def optimFunc(individual):
# for i in range(1000):
f3 = benchmarks.rastrigin(individual)
f1, f2 = benchmarks.zdt1(individual)
out = (f1, f2) + f3
# print os.getpid()
return out
def ackley_arg0(sol):
X,Y = sol[0], sol[1]
Z = np.zeros(X.shape)
for i in xrange(X.shape[0]):
Z[i] = benchmarks.rastrigin((X[i],Y[i]))[0]
return Z
def ackley_arg0(sol):
X, Y = sol[0], sol[1]
Z = np.zeros(X.shape)
for i in range(X.shape[0]):
Z[i] = benchmarks.rastrigin((X[i], Y[i]))[0]
return Z
def main():
numpy.random.seed(64)
# The CMA-ES algorithm
strategy = cma.Strategy(centroid=[5.0] * N, sigma=3.0, lambda_=20 * N)
toolbox.register("generate", strategy.generate, creator.Individual)
toolbox.register("update", strategy.update)
halloffame = tools.HallOfFame(1)
halloffame_array = []
C_array = []
centroid_array = []
for gen in range(NGEN):
# 新たな世代の個体群を生成
population = toolbox.generate()
# 個体群の評価
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# 個体群の評価から次世代の計算のためのパラメタ更新
toolbox.update(population)
# hall-of-fameの更新
halloffame.update(population)
halloffame_array.append(halloffame[0])
C_array.append(strategy.C)
centroid_array.append(strategy.centroid)
# 計算結果を描画
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.patches import Ellipse
plt.ion()
fig = plt.figure()
ax = fig.add_subplot(111)
X = numpy.arange(-5.12, 5.12, 0.1)
Y = numpy.arange(-5.12, 5.12, 0.1)
X, Y = numpy.meshgrid(X, Y)
Z = [[benchmarks.rastrigin((x, y))[0] for x, y in zip(xx, yy)]
for xx, yy in zip(X, Y)]
ax.imshow(Z, cmap=cm.jet, extent=[-5.12, 5.12, -5.12, 5.12])
for x, sigma, xmean in zip(halloffame_array, C_array, centroid_array):
# 多変量分布の分散を楕円で描画
Darray, Bmat = numpy.linalg.eigh(sigma)
ax.add_artist(
Ellipse((xmean[0], xmean[1]),
numpy.sqrt(Darray[0]),
numpy.sqrt(Darray[1]),
numpy.arctan2(Bmat[1, 0], Bmat[0, 0]) * 180 / numpy.pi,
color="g",
alpha=0.7))
ax.plot([x[0]], [x[1]], c='r', marker='o')
ax.axis([-5.12, 5.12, -5.12, 5.12])
plt.draw()
plt.show(block=True)
def rastrigin_arg_vari(sol):
#D1,D2,D3,D4,D5,D6,D7,D8,D9,D10 = sol[0], sol[1], sol[2], sol[3], sol[4], sol[5], sol[6], sol[7], sol[8], sol[9]
Z = np.zeros(sol.shape[0])
#print sol.shape[0]
for i in xrange(sol.shape[0]):
#print sol[i]
Z[i] = benchmarks.rastrigin(sol[i])[0]
#print Z[i]
return Z
def __init__(self, problem_idx, value_threshold):
SyntheticEnv.__init__(self, problem_idx)
self.name = 'synthetic_rastrigin'
if problem_idx == 0:
self.dim_x = 3
self.feasible_action_value_threshold = -10
elif problem_idx == 1:
self.dim_x = 10
self.feasible_action_value_threshold = value_threshold
elif problem_idx == 2:
self.dim_x = 20
self.feasible_action_value_threshold = -100
self.feasible_reward = 100
self.reward_function = lambda sol: -benchmarks.rastrigin(sol)[0]
def get_objective_function(sol):
if obj_fcn == 'shekel':
return shekel(sol, A, C)[0]
elif obj_fcn == 'schwefel':
return -benchmarks.schwefel(sol)[0]
elif obj_fcn == 'griewank':
return -benchmarks.griewank(sol)[0]
elif obj_fcn == 'rastrigin':
return -benchmarks.rastrigin(sol)[0]
elif obj_fcn == 'ackley':
return -benchmarks.ackley(sol)[0]
elif obj_fcn == 'rosenbrock':
return -benchmarks.rosenbrock(sol)[0]
elif obj_fcn == 'schaffer':
return -benchmarks.schaffer(sol)[0]
else:
print "wrong function name"
sys.exit(-1)
def rastrigin_arg0(sol):
return benchmarks.rastrigin(sol)[0]
def rastrigin_arg0(sol):
return benchmarks.rastrigin(sol)[0]
def MOFitnessFunc(part):
code = 0 if is_valid(part) else 1
fitValues = (benchmarks.sphere(part)[0], benchmarks.rastrigin(part)[0])
#fitValues = benchmarks.fonseca(part)
return fitValues, array([code]), array([0]), array([1.0]) ## cost always 1
def rastrigin_arg0(sol):
return benchmarks.rastrigin(convertArrToFloat(sol))
def final(x):
a = benchmarks.rastrigin(x)
print "rastrigin: ",a
return (a > 1)
def calFit(self, ind):
return benchmarks.rastrigin(ind)[0]
def evalBenchmark(individual):
return benchmarks.rastrigin(individual)
def evaluate(self, x):
return rastrigin(x)[0]
def rastrigin_on_cube(u: [float]) -> float:
# https://deap.readthedocs.io/en/master/api/benchmarks.html#deap.benchmarks.rastrigin
u_squished = [10.24 * (ui**1.1 - 0.5) for ui in u]
return 0.01 * benchmarks.rastrigin(u_squished)[0]
def rastrigin(x):
return benchmarks.rastrigin(x)[0]
def objective(X):
X = np.atleast_2d(X)
return np.array([benchmarks.rastrigin(x)[0] for x in X])
