用Python写遗传平衡和遗传漂变的计算机模拟：每个群体的大小分别选取10，20，50， 100，1000，10000；遗传的代数分别选取1、2、3、5、10，50，100，500，1000。

以下是Python实现遗传平衡和遗传漂变的计算机模拟的代码，其中包括了不同群体大小和遗传代数的情况：

import random

# 定义遗传算法的参数
pop_sizes = [10, 20, 50, 100, 1000, 10000] # 种群规模
gens = [1, 2, 3, 5, 10, 50, 100, 500, 1000] # 进化代数
pc = 0.8 # 交叉率
pr = 0.25 # 反转率
pm = 0.4 # 变异率

# 定义适应度函数
def fitness(chromosome):
    return sum(chromosome)

# 定义交叉操作
def crossover(parent1, parent2):
    child1 = parent1.copy()
    child2 = parent2.copy()
    if random.random() < pc:
        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

# 定义变异操作
def mutation(chromosome):
    mutated_chromosome = chromosome.copy()
    for i in range(len(chromosome)):
        if random.random() < pm:
            mutated_chromosome[i] = 1 - chromosome[i]
    return mutated_chromosome

# 定义反转操作
def inversion(chromosome):
    inverted_chromosome = chromosome.copy()
    if random.random() < pr:
        inversion_point1 = random.randint(0, len(chromosome) - 1)
        inversion_point2 = random.randint(0, len(chromosome) - 1)
        if inversion_point1 > inversion_point2:
            inversion_point1, inversion_point2 = inversion_point2, inversion_point1
        inverted_chromosome[inversion_point1:inversion_point2+1] = reversed(inverted_chromosome[inversion_point1:inversion_point2+1])
    return inverted_chromosome

# 定义遗传算法函数
def genetic_algorithm(pop_size, gen):
    # 初始化种群
    population = [[random.randint(0, 1) for _ in range(10)] for _ in range(pop_size)]
    # 进化
    for _ in range(gen):
        # 计算适应度值
        fitness_values = [fitness(chromosome) for chromosome in population]
        # 选择操作
        selected_population = []
        for _ in range(pop_size):
            parent1 = population[random.randint(0, pop_size - 1)]
            parent2 = population[random.randint(0, pop_size - 1)]
            if fitness(parent1) > fitness(parent2):
                selected_population.append(parent1)
            else:
                selected_population.append(parent2)
        # 交叉操作
        new_population = []
        for i in range(0, pop_size, 2):
            child1, child2 = crossover(selected_population[i], selected_population[i+1])
            new_population.append(child1)
            new_population.append(child2)
        # 变异操作
        new_population = [mutation(chromosome) for chromosome in new_population]
        # 反转操作
        new_population = [inversion(chromosome) for chromosome in new_population]
        # 更新种群
        population = new_population
    # 返回最优解
    return max(population, key=fitness)

# 输出不同群体大小和遗传代数的情况下的最优解
for pop_size in pop_sizes:
    for gen in gens:
        print(f"Population size: {pop_size}, Generations: {gen}")
        print(f"Best solution: {genetic_algorithm(pop_size, gen)}")
        print("--------------------------------------------------")