def crossover(self, other_chromosome, every_other=True):
new_path = list([])
if every_other: # Combines chromosome by alternating allels.
self_index = 0
other_index = 1
my_turn = True
while len(new_path) < len(self.route.vertices) - 1:
if my_turn and self_index < len(
self.route.vertices) - 1:
if self.route.vertices[
self_index].vertex_id not in new_path:
new_path.append(
self.route.vertices[self_index].vertex_id)
my_turn = False
self_index += 1
else:
if other_chromosome.route.vertices[
other_index].vertex_id not in new_path:
new_path.append(
other_chromosome.route.
vertices[other_index].vertex_id)
my_turn = True
if other_index >= len(
other_chromosome.route.vertices) - 2:
my_turn = True
else:
other_index += 1
else: # Splits the two chromosomes down the middle
index = 0
while len(new_path) < len(self.route.vertices) - 1:
if index < len(self.route.vertices) // 2:
if self.route.vertices[
index].vertex_id not in new_path:
new_path.append(
self.route.vertices[index].vertex_id)
index += 1
else:
remaining_vertices = [
vertex for vertex in self.route.vertices
if vertex.vertex_id not in new_path
]
for remainining_vertex in remaining_vertices:
new_path.append(remainining_vertex.vertex_id)
new_route = Route(self.route.graph)
new_route.walk_complete_path(new_path)
resultant_chromosome = TravelingSalesman.GeneticAlgorithm.Chromosome(
None, new_route)
return resultant_chromosome
def initialize_population(self):
city_range = list(range(1, 1 + len(self.graph.vertices)))
for chromosome_index in range(self.population_size):
random_city_order = deepcopy(city_range)
random.shuffle(random_city_order)
random_route = Route(graph)
random_route.walk_complete_path(random_city_order)
chromosome = TravelingSalesman.GeneticAlgorithm.Chromosome(
chromosome_index, random_route)
self.population.append(chromosome)
self.population = np.array(self.population)
def initialize_population(self):
city_range = list(range(1, 1 + len(self.graph.vertices)))
for chromosome_index in range(self.population_size):
random_city_order = deepcopy(city_range, memo={})
random.shuffle(random_city_order)
random_route = Route(self.graph)
random_route.walk_complete_path(random_city_order)
chromosome = GeneticAlgorithm.Chromosome(
chromosome_index,
random_route,
crossover_method=self.crossover_method,
mutation_method=self.mutation_method)
self.population.append(chromosome)
self.population = np.array(self.population)
def build_chromosome_from_path_and_graph(chromosome_id, path, graph,
crossover_method, mutation_method):
route = Route(graph)
route.walk_complete_path(path)
return GeneticAlgorithm.Chromosome(chromosome_id, route, crossover_method,
mutation_method)
def crossover(self, other_chromosome):
new_path = list([])
if self.crossover_method == GeneticAlgorithm.Chromosome.CrossoverMethods.UNIFORM:
self_turn = True
while len(new_path) < len(self.route.vertices) - 1:
if self_turn:
remaining_vertex_ids = [
vertex.vertex_id for vertex in self.route.vertices
if vertex.vertex_id not in new_path
]
if len(remaining_vertex_ids) > 0:
new_path.append(
random.choice(remaining_vertex_ids))
self_turn = False
else:
remaining_vertex_ids = [
vertex.vertex_id
for vertex in other_chromosome.route.vertices
if vertex.vertex_id not in new_path
]
if len(remaining_vertex_ids) > 0:
new_path.append(
random.choice(remaining_vertex_ids))
self_turn = True
elif self.crossover_method == GeneticAlgorithm.Chromosome.CrossoverMethods.PARTIALLY_MAPPED:
p1 = random.randint(1, len(self.route.vertices) - 3)
p2 = random.randint(p1 + 1, len(self.route.vertices) - 2)
self_ids = [
vertex.vertex_id for vertex in self.route.vertices
][:-1]
self_s1 = self_ids[:p1]
self_s2 = self_ids[p1:p2]
self_s3 = self_ids[p2:]
other_ids = [
vertex.vertex_id
for vertex in other_chromosome.route.vertices
][:-1]
other_s1 = other_ids[:p1]
other_s2 = other_ids[p1:p2]
other_s3 = other_ids[p2:]
new_path = self_s1
s2_left = list([])
for vertex_id in other_s2:
if vertex_id not in self_s1 and vertex_id not in self_s3:
s2_left.append(vertex_id)
s3_left = list([])
for vertex_id in other_s3:
if vertex_id not in self_s1 and vertex_id not in self_s3:
s3_left.append(vertex_id)
s1_left = list([])
for vertex_id in other_s1:
if vertex_id not in self_s1 and vertex_id not in self_s3:
s1_left.append(vertex_id)
remaining_vertex_ids = s2_left + s3_left + s1_left
new_path += remaining_vertex_ids[:p2 - p1]
new_path += self_s3
elif self.crossover_method == GeneticAlgorithm.Chromosome.CrossoverMethods.ORDERED_CROSSOVER:
p1 = random.randint(1, len(self.route.vertices) - 3)
p2 = random.randint(p1 + 1, len(self.route.vertices) - 2)
j_1 = p1 + 1
j_2 = j_1
k = j_1
to_p1 = self.route.vertices[:p1]
from_p1 = self.route.vertices[p1:]
mid = other_chromosome.route.vertices[p1:p2 + 1]
for vertex in to_p1:
if vertex not in mid:
new_path.append(vertex.vertex_id)
for vertex in mid:
new_path.append(vertex.vertex_id)
for vertex in from_p1:
if vertex.vertex_id not in new_path:
new_path.append(vertex.vertex_id)
else: # Splits the two chromosomes down the middle
index = 0
while len(new_path) < len(self.route.vertices) - 1:
if index < len(self.route.vertices) // 2:
if self.route.vertices[
index].vertex_id not in new_path:
new_path.append(
self.route.vertices[index].vertex_id)
index += 1
else:
remaining_vertices = [
vertex for vertex in self.route.vertices
if vertex.vertex_id not in new_path
]
for remainining_vertex in remaining_vertices:
new_path.append(remainining_vertex.vertex_id)
new_route = Route(self.route.graph)
new_route.walk_complete_path(new_path)
resultant_chromosome = GeneticAlgorithm.Chromosome(
None, new_route, self.crossover_method, self.mutation_method)
return resultant_chromosome