91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
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import random
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import math
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def create_initial_population(population_size, new_list, player):
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population = []
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for _ in range(population_size):
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chromosome = new_list.copy()
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chromosome.remove((player.x+1, player.y+1))
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random.shuffle(chromosome)
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chromosome.insert(0, (player.x+1, player.y+1))
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population.append(chromosome)
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return population
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def calculate_distance(node1, node2):
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x1, y1 = node1
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x2, y2 = node2
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distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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return distance
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def calculate_fitness(individual):
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total_distance = 0
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num_nodes = len(individual)
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for i in range(num_nodes - 1):
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node1 = individual[i]
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node2 = individual[i + 1]
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distance = calculate_distance(node1, node2)
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total_distance += distance
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if total_distance == 0:
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fitness = float('inf')
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return fitness
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fitness = 1 / total_distance
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return fitness
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def crossover(parent1, parent2, player):
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child = [(player.x+1, player.y+1)] + [None] * (len(parent1) - 1)
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start_index = random.randint(1, len(parent1) - 1)
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end_index = random.randint(start_index + 1, len(parent1))
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child[start_index:end_index] = parent1[start_index:end_index]
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remaining_nodes = [node for node in parent2 if node not in child]
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child[1:start_index] = remaining_nodes[:start_index - 1]
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child[end_index:] = remaining_nodes[start_index - 1:]
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return child
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def mutate(individual, mutation_rate):
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for i in range(1, len(individual)):
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if random.random() < mutation_rate:
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j = random.randint(1, len(individual) - 1)
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individual[i], individual[j] = individual[j], individual[i]
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return individual
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def genetic_algorithm(new_list, player):
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max_generations = 200
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population_size = 200
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mutation_rate = 0.1
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population = create_initial_population(population_size, new_list, player)
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best_individual = None
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best_fitness = float('-inf')
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for generation in range(max_generations):
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fitness_values = [calculate_fitness(individual) for individual in population]
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population = [x for _, x in sorted(zip(fitness_values, population), reverse=True)]
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fitness_values.sort(reverse=True)
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best_individuals = population[:10]
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new_population = best_individuals.copy()
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while len(new_population) < population_size:
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parent1, parent2 = random.choices(best_individuals, k=2)
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child = crossover(parent1, parent2, player)
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child = mutate(child, mutation_rate)
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new_population.append(child)
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for individual in best_individuals:
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fitness = calculate_fitness(individual)
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if fitness > best_fitness:
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best_fitness = fitness
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best_individual = individual
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population = new_population[:population_size]
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return best_individual
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