def generate_mutation_pairs(n):
for i in range(n):
LEN = random.randint(MINLEN, MAXLEN)
g = [random.randint(0, MAXV-1) for i in range(LEN)]
gind = make_individual(g)
if gind.phenotype is None:
continue
h = onepoint_mutate(g[:], min(gind[used_idx], len(g)))
hind = make_individual(h)
if hind.phenotype is None:
continue
yield (gind, hind)
def generate_random_pairs(n):
for i in range(n):
LEN = random.randint(MINLEN, MAXLEN)
g = [random.randint(0, MAXV-1) for i in range(LEN)]
gind = make_individual(g)
if gind.phenotype is None:
continue
h = [random.randint(0, MAXV-1) for i in range(LEN)]
hind = make_individual(h)
if hind.phenotype is None:
continue
yield (gind, hind)
def generate_crossover_pairs(n):
for i in range(n):
LEN = random.randint(MINLEN, MAXLEN)
g = [random.randint(0, MAXV-1) for i in range(LEN)]
gind = make_individual(g)
if gind.phenotype is None:
continue
LEN = random.randint(MINLEN, MAXLEN)
h = [random.randint(0, MAXV-1) for i in range(LEN)]
hind = make_individual(h)
if hind.phenotype is None:
continue
children = xover(gind, hind)
cind0 = make_individual(children[0])
cind1 = make_individual(children[1])
if cind0.phenotype is not None:
yield (gind, cind0)
yield (hind, cind0)
if cind1.phenotype is not None:
yield (gind, cind1)
yield (hind, cind1)
