def messengerFullLoop():
while True:
problem = MessFull()
logger().info(problem.name + ' de + cmaes c++')
ret = minimize(problem.fun,
bounds=problem.bounds,
num_retries=60000,
value_limit=10.0,
logger=logger())
def messengerFullLoop():
while True:
problem = MessFull()
logger().info(problem.name + ' de + cmaes c++')
minimize(problem.fun,
bounds=problem.bounds,
num_retries=50000,
value_limit=12.0,
logger=logger(),
optimizer=de_cma(1500))
def test_coordinated_retry():
numRuns = 10
min_evals = 1500
algo = de_cma_pyg(min_evals)
#algo = pyg_de_cma(min_evals)
#algo = de_pyg_cma(min_evals)
fac = 1.005
_test_optimizer(algo,
Gtoc1(),
num_retries=10000,
num=numRuns,
value_limit=-300000.0,
stop_val=-1581950 / fac)
_test_optimizer(algo,
Cassini1(),
num_retries=4000,
num=numRuns,
value_limit=20.0,
stop_val=4.9307 * fac)
_test_optimizer(algo,
Cassini2(),
num_retries=6000,
num=numRuns,
value_limit=20.0,
stop_val=8.383 * fac)
_test_optimizer(algo,
Messenger(),
num_retries=8000,
num=numRuns,
value_limit=20.0,
stop_val=8.63 * fac)
_test_optimizer(algo,
Rosetta(),
num_retries=4000,
num=numRuns,
value_limit=20.0,
stop_val=1.3433 * fac)
_test_optimizer(algo,
Sagas(),
num_retries=4000,
num=numRuns,
value_limit=100.0,
stop_val=18.187 * fac)
_test_optimizer(algo,
Tandem(5),
num_retries=20000,
num=numRuns,
value_limit=-300.0,
stop_val=-1500.6 / fac)
_test_optimizer(algo,
MessFull(),
num_retries=50000,
num=numRuns,
value_limit=12.0,
stop_val=1.959 * fac)
def messengerFullLoop(opt, num=1, log=logger()):
for i in range(num):
problem = MessFull()
log.info(problem.name + ' ' + opt.name)
name = str(i + 1) + ' ' + problem.name if num > 1 else problem.name
advretry.minimize_plot(name,
opt,
problem.fun,
problem.bounds,
12.0,
12.0,
50000,
logger=log)
def main():
numRuns = 100
min_evals = 1500
_test_optimizer(de_cma(min_evals),
Gtoc1(),
num_retries=10000,
num=numRuns,
value_limit=-300000.0,
stop_val=-1581949)
_test_optimizer(de_cma(min_evals),
Cassini1(),
num_retries=4000,
num=numRuns,
value_limit=20.0,
stop_val=4.93075)
_test_optimizer(de_cma(min_evals),
Cassini2(),
num_retries=6000,
num=numRuns,
value_limit=20.0,
stop_val=8.38305)
_test_optimizer(de_cma(min_evals),
Messenger(),
num_retries=8000,
num=numRuns,
value_limit=20.0,
stop_val=8.62995)
_test_optimizer(de_cma(min_evals),
Rosetta(),
num_retries=4000,
num=numRuns,
value_limit=20.0,
stop_val=1.34335)
_test_optimizer(de_cma(min_evals),
Sagas(),
num_retries=4000,
num=numRuns,
value_limit=100.0,
stop_val=18.188)
_test_optimizer(de_cma(min_evals),
Tandem(5),
num_retries=20000,
num=numRuns,
value_limit=-300.0,
stop_val=-1500)
_test_optimizer(de_cma(min_evals),
MessFull(),
num_retries=50000,
num=numRuns,
value_limit=12.0,
stop_val=1.960)
def test_archipelago():
max_evals = 500000
popsize = 32
gen = int(max_evals / popsize + 1)
algo = pg.algorithm(pg.de1220(gen=gen))
fac = 1.005
_test_archipelago(algo, Cassini1(), num=400, stop_val=4.9307 * fac)
_test_archipelago(algo, Cassini2(), num=600, stop_val=8.383 * fac)
_test_archipelago(algo, Gtoc1(), num=1000, stop_val=-1581950 / fac)
_test_archipelago(algo, Messenger(), num=800, stop_val=8.63 * fac)
_test_archipelago(algo, Rosetta(), num=400, stop_val=1.3433 * fac)
_test_archipelago(algo, Sagas(), num=400, stop_val=18.187 * fac)
_test_archipelago(algo, Tandem(5), num=2000, stop_val=-1500.6 / fac)
_test_archipelago(algo, MessFull(), num=5000, stop_val=1.959 * fac)
# Copyright (c) Dietmar Wolz.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory.
import math
from fcmaes.astro import MessFull, Messenger, Gtoc1, Cassini1, Cassini2, Rosetta, Tandem, Sagas
from fcmaes.optimizer import logger, de_cma, da_cma, Cma_cpp, De_cpp, Da_cpp, Dual_annealing, Differential_evolution
from fcmaes.retry import minimize
problems = [Cassini1(), Cassini2(), Rosetta(), Tandem(5), Messenger(), Gtoc1(), MessFull(), Sagas()]
max_evals = 50000
algos = [de_cma(max_evals), da_cma(max_evals), Cma_cpp(max_evals), De_cpp(max_evals),
Da_cpp(max_evals), Dual_annealing(max_evals), Differential_evolution(max_evals)]
def test_all(num_retries = 10000, num = 10):
for problem in problems:
for algo in algos:
_test_optimizer(algo, problem, num_retries, num)
def _test_optimizer(opt, problem, num_retries = 32, num = 10):
log = logger()
log.info(problem.name + ' ' + opt.name)
for _ in range(num):
ret = minimize(problem.fun, problem.bounds, math.inf, num_retries, log, optimizer=opt)
def main():
test_all()