def generation(code_type, input_type, index):
utility = Utility()
population = create_population(250)
parents = []
children = []
i = 0
same_count = 0
control_group = []
Pms = 0.8
Pmc = 0.4
Pbs = 0.1
prob_list = []
quality = []
diff = []
option = ''
while i < 5000:
if i == 0:
for ind in population:
parents.append(ind)
pareto = utility.non_dominated(parents)
control_group = pareto[:]
elif i > 0:
new_pareto = utility.non_dominated(children)
pareto.extend(new_pareto)
pareto = utility.non_dominated(pareto)
if len(pareto) > len(children):
reduced = reduceParetoWithSort(pareto, utility)
del pareto[:]
pareto = reduced[:]
parents = clear_parents(parents)
parents = [par for par in children]
children = clear_children(children)
if control_group != pareto:
del control_group[:]
control_group = pareto[:]
same_count = 0
else:
same_count += 1
if same_count >= 40:
# f = open("/Users/sirinsaygili/workspace/seggen/results/"+code_type+"_"+input_type+"_"+str(index)+".txt","w")
# print "%s type,input %s, result at %d. generation" %(code_type, input_type, (i+1))
# reference = "0000000001000000000100001000010000001000000000000"
# for key in pick_better_results(pareto,utility):
# s = ''.join(str(x) for x in key)
# r = [key,windowdiff(reference,s,9)]
# f.write(str(r)+"\n")
# f.write(str("program finished at %d. generation" %(i+1)))
# f.close()
for t in agg_val.items():
print t
break
pareto_quota = random.randint(1, len(pareto))
pop_quota = (len(parents)-pareto_quota)
create_pareto_fitness_dict(population, pareto, utility)
create_population_fitness_dict(population, pareto, pareto_hardness_dict.values(), utility)
pop_probabilities = get_probability_list()
pareto_probabilities = get_pareto_probability_list()
#ga operator: selection (roulette wheel)
mating_pool = roulette_wheel_pop(population, pop_probabilities, pop_quota)
selected_from_pareto = roulette_wheel_pareto(pareto, pareto_probabilities, pareto_quota)
mating_pool.extend(selected_from_pareto)
#ga operator: crossover
for j in range(0,60):
if len(mating_pool) >= 2:
indexes = random.sample(range(len(mating_pool)), 4)
if option == 'C' or 'M1C' or 'M2C':
created = crossover_merge(mating_pool[indexes[2]], mating_pool[indexes[3]])
children.append(created)
crossed = crossover_same_boundary_count(mating_pool[indexes[0]], mating_pool[indexes[1]])
if crossed[0] is True:
children.extend(crossed[1:])
else:
children.extend(crossover_keep_boundary(mating_pool[indexes[0]], mating_pool[indexes[1]]))
else:
children.extend(crossover(mating_pool[indexes[0]], mating_pool[indexes[1]]))
else:
print "sampler larger than %d" % len(mating_pool)
for k in agg_val.items():
print k
break
#ga operator: mutation
rand_indexes = random.sample(set(range(len(children))), 2)
if option == 'M1' or 'M2' or 'M1C' or 'M2C':
mutated_boundary_shift = mutation_boundary_shift(children[rand_indexes[0]], Pbs)
children.append(mutated_boundary_shift)
mutated_boundary_add = mutation_add_boundary(children[rand_indexes[0]], Pbs)
children.append(mutated_boundary_add)
else:
children[rand_indexes[0]] = mutation_Pms(children[rand_indexes[0]], children[rand_indexes[1]], Pms)
mutated_pmc = mutation_Pmc(children[rand_indexes[0]], Pmc)
children.append(mutated_pmc)
agg_val = aggregation(pareto, utility)
if option == 'M2' or 'M2C':
if i == 9:
values = [x for x in agg_val.values()]
mean_val = sum(values) / float(len(values))
prob_list.append(mean_val)
if i>0 and i%10 == 0:
values = [x for x in agg_val.values()]
mean = sum(values) / float(len(values))
if mean < prob_list[-1] and Pbs<=0.8:
Pbs+=0.05
prob_list.append(mean)
if i == 9:
quality.append(calculate_global_quality(parents,utility))
if i>0 and i%10 == 0:
instant = calculate_global_quality(parents,utility)
if instant < quality[-1]:
option = choice(options)
quality.append(instant)
print quality[-1]
print option
if i == 4999:
for t in agg_val.items():
print t
i += 1
def generation(code_type, input_type, index):
utility = Utility()
population = create_population(250)
parents = []
children = []
i = 0
same_count = 0
control = {}
Pms = 0.8
Pmc = 0.4
Pbs = 0.1
prob_list = []
while i < 5000:
if i == 0:
for ind in population:
parents.append(ind)
pareto = utility.weighted_non_dominated(parents)
elif i > 0:
new_pareto = utility.weighted_non_dominated(children)
pareto.extend(new_pareto)
pareto = utility.weighted_non_dominated(pareto)
if len(pareto) > len(children):
reduced = reduceParetoWithSort(pareto, utility)
del pareto[:]
pareto = reduced[:]
parents = clear_parents(parents)
parents = [par for par in children]
children = clear_children(children)
if set(control.items()) != set(get_diff(agg_val,w_agg_val).items()):
control.clear()
control = get_diff(agg_val,w_agg_val)
same_count = 0
else:
same_count += 1
if same_count >= 30:
print "%s type,input %s, result at %d. generation" %(code_type, input_type, (i+1))
f = open("/Users/sirinsaygili/workspace/seggen/results/"+code_type+"_"+input_type+"_"+str(index)+".txt","w")
reference = "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
#reference = "[0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]"
for item,key in zip(control.items(),control.keys()):
s = ''.join(str(x) for x in key)
r = [item,windowdiff(reference,s,9)]
f.write(str(r)+"\n")
f.close()
break
pareto_quota = random.randint(1, len(pareto))
pop_quota = (len(parents)-pareto_quota)
create_pareto_fitness_dict(population, pareto, utility)
create_population_fitness_dict(population, pareto, pareto_hardness_dict.values(), utility)
pop_probabilities = get_probability_list()
pareto_probabilities = get_pareto_probability_list()
#ga operator: selection (roulette wheel)
mating_pool = roulette_wheel_pop(population, pop_probabilities, pop_quota)
selected_from_pareto = roulette_wheel_pareto(pareto, pareto_probabilities, pareto_quota)
mating_pool.extend(selected_from_pareto)
#ga operator: crossover
for j in range(0,60):
if len(mating_pool) >= 2:
indexes = random.sample(range(len(mating_pool)), 2)
#children.extend(crossover(mating_pool[indexes[0]], mating_pool[indexes[1]]))
crossed = crossover_same_boundary_count(mating_pool[indexes[0]], mating_pool[indexes[1]])
if crossed[0] is True:
children.extend(crossed[1:])
else:
children.extend(crossover_keep_boundary(mating_pool[indexes[0]], mating_pool[indexes[1]]))
else:
print "sampler larger than %d" % len(mating_pool)
for k in agg_val.items():
print k
break
#ga operator: mutation
rand_indexes = random.sample(set(range(len(children))), 2)
# children[rand_indexes[0]] = mutation_Pms(children[rand_indexes[0]], children[rand_indexes[1]], Pms)
# mutated_pmc = mutation_Pmc(children[rand_indexes[0]], Pmc)
# children.append(mutated_pmc)
mutated_boundary_shift = mutation_boundary_shift(children[rand_indexes[0]], Pbs)
children.append(mutated_boundary_shift)
mutated_boundary_add = mutation_add_boundary(children[rand_indexes[0]], Pbs)
children.append(mutated_boundary_add)
agg_val = aggregation(pareto, utility)
w_agg_val = weighted_aggregation(pareto, utility)
#print "%d. generation pareto archive size %d" % ((i+1), len(pareto))
if i == 9:
values = [x for x in agg_val.values()]
mean_val = sum(values) / float(len(values))
prob_list.append(mean_val)
if i>0 and i%10 == 0:
values = [x for x in agg_val.values()]
mean = sum(values) / float(len(values))
if mean < prob_list[-1] and Pbs<=0.8:
Pbs+=0.05
# print "average of population %f at %d . generation" % (mean, (i+1))
# print "new Pbs value %f" % Pbs
prob_list.append(mean)
if i == 4999:
for t in agg_val.items():
print t
i += 1
