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)
cx, cy, sigma = 5.0, 9.0, 2.0
dist2 = (x - cx)**2 + (y - cy)**2
scale = 1.0 / (2 * np.pi * sigma**2)
val += scale * np.exp(-dist2 / (2 * sigma**2))
return -val
# 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:
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)