def get_ref():
reference = []
for l in GLOB.reference:
gene = GA.Gene_Info(None)
gene.set_eval = np.array(l)
reference.append(gene)
return reference
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 read_file(SIR_name, version):
'''
Read files in Result directory and get data
return data by each MOEA and trial
return reference points for the specific SIR program and version
data: dictionary, key = MOEA value = 2D array.
first dimension for each trial
second dimension for data
ex) dict['NSGA2'] = [[pt1,pt2, ...], [pt100,pt101, ...], ...]
each pt = [evaluation metric1, metric2, metric3, ...]
ref_pt: array.
ex) ref_pt = [pt1, pt2, pt3, ...]
each pt = [evaluation metric1, metric2, metric3, ...]
:param SIR_name: String, SIR program name
:param version: int, version of SIR program
:return:
'''
directory = GLOB.RESULT_DIRECTORY + SIR_name + '/'
data = dict()
ref_pt = []
for MOEA in GLOB.TRY_ALGORITHM:
data_moea = [[] for i in range(GLOB.TRIALS_PER_VERSION)]
for i in range(GLOB.TRIALS_PER_VERSION):
fname = 'eval_' + SIR_name + '_version' + str(version) + '_' + MOEA + '_trial' + str(i) + '.csv'
with open(directory + fname, 'r') as f:
f.readline()
rdr = csv.reader(f)
for line in rdr:
for j in range(len(line)):
line[j] = float(line[j])
data_moea[i].append(np.array(line))
gene = GA.Gene_Info(None)
gene.set_eval(np.array(line))
ref_pt.append(gene)
ref_pt = GA.get_pareto(ref_pt)[0] #update reference point
data[MOEA] = data_moea
for i in range(len(ref_pt)):
ref_pt[i] = ref_pt[i].get_eval()
return data, ref_pt