基于改进量子免疫算法的神经网络集成

Neural Network Ensemble Based on Improved Quantum Immune Algorithm

针对量子免疫算法在神经网络集成结论生成时存在精英损失和过早收敛的问题,提出了改进量子免疫算法。改进算法在免疫选择时采用精英策略保留最优个体,提升了收敛效率,并引入反转策略增加个体多样性,加强了全局搜索能力。仿真实验结果表明,改进量子免疫算法是集成结论优化的有效方法,泛化性能明显优于简单平均、推广集成等传统方法。

Aiming at the problems of elite loss and premature convergence when neural network ensemble generates its conclusion,an improved quantum immune algorithm is proposed.It keeps the best individuals by elitism strategy to improve efficiency and adds the diversity of the individuals by contrarian strategy to strengthen global searching ability.The simulation results show that the improved quantum immune algorithm is a valid optimization method of ensemble conclusions with better generalization ability than traditional methods such as basic ensemble method and generalized ensemble method.