def objective_function(self, representation):
num_individuals = len(representation)
evaluations = np.zeros((num_individuals, self.num_objs))
for i in range(num_individuals):
evaluations[i] = list(
benchmarks.dtlz2(representation[i], self.num_objs))
return evaluations
def get_function_definition(function_name: str, num_input_dims: int,
num_objectives: int):
domain = []
fitnessfunc = None
d1x_opt = []
if function_name == "ZDT1":
fitnessfunc = lambda ind: benchmarks.zdt1(ind)
num_objectives = 2
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.0, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "ZDT2":
fitnessfunc = lambda ind: benchmarks.zdt2(ind)
num_objectives = 2
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.0, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "ZDT3":
fitnessfunc = lambda ind: benchmarks.zdt3(ind)
num_objectives = 2
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.0, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "ZDT4":
fitnessfunc = lambda ind: benchmarks.zdt4(ind)
num_objectives = 2
domain.append({
'name': "x_{}]".format(1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
for i in range(1, num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (-5.0, 5.0)
})
d1x_opt = np.repeat(0.0, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "ZDT6":
fitnessfunc = lambda ind: benchmarks.zdt6(ind)
num_objectives = 2
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.0, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "KURSAWE":
fitnessfunc = lambda ind: benchmarks.kursawe(ind)
elif function_name == "POLONI":
fitnessfunc = lambda ind: benchmarks.kursawe(ind)
elif function_name == "DTLZ1":
fitnessfunc = lambda ind: benchmarks.dtlz1(ind, num_objectives)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.5, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "DTLZ2":
fitnessfunc = lambda ind: benchmarks.dtlz2(ind, num_objectives)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.5, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "DTLZ3":
fitnessfunc = lambda ind: benchmarks.dtlz3(ind, num_objectives)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.5, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "DTLZ4":
fitnessfunc = lambda ind: benchmarks.dtlz4(ind, num_objectives, 100)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.5, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "DTLZ5":
fitnessfunc = lambda ind: benchmarks.dtlz5(ind, num_objectives)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.5, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "DTLZ6":
fitnessfunc = lambda ind: benchmarks.dtlz6(ind, num_objectives)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.5, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
elif function_name == "DTLZ7":
fitnessfunc = lambda ind: benchmarks.dtlz7(ind, num_objectives)
for i in range(num_input_dims):
domain.append({
'name': "x_{}]".format(i + 1),
'type': 'continuous',
'domain': (0.0, 1.0)
})
d1x_opt = np.repeat(0.0, num_input_dims - 1).reshape([1, -1])
d1x_opt = np.repeat(d1x_opt, 1000, 0)
d1x_opt = np.hstack((np.linspace(0.0, 1.0,
1000).reshape([-1, 1]), d1x_opt))
return domain, fitnessfunc, d1x_opt, num_input_dims, num_objectives
ax.set_ylabel('Memory')
return None
def variate(ind1, ind2, mate, mutate):
c1, c2 = mate(ind1, ind2)
c1 = mutate(c1)
c2 = mutate(c2)
return c1, c2
toolbox.register("attr_float", uniform, BOUND_LOW, BOUND_UP, NDIM)
toolbox.register("individual", tools.initIterate, creator.Individual,
toolbox.attr_float)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", lambda ind: benchmarks.dtlz2(ind, 2))
toolbox.register("mate", tools.cxSimulatedBinary, eta=20.0)
toolbox.register("mutate",
tools.mutPolynomialBounded,
low=0,
up=10000,
eta=20.0,
indpb=1.0 / NDIM)
toolbox.register("select", tools.selNSGA2)
def main(seed=None):
random.seed(seed)
NGEN = 2
MU = 4
color=plot_colors[i % len(plot_colors)])
ax.set_title('$t=$' + str(frame_index))
ax.set_xlabel('RT');ax.set_ylabel('Memory')
return None
def variate(ind1, ind2, mate, mutate):
c1, c2 = mate(ind1, ind2)
c1 = mutate(c1)
c2 = mutate(c2)
return c1, c2
toolbox.register("attr_float", uniform, BOUND_LOW, BOUND_UP, NDIM)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.attr_float)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", lambda ind: benchmarks.dtlz2(ind, 2))
toolbox.register("mate", tools.cxSimulatedBinary, eta=20.0)
toolbox.register("mutate", tools.mutPolynomialBounded, low=0, up=10000, eta=20.0, indpb=1.0/NDIM)
toolbox.register("select", tools.selNSGA2)
def main(seed=None):
random.seed(seed)
NGEN = 2
MU = 4
CXPB = 0.6
pop = toolbox.population(n=MU)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
def evalBenchmark(individual):
return benchmarks.dtlz2(individual, objectives)