A NEW ALGORITHM OF EVOLVING ARTIFICIAL NEURAL NETWORKS VIA GENE EXPRESSION PROGRAMMING

  • Li, Kangshun (SCHOOL OF INFORMATION ENGINEERING, JIANGXI UNIVERSITY OF SCIENCE & TECHNOLOGY) ;
  • Li, Yuanxiang (STATE KEY LABORATORY OF SOFTWARE ENGINEERING, WUHAN UNIVERSITY) ;
  • Mo, Haifang (STATE KEY LABORATORY OF SOFTWARE ENGINEERING, WUHAN UNIVERSITY) ;
  • Chen, Zhangxin (CENTER FOR SCIENTIFIC COMPUTATION AND DEPARTMENT OF MATHEMATICS, SOUTHERN MATHODIST UNIVERSITY)
  • Published : 2005.12.25

Abstract

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.