def one_point(p1_chbits, p2_chbits):
# select the random point
random_point = random.randint(1, len(p1_chbits) - 1)
c1 = p1_chbits[0:random_point] + p2_chbits[
random_point:len(p2_chbits)]
c2 = p2_chbits[0:random_point] + p1_chbits[
random_point:len(p2_chbits)]
# logging
logger1.debug("<<<one point mutation>>>")
logger1.debug("chosen_point: {}".format(random_point))
return c1, c2
def bit_flip(chromosome_bits):
list_length = len(chromosome_bits)
random_list = list(range(0, list_length))
flip_bit = random.sample(random_list, 1)[0]
old_bit_value = chromosome_bits[flip_bit]
if old_bit_value == 0:
chromosome_bits[flip_bit] = 1
elif old_bit_value == 1:
chromosome_bits[flip_bit] = 0
# logging
logger1.debug("<<<bit_flip>>>")
logger1.debug("flipped_bit: {}".format(flip_bit))
return chromosome_bits
def uniform(p1_chbits, p2_chbits):
random_list = [0, 1]
c1 = p1_chbits[:]
c2 = p2_chbits[:]
# create for logging
filped_points = []
for i, cell in enumerate(c1):
is_filp = random.sample(random_list, 1)[0]
if is_filp:
# logging purpose
filped_points.append(i)
c1[i], c2[i] = c2[i], c1[i]
logger1.debug("<<<uniform>>>")
logger1.debug("flipped_points: {}".format(filped_points))
return c1, c2
def roulette_wheel_selection(compare_tuple_list):
def get_parent_index(solution_probability_list, solution_list):
chosen_parent_index = bisect.bisect(solution_probability_list,
random.random())
return chosen_parent_index
#:::roulette_wheel_selection:::
sorted_compare_tuple_list = sorted(compare_tuple_list,
key=lambda x: x[1],
reverse=True)
unzip_sorted_compare_tuple_list = [
xy for xy in zip(*sorted_compare_tuple_list)
]
solution_list = unzip_sorted_compare_tuple_list[0]
solution_fitness_list = unzip_sorted_compare_tuple_list[1]
# convert the fitness to the possibility of being chosen eg: [0.4, 0.3, 0.2, 0.1]
solution_probability_list = [
float("{:.3f}".format(x / sum(solution_fitness_list)))
for x in solution_fitness_list
]
# [0.4, 0.3, 0.2, 0.1] -> [0.4, 0.7, 0.9, 1.0]
for i, x in enumerate(solution_probability_list):
if i > 0:
solution_probability_list[i] += solution_probability_list[
i - 1]
logger1.debug("solution_probability_list:{}".format(
solution_probability_list))
logger1.debug("solution_list:{}".format(
[solution.name for solution in solution_list]))
sp1_index = get_parent_index(solution_probability_list,
solution_list)
sp2_index = get_parent_index(solution_probability_list,
solution_list)
while sp1_index == sp2_index:
sp2_index = get_parent_index(solution_probability_list,
solution_list)
sp1 = solution_list[sp1_index]
sp2 = solution_list[sp2_index]
logger1.debug(
"parent1 selected name:{}, parent2 selected name:{}".format(
sp1.name, sp2.name))
logger1.debug(
"parent1 name: {}, chromosome_bits:{}, fitness:{}, parent2 name: {}, chromosome_bits:{}, fitness:{},"
.format(sp1.name, sp1.chromosome_bits, sp1.fitness, sp2.name,
sp2.chromosome_bits, sp2.fitness))
logger1.debug(":::roulette wheel parent selection complete:::!")
return sp1, sp2
def multi_point(p1_chbits, p2_chbits):
sample_list = list(range(1, len(p1_chbits) - 1))
# [0,1,2,3,4], sample_num = 4, sample_num indicate the num of gap, eg. the first sample_num 1 is the gap
# between 0 and 1
sample_num = random.randint(1, len(sample_list) - 1)
random_points = random.sample(sample_list, sample_num)
cut_point_list = random_points[:]
cut_point_list.extend([0, len(p1_chbits)])
cut_point_list = sorted(cut_point_list)
# cut_point_list = [0,2,3,8], cut_point_list = [2,3,8]
#cut_point_tuple_list [(0, 2), (2, 3), (3, 8)]
cut_point_tuple_list = list(zip(cut_point_list,
cut_point_list[1:]))
print("cut_point_tuple_list", cut_point_tuple_list)
c1 = p1_chbits[:]
c2 = p2_chbits[:]
for i, edge_tuple in enumerate(cut_point_tuple_list):
# only flip those odd parts
if i % 2 == 0:
continue
e0 = edge_tuple[0]
e1 = edge_tuple[1]
c1[e0:e1], c2[e0:e1] = c2[e0:e1], c1[e0:e1]
# logging
logger1.debug("<<<one point mutation>>>")
logger1.debug("chosen_points: {}".format(cut_point_list))
logger1.debug("cut_parts: {}".format(cut_point_tuple_list))
return c1, c2
def random_flip(chromosome_bits):
list_length = len(chromosome_bits)
random_list = list(range(0, list_length))
flip_bits = random.sample(random_list, flip_bit_num)
for flip_bit in flip_bits:
old_bit_value = chromosome_bits[flip_bit]
if old_bit_value == 0:
chromosome_bits[flip_bit] = 1
elif old_bit_value == 1:
chromosome_bits[flip_bit] = 0
# logging
logger1.debug("<<<random_flip>>>")
logger1.debug("flip_bit_num: {}".format(flip_bit_num))
logger1.debug("flipped_bits: {}".format(flip_bits))
return chromosome_bits
def tournament_selection(compare_tuple_list):
sp1_random_list = random.sample(compare_tuple_list, self.TS_K)
sp1_tuple = sorted(sp1_random_list,
key=lambda x: x[1],
reverse=True)[0]
compare_tuple_list.remove(sp1_tuple)
sp2_random_list = random.sample(compare_tuple_list, self.TS_K)
sp2_tuple = sorted(sp2_random_list,
key=lambda x: x[1],
reverse=True)[0]
sp1 = sp1_tuple[0]
sp2 = sp2_tuple[0]
logger1.debug("chosen_K:{}".format(self.TS_K))
logger1.debug(
"parent1 name: {}, chromosome_bits:{}, fitness:{}, parent2 name: {}, chromosome_bits:{}, fitness:{},"
.format(sp1.name, sp1.chromosome_bits, sp1.fitness, sp2.name,
sp2.chromosome_bits, sp2.fitness))
logger1.debug(":::tournament parent selection complete:::!")
return sp1, sp2
def __call__(self, parents_list):
#:::__call__
temp_offsprings_pool = []
population_now = len(parents_list)
# using fitness/shared_fitness to choose parent
if self.parent_choose == 'f':
compare_tuple_list = [(parent, parent.fitness)
for parent in parents_list]
elif self.parent_choose == 's_f':
compare_tuple_list = [(parent, parent.shared_fitness)
for parent in parents_list]
while population_now <= self.max_population_num - 2:
compare_tuple_list_copy = compare_tuple_list.copy()
logger1.debug(
"===============START PARENT SELECTION===============")
sp1, sp2 = self.parent_selection(compare_tuple_list_copy)
sp1_chbits = sp1.chromosome_bits
sp2_chbits = sp2.chromosome_bits
logger1.debug(
"===============PARENT SELECTION END=================")
# cross_over
logger1.debug(
"===============CROSS OVER START=====================")
cross_over = CrossOver(self.parameter_dict)
c1_chbits, c2_chbits = cross_over(sp1_chbits, sp2_chbits)
logger1.debug(
"===============CROSS OVER END=======================")
# mutation
logger1.debug(
"===============MUTATION START=======================")
mutation = Mutation(self.parameter_dict)
is_mutation = mutation.is_mutation()
if is_mutation:
c1_chbits = mutation(c1_chbits)
c2_chbits = mutation(c2_chbits)
logger1.debug(
"===============MUTATION END=========================")
# create solution object
c1 = Solution()
c2 = Solution()
c1.chromosome_bits = c1_chbits
c2.chromosome_bits = c2_chbits
temp_offsprings_pool.append(c1)
temp_offsprings_pool.append(c2)
# count entire population
population_now += 2
logger1.debug(
"child1 created name:{}, child2 created name:{}".format(
c1.name, c2.name))
if is_mutation:
logger1.info(
"Mutation happened!! child1_name:{}, child2_name:{}".
format(c1.name, c2.name))
logger1.debug(
"child1 chromosome_bits:{}, child2 chromosome_bits:{}".format(
c1.chromosome_bits, c2.chromosome_bits))
logger1.debug("Two children have been created!!")
logger1.debug(
"populaton pool is fool now, total solutions number : {}".format(
population_now))
return temp_offsprings_pool