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Zaktualizuj 'genetic_route.py'

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Jakub Damiński 2020-06-15 01:32:04 +00:00
parent 03efb3cdcf
commit b6faed1ed2
1 changed files with 98 additions and 103 deletions
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genetic_route.py
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@ -4,25 +4,25 @@ from math import floor
import copy import copy
num_of_surviving = 6 num_of_surviving = 6
num_of_couples = 12 num_of_couples = 8
mutation_probability = 0.07 mutation_probability = 0.07
max_population = 20 max_population = 20
iterations = 50 iterations = 50
# creates new random solution to add to population
def create_new_route(points): def create_new_route(points):
route = np.random.permutation(points) route = np.random.permutation(points)
route = [x + 1 for x in route] route = [x + 1 for x in route]
return route return route
# creates initian population
def create_population(points): def create_population(points):
population = [] population = []
for i in range(max_population): for i in range(max_population):
population.append(create_new_route(points)) population.append(create_new_route(points))
return population return population
# gives score to a solution based on lenght
def score_route(graph_map, route): def score_route(graph_map, route):
score = graph_map[0][route[0]] score = graph_map[0][route[0]]
for i in range(len(route) - 2): for i in range(len(route) - 2):
@ -31,7 +31,7 @@ def score_route(graph_map, route):
score = score + graph_map[route[i + 1]][route[i + 2]] score = score + graph_map[route[i + 1]][route[i + 2]]
return score return score
# scores every solution in population
def score_all(graph_map, population): def score_all(graph_map, population):
scores = [] scores = []
for i in range(len(population)): for i in range(len(population)):
@ -39,7 +39,7 @@ def score_all(graph_map, population):
scores.append(tmp) scores.append(tmp)
return scores return scores
# designed to create new population by mixing steps between most succesfull solutions
def crossover(a, b): def crossover(a, b):
new_a = copy.deepcopy(a) new_a = copy.deepcopy(a)
new_b = copy.deepcopy(b) new_b = copy.deepcopy(b)
@ -53,10 +53,9 @@ def crossover(a, b):
new_b[new_b.index(tmp_a)] = tmp_b new_b[new_b.index(tmp_a)] = tmp_b
new_a[rel] = tmp_b new_a[rel] = tmp_b
new_b[rel] = tmp_a new_b[rel] = tmp_a
return new_a, new_b return new_a, new_b
# adds randomness to newly created solutions
def mutate(route): def mutate(route):
new_route = copy.deepcopy(route) new_route = copy.deepcopy(route)
for i in range(len(route) - 1): for i in range(len(route) - 1):
@ -66,7 +65,7 @@ def mutate(route):
new_route[i + 1] = tmp new_route[i + 1] = tmp
return new_route return new_route
# main function that iterate population until the best solutions emerge
def genetic_trace_route(graph_map, packages): def genetic_trace_route(graph_map, packages):
population = create_population(packages) population = create_population(packages)
for i in range(iterations): for i in range(iterations):
@ -91,13 +90,9 @@ def genetic_trace_route(graph_map, packages):
population = copy.deepcopy(new_population) population = copy.deepcopy(new_population)
scores = score_all(graph_map, population) scores = score_all(graph_map, population)
scores.sort(key=lambda x: x[1]) scores.sort(key=lambda x: x[1])
print(population) print("Best route of all population in iteration " + i + 1)
print(scores[0][1]) print(scores[0][1])
scores = score_all(graph_map, population) scores = score_all(graph_map, population)
scores.sort(key=lambda x: x[1]) scores.sort(key=lambda x: x[1])
print("best route lenght")
print(scores[0][1])
print("secand best route lenght")
print(scores[1][1])
return population[scores[0][0]] return population[scores[0][0]]