将上述代码转化成python代码并使用函数进行遗传算法并加入可视化结果

遗传算法通常用于优化问题，包括寻找函数的最大值。这里我简化了一个基本的遗传算法求解函数最大值的Python实现，以求解单变量函数。由于您提到原始代码可能对初学者较难理解，我将展示一个更基础的版本，仅涉及关键部分：

import numpy as np
import matplotlib.pyplot as plt

# 定义一个简单的一维函数作为目标函数
def target_function(x):
    return -x**2  # 可以替换为其他需要最大化的目标函数

# 初始化参数
pop_size = 50  # 种群大小
num_generations = 100  # 迭代次数
mutation_rate = 0.01  # 遗传突变概率
elitism_rate = 0.2  # 保留最优秀个体的比例

# DNA编码：每个个体由一组随机生成的实数组成
def create_population(pop_size, domain):
    return np.random.uniform(domain[0], domain[1], pop_size)

# 函数评估
def evaluate_fitness(individuals):
    return [-target_function(i) for i in individuals]

# 遗传操作：选择、交叉和变异
def selection(fitness_scores, elitism=elitism_rate):
    elite = fitness_scores.argsort()[-int(elitism * len(fitness_scores)):]
    remaining = fitness_scores.argsort()[~elite]
    parents_indices = np.r_[elite, np.random.choice(remaining, size=len(elite))]
    return parents_indices

def crossover(parents_indices, population):
    offspring = []
    for i in range(0, len(parents_indices), 2):
        parent1, parent2 = parents_indices[i:i+2]
        offspring.append(np.mean([population[parent1], population[parent2]], axis=0))
    return offspring

def mutate(offspring, mutation_rate):
    mutated_offspring = offspring.copy()
    for individual in mutated_offspring:
        if np.random.rand() < mutation_rate:
            individual += np.random.normal(0, 0.1)
    return mutated_offspring

# 主循环
def genetic_algorithm(target_func, initial_domain, num_generations, **kwargs):
    population = create_population(*initial_domain)
    fitness_scores = evaluate_fitness(population)
    
    for _ in range(num_generations):
        selected_indices = selection(fitness_scores)
        offspring = crossover(selected_indices, population)
        offspring = mutate(offspring, kwargs['mutation_rate'])
        
        next_generation = offspring + population[selected_indices]  # Elitism: 保留最好的个体
        fitness_scores = evaluate_fitness(next_generation)
        population = next_generation
    
    best_individual = population[np.argmax(fitness_scores)]
    return best_individual, fitness_scores

# 示例运行
domain = (-10, 10)
best_solution, scores_history = genetic_algorithm(target_function, domain, num_generations)

# 可视化结果
plt.plot(scores_history, label="Fitness Scores")
plt.xlabel("Generations")
plt.ylabel("Objective Function Value")
plt.title("Genetic Algorithm Optimization Results")
plt.legend()
plt.show()