Function Mining based on Gene Ezpression Programming and Particle Swarm Optimization
Gene Expression Programming (GEP) is a powerful tool widely used in function mining. However, it is difficult for GEP to generate appropriate numeric constants for function mining. In this paper, a novel approach of creating numeric constants, GEPPSO, was proposed, which embedded Particle Swarm Optimization (PSO) into GEP. In the approach, the evolutionary process was divided into 2 phases: in the first phase, GEP focused on optimizing the structure of function expression, and in the second one, PSO focused on optimizing the constant parameters. The experimental results on function mining problems show that the performance of GEPPSO is better than that of the existing GEP Random Numerical Constants algorithm (GEP-RNC).
evolutionary algorithm function mining gene ezpression programming particle swarm optimization
Taiyong Li Tiangang Dong Jiang Wu Ting He
School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu School of Computer Science Sichuan University Chengdu 610065, China The Research Center for China Payment System Southwestern University of Finance and Economics Chengd College of Pharmaceutical Science Chengdu University of Traditional Chinese Medicine Chengdu 611130,
国际会议
北京
英文
2047-2051
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）