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 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 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 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 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