class jDETestCase(TestCase):
def setUp(self):
self.jde_custom = SelfAdaptiveDifferentialEvolutionAlgorithm(
D=10,
NP=40,
nFES=1000,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark=MyBenchmark())
self.jde_griewank = SelfAdaptiveDifferentialEvolutionAlgorithm(
D=10,
NP=40,
nFES=1000,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark='griewank')
def test_custom_works_fine(self):
self.assertTrue(self.jde_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.jde_griewank.run())
class jDETestCase(TestCase):
def setUp(self):
self.jde_custom = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.1, MyBenchmark())
self.jde_griewank = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.1, 'griewank')
def test_custom_works_fine(self):
self.assertTrue(self.jde_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.jde_griewank.run())
def logging_example(D=10, nFES=50000):
task = TaskConvPrint(D=D, nFES=nFES, nGEN=50000, benchmark=MyBenchmark())
algo = SelfAdaptiveDifferentialEvolutionAlgorithm(NP=10,
F=0.5,
F_l=-1,
F_u=2.0,
Tao1=0.1,
CR=0.45,
Tao2=0.25,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def plot_example(D=10, nFES=50000):
task = TaskConvPlot(D=D, nFES=nFES, nGEN=10000, benchmark=MyBenchmark())
algo = SelfAdaptiveDifferentialEvolutionAlgorithm(NP=10,
F=0.5,
F_l=-1,
F_u=2.0,
Tao1=0.1,
CR=0.45,
Tao2=0.25,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def simple_example(runs=10, D=50, nFES=50000):
for i in range(10):
algo = SelfAdaptiveDifferentialEvolutionAlgorithm(
NP=50,
D=D,
nFES=nFES,
F=0.5,
F_l=-1,
F_u=2.0,
Tao1=0.1,
CR=0.45,
Tao2=0.25,
benchmark=MyBenchmark())
Best = algo.run()
logger.info('%s %s' % (Best[0], Best[1]))
def setUp(self):
self.jde_custom = SelfAdaptiveDifferentialEvolutionAlgorithm(
D=10,
NP=40,
nFES=1000,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark=MyBenchmark())
self.jde_griewank = SelfAdaptiveDifferentialEvolutionAlgorithm(
D=10,
NP=40,
nFES=1000,
F=0.5,
F_l=0.0,
F_u=2.0,
Tao1=0.9,
CR=0.1,
Tao2=0.45,
benchmark='griewank')
def setUp(self):
self.jde_custom = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.1, MyBenchmark())
self.jde_griewank = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.1, 'griewank')
# 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.modified import SelfAdaptiveDifferentialEvolutionAlgorithm
def Fun(D, sol):
val = 0.0
for i in range(D):
val = val + sol[i] * sol[i]
return val
for i in range(10):
Algorithm = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.0, 2.0, Fun)
Best = Algorithm.run()
print(Best)
# 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.modified import SelfAdaptiveDifferentialEvolutionAlgorithm
for i in range(10):
Algorithm = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.1, 'sphere')
Best = Algorithm.run()
print(Best)
logging.basicConfig()
logger = logging.getLogger('examples')
logger.setLevel('INFO')
# For reproducive results
random.seed(1234)
class MyBenchmark(object):
def __init__(self):
self.Lower = -11
self.Upper = 11
def function(self):
def evaluate(D, sol):
val = 0.0
for i in range(D):
val = val + sol[i] * sol[i]
return val
return evaluate
for i in range(10):
Algorithm = SelfAdaptiveDifferentialEvolutionAlgorithm(
10, 40, 10000, 0.5, 0.9, 0.1, MyBenchmark())
Best = Algorithm.run()
logger.info(Best)