是否有参考代码

是的，有许多开源的代码可以用来实现遗传算法优化LSTM神经网络进行共享单车需求预测。其中比较常用的是使用Python编写的开源库，如DEAP、Pyevolve等。这些库提供了一些常用的遗传算法实现，可以很方便地用来优化LSTM神经网络。

以下是一个简单的使用DEAP库实现遗传算法优化LSTM神经网络预测的示例代码：

import random
import numpy as np
from deap import base, creator, tools, algorithms
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 定义遗传算法的参数
POP_SIZE = 50  # 种群数量
GEN_SIZE = 20  # 迭代次数
MUT_RATE = 0.05  # 变异率

# 构造LSTM神经网络模型
def create_model(input_dim, output_dim, lstm_units, dense_units):
    model = Sequential()
    model.add(LSTM(lstm_units, input_shape=(input_dim, 1)))
    model.add(Dense(dense_units, activation='relu'))
    model.add(Dense(output_dim))
    model.compile(loss='mse', optimizer='adam')
    return model

# 定义适应度函数
def eval_fitness(individual):
    # 解码个体
    lstm_units = individual[0]
    dense_units = individual[1]
    # 训练LSTM神经网络，并返回预测误差作为适应度值
    model = create_model(input_dim, output_dim, lstm_units, dense_units)
    model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
    y_pred = model.predict(X_test)
    mse = np.mean(np.square(y_pred - y_test))
    return (mse,)

# 初始化遗传算法
creator.create('FitnessMin', base.Fitness, weights=(-1.0,))
creator.create('Individual', list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register('attr_lstm_units', random.randint, 10, 100)
toolbox.register('attr_dense_units', random.randint, 10, 100)
toolbox.register('individual', tools.initCycle, creator.Individual, 
                 (toolbox.attr_lstm_units, toolbox.attr_dense_units), n=1)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
toolbox.register('evaluate', eval_fitness)
toolbox.register('mate', tools.cxUniform, indpb=0.5)
toolbox.register('mutate', tools.mutUniformInt, low=10, up=100, indpb=MUT_RATE)
toolbox.register('select', tools.selTournament, tournsize=3)

# 加载数据
data = np.load('data.npy')
X_train, y_train = data[:500, :-1], data[:500, -1]
X_test, y_test = data[500:, :-1], data[500:, -1]
input_dim, output_dim = X_train.shape[1], 1

# 运行遗传算法
pop = toolbox.population(n=POP_SIZE)
best = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register('avg', np.mean)
stats.register('min', np.min)
pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=GEN_SIZE, 
                               stats=stats, halloffame=best, verbose=True)

# 输出预测结果
lstm_units = best[0][0]
dense_units = best[0][1]
model = create_model(input_dim, output_dim, lstm_units, dense_units)
model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
y_pred = model.predict(X_test)
print('预测值:', y_pred.ravel())
print('真实值:', y_test.ravel())

请注意，这只是一个示例代码，仅用于演示如何使用遗传算法优化LSTM神经网络进行共享单车需求预测。实际的应用中，您需要根据自己的数据和需求进行适当的修改和调整。