import pickle
import random


def manhattan_distance(point1, point2):
    return abs(point1[0] - point2[0]) + abs(point1[1] - point2[1])


def calculate_fitness(individual, points):
    return -sum(manhattan_distance(points[individual[i - 1]], points[individual[i]]) for i in range(len(individual)))


def crossover(parent1, parent2):
    size = len(parent1)
    start, end = sorted(random.sample(range(size), 2))
    child = [None] * size

    # Copy subset from first parent
    child[start:end + 1] = parent1[start:end + 1]

    # Copy remaining genes from second parent, wrapping around
    wrapped_parent2 = parent2[end + 1:] + parent2[:end + 1]
    remaining_genes = [gene for gene in wrapped_parent2 if gene not in child]

    child[end + 1:] = remaining_genes[:size - end - 1]
    child[:start] = remaining_genes[size - end - 1:]

    return child


def mutate(individual):
    idx1, idx2 = random.sample(range(len(individual)), 2)
    individual[idx1], individual[idx2] = individual[idx2], individual[idx1]


def genetic_algorithm(points, population_size, generations):
    population = [random.sample(range(len(points)), len(points)) for _ in range(population_size)]
    for _ in range(generations):
        population.sort(key=lambda individual: calculate_fitness(individual, points), reverse=True)
        next_generation = population[:population_size // 10]
        while len(next_generation) < population_size:
            parent1, parent2 = random.choices(population[:population_size // 2], k=2)
            child = crossover(parent1, parent2)
            if random.random() < 0.01:
                mutate(child)
            next_generation.append(child)
        population = next_generation
    best_individual = max(population, key=lambda individual: calculate_fitness(individual, points))
    best_path = [points[i] for i in best_individual]

    with open('best_path.pkl', 'wb') as f:
        pickle.dump(best_path, f)

    return best_path, -calculate_fitness(best_individual, points)


point_list = []
for i in range(1, 29):
    if i % 2 == 1:
        point_list.append((i, 6))
        point_list.append((i, 12))
    elif i % 2 == 0:
        point_list.append((i, 9))
    if i % 2 == 1 and i % 4 != 1:
        point_list.append((i, 10))

print(genetic_algorithm(point_list, 100, 1000))