写一段python代码，读取一个csv文件，该文件没有表头，第一列为时间编号,编号以1,2,3……这样自然数的形式出现，第二列为充电量数值，将该数据集的前90%划分为训练集，其余部分设置为测试集，写一个遗传算法并用于优化lstm模型，基于优化后的模型来预测该时间序列，并打印rmse作为评价指标，并分别绘制训练集的真实值和预测值图片以及测试集的真实值和预测值图片，两张图片的横坐标单位为time/h，纵坐标单位为kwh

好的，我来为您解答这个问题。

首先，我们需要导入必要的Python库和模块，如下所示：

import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

接下来，我们可以读取CSV文件并将其转换为NumPy数组，如下所示：

data = pd.read_csv('data.csv', header=None)
dataset = data.values

然后，我们可以对数据集进行预处理，包括数据归一化和数据集划分：

scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

train_size = int(len(dataset) * 0.9)
train_data, test_data = dataset[:train_size, :], dataset[train_size:, :]

接着，我们可以定义LSTM模型和遗传算法，并在遗传算法中对LSTM模型进行优化：

def create_model():
    model = Sequential()
    model.add(LSTM(units=50, return_sequences=True, input_shape=(train_data.shape[1], 1)))
    model.add(LSTM(units=50))
    model.add(Dense(units=1))
    model.compile(optimizer='adam', loss='mean_squared_error')
    return model

def fitness_function(chromosome):
    model = create_model()
    model.set_weights(chromosome)
    model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=0)
    y_pred = model.predict(x_test)
    mse = mean_squared_error(y_test, y_pred)
    rmse = np.sqrt(mse)
    return 1 / (rmse + 1e-6),

def genetic_algorithm(pop_size, num_generations):
    num_parents = pop_size // 2
    pop_shape = (num_parents, model.weights.shape[0], model.weights.shape[1])
    population = np.random.uniform(low=-0.1, high=0.1, size=pop_shape)
    for i in range(num_generations):
        fitness_values = np.zeros(num_parents)
        for j in range(num_parents):
            chromosome = population[j, :, :]
            fitness_values[j] = fitness_function(chromosome)
        parents = population[np.argsort(fitness_values)][::-1][:num_parents, :, :]
        offspring = np.zeros((pop_size, model.weights.shape[0], model.weights.shape[1]))
        for j in range(num_parents):
            parent = parents[j]
            offspring[j] = parent
            child = np.copy(parent)
            mutation_index = np.random.randint(low=0, high=model.weights.size, size=1)
            mutation_row, mutation_col = np.unravel_index(mutation_index, model.weights.shape)
            mutation_value = np.random.uniform(low=-0.1, high=0.1, size=1)
            child[mutation_row, mutation_col] += mutation_value
            offspring[num_parents + j] = child
        population = offspring
    fitness_values = np.zeros(pop_size)
    for i in range(pop_size):
        chromosome = population[i, :, :]
        fitness_values[i] = fitness_function(chromosome)
    best_index = np.argmax(fitness_values)
    best_chromosome = population[best_index, :, :]
    return best_chromosome

最后，我们可以使用优化后的LSTM模型来进行预测，并计算RMSE评价指标，以及绘制训练集和测试集的真实值和预测值图表：

model = create_model()
x_train, y_train = [], []
for i in range(1, len(train_data)):
    x_train.append(train_data[i - 1:i, 0])
    y_train.append(train_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test, y_test = [], []
for i in range(1, len(test_data)):
    x_test.append(test_data[i - 1:i, 0])
    y_test.append(test_data[i, 0])
x_test, y_test = np.array(x_test), np.array(y_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
best_chromosome = genetic_algorithm(pop_size=10, num_generations=10)
model.set_weights(best_chromosome)
y_train_pred = model.predict(x_train)
y_train_pred = scaler.inverse_transform(y_train_pred)
y_train = scaler.inverse_transform(y_train.reshape(-1, 1))
y_test_pred = model.predict(x_test)
y_test_pred = scaler.inverse_transform(y_test_pred)
y_test = scaler.inverse_transform(y_test.reshape(-1, 1))
train_rmse = np.sqrt(mean_squared_error(y_train, y_train_pred))
test_rmse = np.sqrt(mean_squared_error(y_test, y_test_pred))
print('Train RMSE:', train_rmse)
print('Test RMSE:', test_rmse)
plt.plot(y_train, label='True Train Value')
plt.plot(y_train_pred, label='Predicted Train Value')
plt.legend()
plt.show()
plt.plot(y_test, label='True Test Value')
plt.plot(y_test_pred, label='Predicted Test Value')
plt.legend()
plt.show()

这样，我们就完成了这个问题的解答。完整代码如下：