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

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

研究成果: 雜誌貢獻文章

34 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)2211-2225
頁數15
期刊Pattern Recognition
40
發行號8
DOIs
出版狀態已發佈 - 2007 八月 1

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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