def _do(self, Hm, n_select, n_parents=1, **kwargs):
algorithm = kwargs['algorithm']
n_pop = len(Hm) // 2
_, rank = NonDominatedSorting().do(Hm.get('F'), return_rank=True)
Pc = (rank[:n_pop] == 0).sum() / len(Hm)
Pd = (rank[n_pop:] == 0).sum() / len(Hm)
PC = len(algorithm.opt) / n_pop
# number of random individuals needed
n_random = n_select * n_parents * self.pressure
n_perms = math.ceil(n_random / n_pop)
# get random permutations and reshape them
P = random_permuations(n_perms, n_pop)[:n_random]
P = np.reshape(P, (n_select * n_parents, self.pressure))
if Pc <= Pd:
# Choose from DA
P[::n_parents, :] += n_pop
pf = np.random.random(n_select)
P[1::2, 1][pf >= PC] += n_pop
# compare using tournament function
S = self.f_comp(Hm, P, **kwargs)
return np.reshape(S, (n_select, n_parents))
def _do(self, pop, n_select, n_parents, **kwargs):
# number of random individuals needed
n_random = n_select * n_parents
# number of permutations needed
n_perms = math.ceil(n_random / len(pop))
# get random permutations and reshape them
P = random_permuations(n_perms, len(pop))[:n_random]
return np.reshape(P, (n_select, n_parents))
def _do(self, pop, n_select, n_parents=1, **kwargs):
# number of random individuals needed
n_random = n_select * n_parents * self.pressure
# number of permutations needed
n_perms = math.ceil(n_random / len(pop))
# get random permutations and reshape them
P = random_permuations(n_perms, len(pop))[:n_random]
P = np.reshape(P, (n_select * n_parents, self.pressure))
# compare using tournament function
S = self.f_comp(pop, P, **kwargs)
return np.reshape(S, (n_select, n_parents))
