y=f(x1,x2,x3,x4,x5,x6,x7)=100/(|x1+1|+|x2|+|x3-1|+|x4+2|+|x5+3|+|x6-2|+|x7-3|+1),用python编写代码求解函数最小值，要求不使用numpy库，过程运用遗传算法

要使用遗传算法求解函数的最小值，可以按照以下步骤进行编写代码：

1. 定义适应度函数：根据给定的函数表达式，计算适应度函数的值。适应度函数的值越小，表示个体的适应度越高。

def fitness_function(x):
    x1, x2, x3, x4, x5, x6, x7 = x
    y = 100 / (abs(x1 + 1) + abs(x2) + abs(x3 - 1) + abs(x4 + 2) + abs(x5 + 3) + abs(x6 - 2) + abs(x7 - 3) + 1)
    return y

2. 初始化种群：随机生成一组个体作为初始种群。

import random

def initialize_population(population_size, chromosome_length):
    population = []
    for _ in range(population_size):
        individual = [random.uniform(-10, 10) for _ in range(chromosome_length)]
        population.append(individual)
    return population

3. 选择操作：使用轮盘赌选择算法，根据个体的适应度选择一部分个体作为下一代的父代。

def selection(population, fitness_values):
    total_fitness = sum(fitness_values)
    probabilities = [fitness / total_fitness for fitness in fitness_values]
    selected_population = []
    for _ in range(len(population)):
        r = random.random()
        cumulative_probability = 0
        for i, probability in enumerate(probabilities):
            cumulative_probability += probability
            if r <= cumulative_probability:
                selected_population.append(population[i])
                break
    return selected_population

4. 交叉操作：使用单点交叉算法，对父代个体进行交叉操作生成子代个体。

def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1) - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

5. 变异操作：对子代个体进行变异操作，增加种群的多样性。

def mutation(individual, mutation_rate):
    mutated_individual = individual.copy()
    for i in range(len(mutated_individual)):
        if random.random() < mutation_rate:
            mutated_individual[i] = random.uniform(-10, 10)
    return mutated_individual

6. 遗传算法主程序：根据上述操作，进行遗传算法的迭代过程。

def genetic_algorithm(population_size, chromosome_length, generations, mutation_rate):
    population = initialize_population(population_size, chromosome_length)
    for _ in range(generations):
        fitness_values = [fitness_function(individual) for individual in population]
        selected_population = selection(population, fitness_values)
        new_population = []
        while len(new_population) < population_size:
            parent1 = random.choice(selected_population)
            parent2 = random.choice(selected_population)
            child1, child2 = crossover(parent1, parent2)
            mutated_child1 = mutation(child1, mutation_rate)
            mutated_child2 = mutation(child2, mutation_rate)
            new_population.extend([mutated_child1, mutated_child2])
        population = new_population
    best_individual = max(population, key=fitness_function)
    best_fitness = fitness_function(best_individual)
    return best_individual, best_fitness

7. 调用遗传算法函数并输出结果。

population_size = 100
chromosome_length = 7
generations = 100
mutation_rate = 0.01

best_individual, best_fitness = genetic_algorithm(population_size, chromosome_length, generations, mutation_rate)
print("最优个体:", best_individual)
print("最优适应度:", best_fitness)

这样就可以使用遗传算法求解给定函数的最小值了。