AgingNpDifferentialEvolution(NP=10,
CR=0.5,
F=0.9,
age=proportional,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_griewank, de_griewankc,
'griewank')
def test_custom_works_proportional_fine(self):
de_custom = AgingNpDifferentialEvolution(NP=40,
F=0.5,
CR=0.9,
age=proportional,
seed=self.seed)
de_customc = AgingNpDifferentialEvolution(NP=40,
F=0.5,
CR=0.9,
age=proportional,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_custom, de_customc,
MyBenchmark())
def test_griewank_works_fine(self):
de_griewank = AgingNpDifferentialEvolution(NP=10,
D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
CR=0.5,
F=0.9,
benchmark='griewank',
seed=self.seed)
de_griewankc = AgingNpDifferentialEvolution(NP=10,
D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
CR=0.5,
F=0.9,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_griewank, de_griewankc)
def test_Custom_works_fine(self):
de_custom = AgingNpDifferentialEvolution(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
CR=0.9,
benchmark=MyBenchmark(),
seed=self.seed)
de_customc = AgingNpDifferentialEvolution(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
F=0.5,
CR=0.9,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, de_custom, de_customc)
# This is temporary fix to import module from parent folder
# It will be removed when package is published on PyPI
import sys
sys.path.append('../')
# End of fix
import random
from NiaPy.algorithms.basic import AgingNpDifferentialEvolution
from NiaPy.algorithms.basic.de import bilinear
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Differential Evolution for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=10000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = AgingNpDifferentialEvolution(NP=40,
F=0.63,
CR=0.9,
Lt_min=3,
Lt_max=7,
omega=0.2,
delta_np=0.1,
age=bilinear)
best = algo.run(task=task)
print('%s -> %s' % (best[0].x, best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def test_type_parameters(self):
d = AgingNpDifferentialEvolution.typeParameters()
self.assertIsNotNone(d.pop('Lt_min', None))
self.assertIsNotNone(d.pop('Lt_max', None))
self.assertIsNotNone(d.pop('delta_np', None))
self.assertIsNotNone(d.pop('omega', None))
def test_algorithm_info(self):
self.assertIsNotNone(AgingNpDifferentialEvolution.algorithmInfo())