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 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 get_APFDc():
#moea and greedy
for SIR_name in GLOB.TEST_PGM:
directory = GLOB.RESULT_DIRECTORY + SIR_name + '/'
for version in range(GLOB.NUM_VERSIONS[SIR_name]):
evaluator = veval.VEval(SIR_name,
version + 1,
GLOB.NUM_TESTCASES[SIR_name],
MOEA=False)
for MOEA in GLOB.TRY_ALGORITHM + GLOB.TRY_GALGORITHM:
sequences = []
APFDc = []
for i in range(GLOB.TRIALS_PER_VERSION):
fname = 'seq_' + SIR_name + '_version' + str(
version + 1) + '_' + MOEA + '_trial' + str(i) + '.csv'
with open(directory + fname, 'r') as f:
rdr = csv.reader(f)
for line in rdr:
for j in range(len(line)):
line[j] = int(line[j])
sequences.append(line)
if MOEA in GLOB.TRY_GALGORITHM:
break
for seq in sequences:
APFDc.append(evaluator.eval(seq))
mean = sum(APFDc) / len(APFDc)
std = np.array(APFDc).std()
maximum = max(APFDc)
fname = 'APFDc_' + SIR_name + '_version' + str(
version + 1) + '_' + MOEA + '.csv'
with open(GLOB.RESULT_DIRECTORY + 'Indicator/' + fname,
'w',
newline='') as f:
wr = csv.writer(f)
wr.writerow(['mean', 'std', 'max'])
wr.writerow([mean, std, maximum])
wr.writerow(APFDc)
print('finish to write', fname)
#hybrid
for SIR_name in GLOB.TEST_PGM:
directory = GLOB.RESULT_DIRECTORY + SIR_name + '_hybrid/'
for version in range(GLOB.NUM_VERSIONS[SIR_name]):
evaluator = veval.VEval(SIR_name,
version + 1,
GLOB.NUM_TESTCASES[SIR_name],
MOEA=False)
for MOEA in GLOB.TRY_ALGORITHM:
sequences = []
APFDc = []
for i in range(GLOB.TRIALS_PER_VERSION):
fname = 'seq_' + SIR_name + '_version' + str(
version + 1) + '_' + MOEA + '_trial' + str(i) + '.csv'
with open(directory + fname, 'r') as f:
rdr = csv.reader(f)
for line in rdr:
for j in range(len(line)):
line[j] = int(line[j])
sequences.append(line)
if MOEA in GLOB.TRY_GALGORITHM:
break
for seq in sequences:
APFDc.append(evaluator.eval(seq))
mean = sum(APFDc) / len(APFDc)
std = np.array(APFDc).std()
maximum = max(APFDc)
fname = 'APFDc_' + SIR_name + '_version' + str(
version + 1) + '_' + MOEA + '_hybrid.csv'
with open(GLOB.RESULT_DIRECTORY + 'Indicator/' + fname,
'w',
newline='') as f:
wr = csv.writer(f)
wr.writerow(['mean', 'std', 'max'])
wr.writerow([mean, std, maximum])
wr.writerow(APFDc)
print('finish to write', fname)