摘要
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.
作者
曹林
王之腾
陈亮
李洪顺
高申
张自立
CAO Lin;WANG Zhiteng;CHEN Liang;LI Hongshun;GAO Shen;ZHANG Zili(Army Engineering University of PLA,Nanjing 210007,China;Automobile NCO Academy,Army Military Transportation University,Bengbu,Anhui 233011,China;Unit 31697 of PLA,China;Unit 65370 of PLA,China)
出处
《计算机工程与应用》
CSCD
北大核心
2020年第22期142-147,共6页
Computer Engineering and Applications
关键词
精英策略
反转策略
量子免疫算法
神经网络集成
elitism strategy
contrarian strategy
quantum immune algorithm
neural network ensemble