def new_crossover(CA, DA, cr, mr, dim):
ratio = GLOB.CA_DA_RATIO
new_pop = []
if len(CA + DA) < 2:
new_pop += GA.initial_genes(dim, GLOB.POP)
return new_pop
elif len(CA) < 2 or len(DA) < 2:
new_pop += GA.crossover(CA + DA, mr=mr)
return new_pop
for i in range(GLOB.POP):
child = None
# consider crossover rate
if random.random() < cr:
p1, p2 = None, None
# randomly choose parents between CA and DA
rand_num = random.random()
if rand_num < ratio * ratio:
p1, p2 = random.sample(CA, 2)
elif rand_num < 2 * ratio - ratio * ratio:
if random.random() < 0.5:
p1 = random.sample(CA, 1)[0]
p2 = random.sample(DA, 1)[0]
else:
p1 = random.sample(DA, 1)[0]
p2 = random.sample(CA, 1)[0]
else:
p1, p2 = random.sample(DA, 2)
child = GA.PMX(p1, p2)
else:
seq = random.sample(CA + DA, 1)[0].get_seq().copy()
child = GA.Gene_Info(seq)
# mutation
if random.random() < mr:
seq = child.get_seq()
idx1, idx2 = random.sample(list(seq), 2)
seq[idx2], seq[idx1] = seq[idx1], seq[idx2]
#check overlap
overlapped = False
for parent in CA + DA:
if (child.get_seq() == parent.get_seq()).all():
overlapped = True
break
if overlapped: # if overlap, do not add child at new_pop
continue
new_pop.append(child)
return new_pop
#run_TAEA('sed',1,360)
def new_crossover(CA,DA):
ratio = GLOB.CA_DA_RATIO
new_pop = []
for i in range(GLOB.POP):
p1,p2 = None,None
if len(DA) != 0:
rand_num = random.random()
if rand_num < ratio*ratio:
p1,p2 = random.sample(CA,2)
elif rand_num < 2*ratio-ratio*ratio:
p1 = random.sample(CA,1)[0]
p2 = random.sample(DA,1)[0]
else:
p1,p2 = random.sample(DA,2)
else:
p1,p2 = random.sample(CA,2)
child = GA.PMX(p1,p2)
new_pop.append(child)
return new_pop