def testRestart(self):
fitness = FitnessNapsack()
ilog = pv.ImageLog()
restart_dir = ilog.dir
#fitness.solve()
alg = ga.GeneticAlgorithm(fitness,[ga.GARanking(100)],n_processes=1)
result = alg.optimize(max_iter=500,ilog=ilog)
time.sleep(2)
#print "Restarting..."
ilog = pv.ImageLog()
alg = ga.GeneticAlgorithm(fitness,[ga.GARanking(100)],n_processes=1)
result = alg.optimize(max_iter=1000,ilog=ilog,restart_dir=restart_dir)
def testGAUnitRect(self):
#print "Running unitrect test"
alg = ga.GeneticAlgorithm(fitnessUnitRect, [
ga.GAUnitRect2(
min_width=0.05, min_height=0.05, max_height=1.0, max_width=1.0)
],
population_size=20,
n_processes=4)
_ = alg.optimize(max_iter=1000, callback=unitRectCallback)
# use True and False to toggle between running GA and loading archived results
if True:
# Check the fitness function
print myFitness(8, 2)
assert abs(myFitness(8, 2) + 0.63) < 0.01
# Here are the three arguments
args = [ga.GAFloat(0, 10), ga.GAFloat(0, 10)]
# no keyword arguments this time
kwargs = {}
ilog = pv.ImageLog()
ga_test = ga.GeneticAlgorithm(myFitness, args, kwargs, n_processes='AUTO')
print 'running ga'
result = ga_test.optimize(max_iter=2000, callback=callback, display=True)
print "Best Score =", result[0]
print "Best args =", result[1]
print "Best kwargs =", result[2]
else:
# load the archived result
# look in the "/tmp" directory for a log of the run in a *_pyvis directory
# pkl files document the result
# this directory can also be used to restart a GA run if there was a problem
def testGAUnitVector(self):
#print "Running unitrect test"
alg = ga.GeneticAlgorithm(fitnessUnitVector, [ga.GAUnitVector(9)],
population_size=20,
n_processes=1)
_ = alg.optimize(max_iter=10000)
def testNapsack(self):
fitness = FitnessNapsack()
#fitness.solve()
alg = ga.GeneticAlgorithm(fitness, [ga.GARanking(100)], n_processes=1)
result = alg.optimize(max_iter=1000)
def test2DSurfaceIntSP(self):
alg = ga.GeneticAlgorithm(
FitnessInt(), [ga.GAInteger(0, 1000),
ga.GAInteger(0, 1000)],
n_processes=1)
_ = alg.optimize(max_iter=1000)
def test2DSurfaceFloatSP(self):
alg = ga.GeneticAlgorithm(
Fitness(), [ga.GAFloat(0.0, 10.0),
ga.GAFloat(0.0, 10.0)],
n_processes=1)
_ = alg.optimize(max_iter=5000, callback=callback)
def test2DSurfaceFloatMP(self):
alg = ga.GeneticAlgorithm(
Fitness(), [ga.GAFloat(0.0, 10.0),
ga.GAFloat(0.0, 10.0)],
n_processes=4)
_ = alg.optimize(max_iter=1000)
def dtestNapsack(self):
fitness = FitnessNapsack()
#fitness.solve()
alg = ga.GeneticAlgorithm(FitnessNapsack(), [ga.GARanking(100)],
n_processes=1)
score, args, kwargs = alg.optimize(max_iter=1000)
