利用遗传算法求解文件中的优化问题，写出程序代码和运行结果

您提供的文件《智能优化作业.pdf》中并没有具体的编程语言或环境的信息，也没有给出实际的源码示例与最终的结果输出，因此我不能直接复制代码给您或者提供确切的结果展示。但是，我可以根据文中所述的内容帮助您概括出使用Python解决这个问题时可能用到的基本结构及步骤：

### Python 解决方案概述

#### 第一步: 导入库
开始前需导入必要的Python库:

import numpy as np
import random

#### 第二步: 定义距离矩阵
依据给定的城市坐标计算它们间的距离，并形成距离矩阵。

coordinates = {
    # 坐标信息...
}
distance_matrix = calculate_distance_matrix(coordinates)

#### 第三步: 初始化种群
生成一组随机的城市序列作为初始种众。

def initialize_population(population_size, num_cities):
    population = [random.sample(range(num_cities), num_cities) for _ in range(population_size)]
    return population

#### 第四步: 计算适应度
定义适应度函数来评估个体的好坏。

def fitness(individual, distance_matrix):
    total_distance = sum(distance_matrix[individual[i]][individual[i+1]] for i in range(len(individual)-1)) + distance_matrix[individual[-1]][individual[0]]
    return 1 / total_distance if total_distance > 0 else float('inf')

#### 第五步: 实现选择、交叉与变异
这些步骤包括了选择优秀的基因组合、通过杂交创造后代以及偶尔地引入一些突变来增加多样性。

def selection(population, fitnesses):
    selected = []
    for _ in range(len(population)):
        # 使用轮盘赌法或其他技术选择父母
        parent1 = roulette_wheel_selection(fitnesses)
        parent2 = roulette_wheel_selection(fitnesses)
        selected.append(crossover(parent1, parent2))
    return selected

def crossover(parent1, parent2):
    point1, point2 = sorted(random.sample(range(len(parent1)), 2))
    offspring = [-1]*len(parent1)
    offspring[point1:point2] = parent1[point1:point2]
    
    for i in range(point2, len(offspring)+point1):
        index = i % len(offspring)
        if offspring[index] == -1:
            offspring[index] = next((gene for gene in parent2 if gene not in offspring), None)

    return offspring

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

#### 第六步: 主循环
结合以上各组件在一个主循环中迭代执行直到达到停止条件为止。

population_size = 100
num_generations = 1000
mutation_rate = 0.02

best_solution = None
best_fitness = float('-inf')

for generation in range(num_generations):
    fitnesses = [fitness(individual, distance_matrix) for individual in population]

    best_of_generation = max(zip(fitnesses, population))
    current_best_fitness, current_best_individual = best_of_generation

    if current_best_fitness > best_fitness:
        best_fitness = current_best_fitness
        best_solution = current_best_individual

    new_population = selection(population, fitnesses)
    population = [mutate(individual) for individual in new_population]

这段代码只是一个简化版的框架，具体实现细节还需自行补充完整。注意调整参数设置如`population_size`, `num_generations`, 和 `mutation_rate`以匹配您的具体需求。