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

@article{Lin2007DesigningAC,
  title={Designing a classifier by a layered multi-population genetic programming approach},
  author={Jung-Yi Lin and Hao-Ren Ke and Been-Chian Chien and Wei-Pang Yang},
  journal={Pattern Recognition},
  year={2007},
  volume={40},
  pages={2211-2225}
}

• Jung-Yi Lin, Hao-Ren Ke, +1 author Wei-Pang Yang
• Published in Pattern Recognition 2007
• DOI:10.1016/j.patcog.2007.01.003

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… CONTINUE READING

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