def generate(self, ind_init):
"""Generate a population of :math:`\lambda` individuals of type
*ind_init* from the current strategy.
:param ind_init: A function object that is able to initialize an
individual from a list.
:returns: A list of individuals with a private attribute :attr:`_ps`.
This last attribute is essential to the update function, it
indicates that the individual is an offspring and the index
of its parent.
"""
arz = numpy.random.randn(self.lambda_, self.dim)
individuals = list()
# Make sure every parent has a parent tag and index
for i, p in enumerate(self.parents):
p._ps = "p", i
# Each parent produce an offspring
if self.lambda_ == self.mu:
for i in range(self.lambda_):
# print "Z", list(arz[i])
individuals.append(ind_init(self.parents[i] + self.sigmas[i] * numpy.dot(self.A[i], arz[i])))
individuals[-1]._ps = "o", i
# Parents producing an offspring are chosen at random from the first front
else:
ndom = tools.sortLogNondominated(self.parents, len(self.parents), first_front_only=True)
for i in range(self.lambda_):
j = numpy.random.randint(0, len(ndom))
_, p_idx = ndom[j]._ps
individuals.append(ind_init(self.parents[p_idx] + self.sigmas[p_idx] * numpy.dot(self.A[p_idx], arz[i])))
individuals[-1]._ps = "o", p_idx
return individuals
def _select(self, candidates):
if len(candidates) <= self.mu:
return candidates, []
pareto_fronts = tools.sortLogNondominated(candidates, len(candidates))
chosen = list()
mid_front = None
not_chosen = list()
# Fill the next population (chosen) with the fronts until there is not enouch space
# When an entire front does not fit in the space left we rely on the hypervolume
# for this front
# The remaining fronts are explicitely not chosen
full = False
for front in pareto_fronts:
if len(chosen) + len(front) <= self.mu and not full:
chosen += front
elif mid_front is None and len(chosen) < self.mu:
mid_front = front
# With this front, we selected enough individuals
full = True
else:
not_chosen += front
# Separate the mid front to accept only k individuals
k = self.mu - len(chosen)
if k > 0:
# reference point is chosen in the complete population
# as the worst in each dimension +1
ref = numpy.array([ind.fitness.wvalues for ind in candidates]) * -1
ref = numpy.max(ref, axis=0) + 1
for i in range(len(mid_front) - k):
idx = self.indicator(mid_front, ref=ref)
not_chosen.append(mid_front.pop(idx))
chosen += mid_front
return chosen, not_chosen
