遗传算法与强化学习在物联网中的应用

### 遗传算法与强化学习在物联网中的应用 #### 1. 遗传算法优化LSTM网络 遗传算法是一种受自然进化过程启发的优化算法，可用于解决各种复杂的优化问题。在本节中，我们将使用遗传算法来寻找LSTM网络的最优超参数，以降低模型的均方根误差（RMSE）。 ##### 1.1 代码实现步骤 - **导入必要的模块**：使用Keras实现LSTM模型，同时导入其他必要的库。 ```python import numpy as np import pandas as pd from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split as split from keras.layers import LSTM, Input, Dense from keras.models import Model from deap import base, creator, tools, algorithms from scipy.stats import bernoulli from bitstring import BitArray np.random.seed(1120) ``` - **加载数据集**：使用Kaggle上的风力发电预测数据作为时间序列数据。 ```python data = pd.read_csv('train.csv') data = np.reshape(np.array(data['wp1']),(len(data['wp1']),1)) train_data = data[0:17257] test_data = data[17257:] ``` - **定义数据准备函数**：根据选择的窗口大小准备数据集。 ```python def prepare_dataset(data, window_size): X, Y = np.empty((0,window_size)), np.empty((0)) for i in range(len(data)-window_size-1): X = np.vstack([X,data[i:(i + window_size),0]]) Y = np.append(Y,data[i + window_size,0]) X = np.reshape(X,(len(X),window_size,1)) Y = np.reshape(Y,(len(Y),1)) return X, Y ``` - **定义训练评估函数**：为给定的个体创建LSTM网络，并返回其RMSE值作为适应度函数。 ```python def train_evaluate(ga_individual_solution): # Decode genetic algorithm solution to integer for window_size and num_units window_size_bits = BitArray(ga_individual_solution[0:6]) num_units_bits = BitArray(ga_individual_solution[6:]) window_size = window_size_bits.uint num_units = num_units_bits.uint print('\nWindow Size: ', window_size, ', Num of Units: ', num_units) # Return fitness score of 100 if window_size or num_unit is zero if window_size == 0 or num_units == 0: return 100, # Segment the train_data based on new window_size; split into train and validation (80/20) X,Y = prepare_dataset(train_data,window_size) X_train, X_val, y_train, y_val = split(X, Y, test_size = 0.20, random_state = 1120) # Train LSTM model and predict on validation set inputs = Input(shape=(window_size,1)) x = LSTM(num_units, input_shape=(window_size,1))(inputs) predictions = Dense(1, activation='linear')(x) model = Model(inputs=inputs, outputs=predictions) model.compile(optimizer='adam',loss='mean_squared_error') model.fit(X_train, y_train, epochs=5, batch_size=10,shuffle=True) y_pred = model.predict(X_val) # Calculate the RMSE score as fitness score for GA rmse = np.sqrt(mean_squared_error(y_val, y_pred)) print('Validation RMSE: ', rmse,'\n') return rmse, ``` - **使用DEAP工具定义个体、创建种群、进行交叉、变异和选择操作**： ```python population_size = 4 num_generations = 4 gene_length = 10 # As we are trying to minimize the RMSE score, that's why using -1.0. # In case, when you want to maximize accuracy for instance, use 1.0 creator.create('FitnessMax', base.Fitness, weights = (-1.0,)) creator.create('Individual', ```