Designing a classifier by a layered multi-population genetic programming approach

  • Authors:

  • Jung-Yi Lin;Hao-Ren Ke;Been-Chian Chien;Wei-Pang Yang

  • Affiliations:

  • Department of Computer Science, National Chiao Tung University, Taiwan;Library and Institute of Information Management, National Chiao Tung University, Taiwan;Department of Computer Science and Information Engineering, National University of Tainan, Taiwan;Department of Computer Science, National Chiao Tung University, Taiwan and Department of Information Management, National Dong Hwa University, Tawian

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

Quantified Score

Hi-index 0.01

Visualization

Abstract

This paper proposes a method called layered genetic programming (LAGEP) to construct a classifier based on multi-population genetic programming (MGP). LAGEP employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. The results of populations are discriminant functions. These functions transform the training set to construct a new training set. The successive layer uses the new training set to obtain better discriminant functions. Moreover, because the functions generated by each layer will be composed to a long discriminant function, which is the result of LAGEP, every layer can evolve with short individuals. For each population, we propose an adaptive mutation rate tuning method to increase the mutation rate based on fitness values and remaining generations. Several experiments are conducted with different settings of LAGEP and several real-world medical problems. Experiment results show that LAGEP achieves comparable accuracy to single population GP in much less time.