def optimize(bench, algo):
average_mfo = 0
average_de = 0
average_abc = 0
average_pso = 0
average_ba = 0
average_fa = 0
average_ga = 0
for i in np.arange(epoch):
mfo = MothFlameOptimizer(D=dim, NP=pop, nGEN=maxIter, benchmark=bench)
de = DifferentialEvolution(D=dim,
NP=pop,
nGEN=maxIter,
benchmark=bench)
abc = ArtificialBeeColonyAlgorithm(D=dim,
NP=pop,
nFES=maxIter,
benchmark=bench)
pso = ParticleSwarmAlgorithm(D=dim,
NP=pop,
nGEN=maxIter,
benchmark=bench)
ba = BatAlgorithm(D=dim, NP=pop, nFES=maxIter, benchmark=bench)
fa = FireflyAlgorithm(D=dim, NP=pop, nFES=maxIter, benchmark=bench)
ga = GeneticAlgorithm(D=dim, NP=pop, nFES=maxIter, benchmark=bench)
gen, best_de = de.run()
gen, best_mfo = mfo.run()
gen, best_abc = abc.run()
gen, best_pso = pso.run()
gen, best_ba = ba.run()
gen, best_fa = fa.run()
gen, best_ga = ga.run()
average_mfo += best_de / epoch
average_de += best_mfo / epoch
average_abc += best_abc / epoch
average_pso += best_pso / epoch
average_ba += best_ba / epoch
average_fa += best_fa / epoch
average_ga += best_ga / epoch
print(algo, ': DE Average of Bests over', epoch, 'run: ', average_de)
print(algo, ': MFO Average of Bests over', epoch, 'run: ', average_mfo)
print(algo, ': ABC Average of Bests over', epoch, 'run: ', average_abc)
print(algo, ': PSO Average of Bests over', epoch, 'run: ', average_pso)
print(algo, ': BA Average of Bests over', epoch, 'run: ', average_ba)
print(algo, ': FA Average of Bests over', epoch, 'run: ', average_fa)
print(algo, ': GA Average of Bests over', epoch, 'run: ', average_ga)
return [
average_de, average_mfo, average_abc, average_pso, average_ba,
average_fa, average_ga
]
class GATestCase(TestCase):
def setUp(self):
self.ga_custom = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark=MyBenchmark())
self.ga_griewank = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark='griewank')
self.ga_tpcr = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=TwoPointCrossover,
benchmark='griewank')
self.ga_mpcr = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=4,
Crossover=MultiPointCrossover,
benchmark='griewank')
self.ga_crmt = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=CreepMutation,
benchmark='griewank')
def test_custom_works_fine(self):
self.assertTrue(self.ga_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.ga_griewank.run())
def test_two_point_crossover_fine(self):
self.assertTrue(self.ga_tpcr.run())
def test_multi_point_crossover_fine(self):
self.assertTrue(self.ga_mpcr.run())
def test_creep_mutation_fine(self):
self.assertTrue(self.ga_crmt.run())
def simple_example(runs=10):
for i in range(10):
algo = GeneticAlgorithm(D=10,
NP=40,
nFES=100000,
Ts=5,
Mr=0.5,
Cr=0.4,
benchmark=MyBenchmark())
Best = algo.run()
logger.info('%s %s' % (Best[0], Best[1]))
class GATestCase(TestCase):
def setUp(self):
self.ga_custom = GeneticAlgorithm(10, 40, 1000, 4, 0.05, 0.4,
MyBenchmark())
self.ga_griewank = GeneticAlgorithm(10, 40, 1000, 4, 0.05, 0.4,
'griewank')
def test_custom_works_fine(self):
self.assertTrue(self.ga_custom.run())
def test_griewank_works_fine(self):
self.assertTrue(self.ga_griewank.run())
def test_griewank_works_fine(self):
ga_griewank = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
seed=self.seed)
ga_griewankc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_griewank, ga_griewankc)
def test_custom_works_fine(self):
ga_custom = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
seed=self.seed)
ga_customc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_custom, ga_customc,
MyBenchmark())
def test_mutation_urso(self):
ga_crmt = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=MutationUros,
seed=self.seed)
ga_crmtc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=MutationUros,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def test_multi_point_crossover_fine(self):
ga_mpcr = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=4,
Crossover=MultiPointCrossover,
seed=self.seed)
ga_mpcrc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=4,
Crossover=MultiPointCrossover,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_mpcr, ga_mpcrc)
def logging_example():
task = TaskConvPrint(D=10, nFES=50000, nGEN=50000, benchmark=MyBenchmark())
algo = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.2,
Cr=0.5,
Selection=RouletteSelection,
Mutation=MutationUros,
Crossover=CrossoverUros,
seed=None,
task=task)
# algo = GeneticAlgorithm(NP=50, Ts=10, Mr=0.5, Cr=0.5, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
def test_reulete_selection(self):
ga_crmt = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Selection=RouletteSelection,
seed=self.seed)
ga_crmtc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Selection=RouletteSelection,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def test_creep_mutation_fine(self):
ga_crmt = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=CreepMutation,
seed=self.seed)
ga_crmtc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=CreepMutation,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def test_crossover_urso(self):
ga_crmt = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=CrossoverUros,
seed=self.seed)
ga_crmtc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=CrossoverUros,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def test_two_point_crossover_fine_c(self):
ga_tpcr = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=TwoPointCrossover,
seed=self.seed)
ga_tpcrc = GeneticAlgorithm(NP=40,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=TwoPointCrossover,
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_tpcr, ga_tpcrc,
MyBenchmark())
def executeGA(typeBenchmark):
task = StoppingTask(
D=dimensoes,
nFES=numeroAvaliacoes,
optType=tipoOtimizacao,
logger=True,
benchmark=typeBenchmark,
)
algo = GeneticAlgorithm(
NP=tamanhoPopulacao,
Crossover=crossover,
Mutation=mutation,
Cr=taxaCruzamento,
Mr=taxaMutacao,
)
best = algo.run(task=task)
return [task, best[1], best[0]]
def test_custom_works_fine(self):
ga_custom = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark=MyBenchmark(),
seed=self.seed)
ga_customc = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark=MyBenchmark(),
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_custom, ga_customc)
def test_griewank_works_fine(self):
ga_griewank = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark='griewank',
seed=self.seed)
ga_griewankc = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_griewank, ga_griewankc)
def test_creep_mutation_fine(self):
ga_crmt = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=CreepMutation,
benchmark='griewank',
seed=self.seed)
ga_crmtc = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=CreepMutation,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def test_multi_point_crossover_fine(self):
ga_mpcr = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=4,
Crossover=MultiPointCrossover,
benchmark='griewank',
seed=self.seed)
ga_mpcrc = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=4,
Crossover=MultiPointCrossover,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_mpcr, ga_mpcrc)
def test_crossover_urso(self):
ga_crmt = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=CrossoverUros,
benchmark='griewank',
seed=self.seed)
ga_crmtc = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=CrossoverUros,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def setUp(self):
self.ga_custom = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark=MyBenchmark())
self.ga_griewank = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
benchmark='griewank')
self.ga_tpcr = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
Crossover=TwoPointCrossover,
benchmark='griewank')
self.ga_mpcr = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=4,
Crossover=MultiPointCrossover,
benchmark='griewank')
self.ga_crmt = GeneticAlgorithm(D=10,
NP=40,
nFES=1000,
Ts=4,
Mr=0.05,
Cr=0.4,
Mutation=CreepMutation,
benchmark='griewank')
def test_reulete_selection(self):
ga_crmt = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
Selection=RouletteSelection,
benchmark='griewank',
seed=self.seed)
ga_crmtc = GeneticAlgorithm(D=self.D,
NP=40,
nFES=self.nFES,
nGEN=self.nGEN,
Ts=4,
Mr=0.05,
Cr=0.4,
Selection=RouletteSelection,
benchmark='griewank',
seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, ga_crmt, ga_crmtc)
def plot_example():
task = TaskConvPlot(D=50, nFES=50000, nGEN=10000, benchmark=MyBenchmark())
algo = GeneticAlgorithm(NP=40, Ts=5, Mr=0.5, Cr=0.4, task=task)
best = algo.run()
logger.info('%s %s' % (best[0], best[1]))
input('Press [enter] to continue')
# 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
from NiaPy.algorithms.basic import GeneticAlgorithm
from NiaPy.algorithms.basic.ga import UniformCrossover, UniformMutation
from NiaPy.task import StoppingTask
from NiaPy.benchmarks import Sphere
# we will run Genetic Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10, nFES=10000, benchmark=Sphere())
algo = GeneticAlgorithm(NP=100, Crossover=UniformCrossover, Mutation=UniformMutation, Cr=0.45, Mr=0.9)
best = algo.run(task=task)
print('%s -> %s' % (best[0], best[1]))
def setUp(self):
self.ga_custom = GeneticAlgorithm(10, 40, 1000, 4, 0.05, 0.4,
MyBenchmark())
self.ga_griewank = GeneticAlgorithm(10, 40, 1000, 4, 0.05, 0.4,
'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.basic import GeneticAlgorithm
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 = GeneticAlgorithm(10, 40, 10000, 4, 0.05, 0.0, 2.0, Fun)
Best = Algorithm.run()
print(Best.toString())
# 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 GeneticAlgorithm
for i in range(10):
Algorithm = GeneticAlgorithm(10, 40, 10000, 4, 0.05, 0.4, 'sphere')
Best = Algorithm.run()
print(Best)
# 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
from NiaPy.algorithms.basic import GeneticAlgorithm
from NiaPy.algorithms.basic.ga import MutationUros, CrossoverUros
from NiaPy.task.task import StoppingTask, OptimizationType
from NiaPy.benchmarks import Sphere
# we will run Fireworks Algorithm for 5 independent runs
for i in range(5):
task = StoppingTask(D=10,
nFES=4000,
optType=OptimizationType.MINIMIZATION,
benchmark=Sphere())
algo = GeneticAlgorithm(NP=100,
Crossover=CrossoverUros,
Mutation=MutationUros,
Cr=0.45,
Mr=0.9)
best = algo.run(task=task)
print('%s -> %s' % (best[0].x, best[1]))
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
