def run_SPEA2(SIR_name, version, test_size):
evaluator = evaluation.VEval(SIR_name, version, test_size)
dim = test_size
population = GA.initial_genes(dim, GLOB.POP)
archive = []
population_history = [population]
archive_history = [archive]
if GLOB.DEBUG:
print('0', len(population))
for i in range(GLOB.MAX_IT):
#update
union = population + archive
GA.evaluate(union, evaluator)
get_fitness(union)
archive = select(union)
population = GA.crossover(archive)[GLOB.POP + 1:]
if GLOB.DEBUG:
print(i, len(population))
#save history
population_history.append(population)
archive_history.append(archive)
first_pareto = get_first_pareto(archive) #final result
return first_pareto
def get_fitness(pop):
#get score for each gene
for p in pop:
score = 0
for q in pop:
if GA._is_dominate(q, p):
score += 1
p.update_flag({'score': score})
#get raw fitness for each gene
for p in pop:
raw = 0
for q in pop:
if GA._is_dominate(q, p):
q_score = q.get_flag("score")
raw += q_score
p.update_flag({"raw": raw})
#get density and fitness for each gene
N = math.ceil(math.sqrt(GLOB.POP + GLOB.MAX_ARCHIVE_SPEA2))
for p in pop:
dens = 0
for q in pop: #get distance from others
dist = np.linalg.norm(p.get_eval() - q.get_eval())
q.update_flag({'dist': dist})
sorted_pop = sorted(pop, key=lambda gene: gene.get_flag('dist'))
dens = sorted_pop[N + 1].get_flag('dist')
fitness = dens + p.get_flag('raw')
p.update_flag({'dense': dens, 'fitness': fitness})
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 collect_non_dominated(pop, CA, DA):
def distance(eval1, eval2):
return np.linalg.norm(eval1 - eval2)
new_CA = CA[:]
new_DA = DA[:]
#step1: update CA and DA
for i in range(len(pop)):
p = pop[i]
#check if non dominated
p.update_flag({"non-dominated": True, "dominate-other": False})
for archive in new_CA + new_DA:
if GA._is_dominate(archive, p):
p.update_flag({"non-dominated": False})
break
if not p.get_flag("non-dominated"): #skip if not non-dominated
continue
#check if it dominated other
for archive in new_CA:
if GA._is_dominate(p, archive):
new_CA.remove(archive)
p.update_flag({"dominate-other": True})
for archive in new_DA:
if GA._is_dominate(p, archive):
new_DA.remove(archive)
p.update_flag({"dominate-other": True})
if p.get_flag("dominate-other"): #dominate other, add at CA
new_CA.append(p)
else: #not dominate other, add at DA
new_DA.append(p)
#step2: limit the size of CA and DA
if len(new_CA + new_DA) > GLOB.MAX_ARCHIVE_TAEA:
if len(new_CA) > GLOB.MAX_ARCHIVE_TAEA:
new_DA = []
return
for pop_da in new_DA:
pop_da.update_flag({"len": GLOB.LARGE})
for pop_ca in new_CA:
dist = distance(pop_da.get_eval(), pop_ca.get_eval())
if dist < pop_da.get_flag("len"):
pop_da.update_flag({"len": dist})
#remove some population in DA
new_DA = sorted(new_DA, key=lambda pop: pop.get_flag("len"))
new_DA = new_DA[-GLOB.MAX_ARCHIVE_TAEA + len(new_CA):]
return new_CA, new_DA
def run_NSGA3(input_fname):
modify = modifier.modify_testcase(input_fname)
dim = modify.get_datasize()
reference = get_ref()
population = GA.initial_genes(dim,GLOB.POP)
population_history = [population]
for i in range(GLOB.MAX_IT):
new_pop = GA.crossover(population)
GA.evaluate(new_pop,modify)
pareto = GA.get_pareto(new_pop)
new_pop = select(pareto)
population = new_pop
population_history.append(population)
first_pareto = get_first_pareto(population) #final result
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 get_first_pareto(pop):
'''
get only first pareto
:param pop:
:return:
'''
return GA.get_pareto(pop)[0]
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
def run_NSGA2(SIR_name, version, test_size):
evaluator = evaluation.VEval(SIR_name, version, test_size)
dim = test_size
population = GA.initial_genes(dim, GLOB.POP, version)
if GLOB.DEBUG:
print('0', len(population))
population_history = [population]
for i in range(GLOB.MAX_IT):
new_pop = GA.crossover(population, mr=1 / dim)
GA.evaluate(new_pop, evaluator)
pareto = GA.get_pareto(new_pop)
new_pop = select(pareto)
population = new_pop
population_history.append(population)
if GLOB.DEBUG:
print(i, len(population))
first_pareto = get_first_pareto(population) #final result
return first_pareto
def run_TAEA(SIR_name, version, test_size):
evaluator = evaluation.VEval(SIR_name, version, test_size)
dim = test_size
population = GA.initial_genes(dim, GLOB.POP,version)
population_history = [population]
CA, DA = [], [] #covergence archive, diversity archive
GA.evaluate(population,evaluator)
if GLOB.DEBUG:
print('0', 0)
for i in range(GLOB.MAX_IT):
CA, DA = collect_non_dominated(population,CA,DA)
#print(len(CA), len(DA))
population = new_crossover(CA,DA,GLOB.CROSSOVER_RATE,1/test_size)
GA.evaluate(population,evaluator)
if GLOB.DEBUG:
print(i,len(CA+DA), 'covergence archive size', len(CA), 'divergence archive size', len(DA))
return CA+DA
def run_TAEA(input_fname):
modify = modifier.modify_testcase(input_fname)
dim = modify.get_datasize()
population = GA.initial_genes(dim, GLOB.POP)
population_history = [population]
CA, DA = [], [] #covergence archive, diversity archive
GA.evaluate(population,modify)
for i in range(GLOB.MAX_IT):
collect_non_dominated(population,CA,DA)
new_pop = GA.crossover(CA+DA)
GA.evaluate(new_pop,modify)
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