帮我改写这段代码def get_fitness(DNA, value_min, value_max, accuracy): x = Decode(DNA, value_min, value_max, accuracy) y = x**2 * np.exp(-x/2) * ((math.sin(5 * (x**2))) ** 2) return y # 初始化种群 def init(generation_num, DNA_length): generation = [[random.choice([0, 1]) for i in range(DNA_length)] for j in range(generation_num)] return generation # 选择操作 def select(generation, value_min, value_max, accuracy): fitness_list = [get_fitness(DNA, value_min, value_max, accuracy) for DNA in generation] p_list = [f / sum(fitness_list) for f in fitness_list] idx = list(range(len(generation))) idx_selected = np.random.choice(idx, size=len(generation), replace=True, p=p_list) # 根据概率选择，有放回抽样 return [generation[i] for i in idx_selected]

这段代码的功能是实现了一个遗传算法的基本操作，包括初始化种群、计算个体适应度、根据适应度选择个体等。以下是改写后的代码：

def get_fitness(DNA, value_min, value_max, accuracy):
    x = decode(DNA, value_min, value_max, accuracy)
    y = x**2 * np.exp(-x/2) * ((math.sin(5 * (x**2))) ** 2)
    return y

# 初始化种群
def init_population(generation_num, DNA_length):
    return [[random.choice([0, 1]) for i in range(DNA_length)] for j in range(generation_num)]

# 选择操作
def select(generation, value_min, value_max, accuracy):
    fitness_list = [get_fitness(DNA, value_min, value_max, accuracy) for DNA in generation]
    p_list = [f / sum(fitness_list) for f in fitness_list]
    idx_selected = np.random.choice(range(len(generation)), size=len(generation), replace=True, p=p_list)
    return [generation[i] for i in idx_selected]

主要的改动有：

1. 函数名改为小写，并使用下划线分隔单词，符合常规的Python命名规范。
2. 将初始化种群的函数名从init改为init_population，以避免与Python内置函数init重名。
3. 在选择操作中，简化了代码，去掉了不必要的变量。