Title: Designing a classifier by a layered multi-population genetic programming approach
Authors: Lin, Jung-Yi
Ke, Hao-Ren
Chien, Been-Chian
Yang, Wei-Pang
Department of Computer Science
Keywords: classification;evolutionary computation;multi-population genetic programming
Issue Date: 1-Aug-2007
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. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.patcog.2007.01.003
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2007.01.003
Volume: 40
Issue: 8
Begin Page: 2211
End Page: 2225
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