def test_mo_cma_es():
def distance(feasible_ind, original_ind):
"""A distance function to the feasibility region."""
return sum((f - o)**2 for f, o in zip(feasible_ind, original_ind))
def closest_feasible(individual):
"""A function returning a valid individual from an invalid one."""
feasible_ind = numpy.array(individual)
feasible_ind = numpy.maximum(BOUND_LOW, feasible_ind)
feasible_ind = numpy.minimum(BOUND_UP, feasible_ind)
return feasible_ind
def valid(individual):
"""Determines if the individual is valid or not."""
if any(individual < BOUND_LOW) or any(individual > BOUND_UP):
return False
return True
NDIM = 5
BOUND_LOW, BOUND_UP = 0.0, 1.0
MU, LAMBDA = 10, 10
NGEN = 500
# The MO-CMA-ES algorithm takes a full population as argument
population = [creator.__dict__[INDCLSNAME](x) for x in numpy.random.uniform(BOUND_LOW, BOUND_UP, (MU, NDIM))]
toolbox = base.Toolbox()
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.decorate("evaluate", tools.ClosestValidPenality(valid, closest_feasible, 1.0e-6, distance))
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)
strategy = cma.StrategyMultiObjective(population, sigma=1.0, mu=MU, lambda_=LAMBDA)
toolbox.register("generate", strategy.generate, creator.__dict__[INDCLSNAME])
toolbox.register("update", strategy.update)
for gen in range(NGEN):
# Generate a new population
population = toolbox.generate()
# Evaluate the individuals
fitnesses = toolbox.map(toolbox.evaluate, population)
for ind, fit in zip(population, fitnesses):
ind.fitness.values = fit
# Update the strategy with the evaluated individuals
toolbox.update(population)
hv = hypervolume(strategy.parents, [11.0, 11.0])
assert hv > HV_THRESHOLD, "Hypervolume is lower than expected %f < %f" % (hv, HV_THRESHOLD)
feasible_ind = numpy.minimum(MAX_BOUND, feasible_ind)
return feasible_ind
def valid(individual):
"""Determines if the individual is valid or not."""
if any(individual < MIN_BOUND) or any(individual > MAX_BOUND):
return False
return True
toolbox = base.Toolbox()
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.decorate(
"evaluate",
tools.ClosestValidPenality(valid, closest_feasible, 1.0e-6, distance))
def main():
# The cma module uses the numpy random number generator
# numpy.random.seed(128)
MU, LAMBDA = 10, 10
NGEN = 500
verbose = True
# The MO-CMA-ES algorithm takes a full population as argument
population = [
creator.Individual(x) for x in (numpy.random.uniform(0, 1, (MU, N)))
]
feasible_ind = numpy.array(individual)
feasible_ind = numpy.maximum(MIN_BOUND, feasible_ind)
feasible_ind = numpy.minimum(MAX_BOUND, feasible_ind)
return feasible_ind
def valid(individual):
"""Determines if the individual is valid or not."""
if any(individual < MIN_BOUND) or any(individual > MAX_BOUND):
return False
return True
toolbox = base.Toolbox()
toolbox.register("evaluate", benchmarks.zdt1)
toolbox.decorate("evaluate", tools.ClosestValidPenality(valid, closest_feasible, 1.0e-6, distance))
def main():
# The cma module uses the numpy random number generator
# numpy.random.seed(128)
MU, LAMBDA = 10, 10
NGEN = 500
verbose = True
# The MO-CMA-ES algorithm takes a full population as argument
population = [creator.Individual(x) for x in (numpy.random.uniform(0, 1, (MU, N)))]
for ind in population:
ind.fitness.values = toolbox.evaluate(ind)