Automatic modeling of a novel gene expression programming based on statistical analysis and critical velocity
Kangshun Li, Weifeng Pan, Wensheng Zhang, Zhangxin Chen · 2008
The basic principle of GEP is briefly introduced. And considering the defects of classic GEP such as lack of variety, the problem of convergence and blind searching without learning mechanism, a novel GEP based on statistical analysis and stagnancy velocity is proposed (called AMACGEP). It mainly has the following characteristics: First, improve the initial population by statistic analysis of repeated bodies. Second, introduce the concept of stagnancy velocity to adjust the searching space, evolution velocity, the diversity of individuals and the accuracy of prediction. Third, introduce dynamic mutation operator to improve the diversity of individuals and the velocity of convergence. Compared with other methods like traditional methods, methods of neural network, classic GEP and other improved GEPs in automatic modeling of complex function, the simulation results show that the AMACGEP set up by this paper is better.