def test_griewank_works_fine(self):
es_griewank = EvolutionStrategyMp1(mu=30,
k=25,
c_a=1.5,
c_r=0.5,
seed=self.seed)
es_griewankc = EvolutionStrategyMp1(mu=30,
k=25,
c_a=1.5,
c_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, es_griewank, es_griewankc)
def test_custom_works_fine(self):
es_custom = EvolutionStrategyMp1(mu=45,
k=50,
c_a=1.1,
c_r=0.5,
seed=self.seed)
es_customc = EvolutionStrategyMp1(mu=45,
k=50,
c_a=1.1,
c_r=0.5,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, es_custom, es_customc,
MyBenchmark())
def simple_example(runs=10):
for i in range(runs):
algo = EvolutionStrategyMp1(D=50,
nFES=50000,
seed=None,
benchmark=MyBenchmark())
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def test_griewank_works_fine(self):
es_griewank = EvolutionStrategyMp1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
mu=30,
k=25,
c_a=1.5,
c_r=0.5,
benchmark=Griewank(),
seed=self.seed)
es_griewankc = EvolutionStrategyMp1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
mu=30,
k=25,
c_a=1.5,
c_r=0.5,
benchmark=Griewank(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, es_griewank, es_griewankc)
def test_custom_works_fine(self):
es_custom = EvolutionStrategyMp1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
mu=45,
k=50,
c_a=1.1,
c_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
es_customc = EvolutionStrategyMp1(D=self.D,
nFES=self.nFES,
nGEN=self.nGEN,
mu=45,
k=50,
c_a=1.1,
c_r=0.5,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, es_custom, es_customc)
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = EvolutionStrategyMp1(mu=65,
k=25,
c_a=1.5,
c_r=0.25,
seed=None,
task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
def logging_example():
task = TaskConvPrint(D=50, nFES=50000, nGEN=50000, benchmark=MyBenchmark())
algo = EvolutionStrategyMp1(mu=50,
k=25,
c_a=1.5,
c_r=0.25,
seed=None,
task=task)
best = algo.run()
logger.info('nFES:%s nGEN:%s\n%s %s' %
(task.Evals, task.Iters, best[0], best[1]))
# encoding=utf8
# 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 EvolutionStrategyMp1
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 = EvolutionStrategyMp1()
best = algo.run(task=task)
print('%s -> %f' % (best[0].x, best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
def setUp(self): self.D = 40 self.es_custom = EvolutionStrategyMp1(D=self.D, nFES=1000, mu=45, k=50, c_a=1.1, c_r=0.5, benchmark=MyBenchmark()) self.es_griewank = EvolutionStrategyMp1(D=self.D, nFES=1000, mu=30, k=25, c_a=1.5, c_r=0.5, benchmark=Griewank())