摘要
介绍基因表达式程序设计方法的基本原理,针对股票指数分析与预测问题,在经典的GEP算法基础上,提出一种基于动态变异算子的改进的GEP算法——IGEP算法,动态变异算子随着进化代数和染色体所含基因数目不同而变化,从而加快了GEP的收敛速度和精确度,对算法进行了复杂度和收敛性分析。设计一种基于IGEP的股票指数分析与预测算法,数值实验结果表明,该算法优于经典GEP算法,具有较广泛的通用性。
This paper introduces the basic principle of Gene Expression Programming（GEP）. An improved GEP algorithm called IGEP based on dynamic mutation operator which is changed with the gene number of the genome and the number of evolutionary generation is presented. The algorithm complexity of IGEP is given in the paper. Furthermore. IGEP is applied in the solution of prediction in stock-price index. The simulation results show that the model found by IGEP is more accurate than the one of classic GEP and which proves the IGEP can be widely used in many fields.
出处
《计算机工程》
CAS
CSCD
北大核心
2009年第5期200-202,共3页
Computer Engineering
关键词
基因表达式程序设计
复杂度分析
收敛性分析
股票指数预测
Gene Expression Programming（GEP）
complexity analysis
convergence analysis
prediction in stock-price index
