# 遗传算法优化 def optimize_portfolio_with_genetic_algorithm(): n_assets = 3701 # 资产数量 population_size = 50 # 种群大小 generations = 100 # 迭代次数 # 定义种群 population = [] for i in range(population_size): individual = np.random.rand(n_assets) individual /= np.sum(individual) population.append(individual) # 定义适应度函数 def fitness_function(individual): return minimize_portfolio_risk(individual, returns, cov_matrix) # 进行迭代 for i in range(generations): # 选择 fits = [fitness_function(individual) for individual in population] indices = np.argsort(fits) population = [population[i] for i in indices[:population_size//2]] # 交叉 for i in range(population_size//2): individual1, individual2 = random.choice(population), random.choice(population) child1, child2 = crossover(individual1, individual2) population += [child1, child2] # 变异 for i in range(population_size): population[i] = mutate(population[i]) # 返回最优解 best_solution = max(population, key=fitness_function) return best_solution # 运行优化程序 returns = mu cov_matrix = S weights = optimize_portfolio_with_genetic_algorithm() print(weights)TypeError: mutate() missing 1 required positional argument: 'mutation_rate'
时间: 2024-01-25 07:05:04 浏览: 27
从错误提示来看,是因为在调用`mutate()`函数时没有传递`mutation_rate`参数导致的。在这个代码片段中,我们可以看到`mutate()`函数的定义没有变,但是在调用`mutate()`函数时没有传递`mutation_rate`参数,因此需要添加`mutation_rate`参数。
假设我们希望将变异率设置为0.1,可以在调用`mutate()`函数时传递`mutation_rate`参数,例如:
```python
def optimize_portfolio_with_genetic_algorithm():
# ... 省略部分代码 ...
mutation_rate = 0.1 # 设置变异率
for i in range(generations):
# ... 省略部分代码 ...
for i in range(population_size):
population[i] = mutate(population[i], mutation_rate) # 传递mutation_rate参数
# ... 省略部分代码 ...
```
在这里,我们将变异率设置为0.1,并在每次调用`mutate()`函数时将其作为第二个参数传递。这样做可以避免上述错误,并确保算法正常运行。
相关问题
# 设置变异率 # 进行迭代 for i in range(generations): # 选择 fits = [fitness_function(individual) for individual in population] indices = np.argsort(fits) population = [population[i] for i in indices[:population_size//2]] # 交叉 for i in range(population_size//2): individual1, individual2 = random.choice(population), random.choice(population) child1, child2 = crossover(individual1, individual2) population += [child1, child2] # 变异 for i in range(population_size): population[i] = mutate(population[i],mutation_rate) # 返回最优解 best_solution = max(population, key=fitness_function) return best_solution # 运行优化程序 returns = mu cov_matrix = S weights = optimize_portfolio_with_genetic_algorithm() print(weights)算出来的权重和为多少
遗传算法优化投资组合权重的过程中,权重并没有被限制为必须相加为1,因此它们的和可能会超过1或少于1。如果我们想要将它们限制为相加为1,可以使用下面的代码:
```
weights /= np.sum(weights)
```
将此代码添加到打印权重的代码后面,可以得到相加为1的投资组合权重。
另一种方法是在适应度函数中对权重做一个约束,使得其相加为1。这可以确保最终得到的权重总和为1。
res = optimize.minimize(lambda x: -my_func(x, a), x0=0)
这是一个使用SciPy库中的optimize模块中的minimize函数来进行优化的例子。其中,lambda x: -my_func(x, a)是要最小化的函数,x0是初始猜测值。具体来说,这个例子是在最小化my_func函数(第一个参数为x,第二个参数为a)的相反数,因为minimize默认是寻找最小值而不是最大值。optimize.minimize函数会尝试找到使得my_func函数最小化的x值,最终返回一个结果对象res,其中包括最小化函数的最优解、函数值、迭代次数等信息。