Designing a classifier by a layered multi-population genetic programming approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Lin20072211,
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author = "Jung-Yi Lin and Hao-Ren Ke and Been-Chian Chien and
Wei-Pang Yang",
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title = "Designing a classifier by a layered multi-population
genetic programming approach",
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journal = "Pattern Recognition",
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volume = "40",
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number = "8",
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pages = "2211--2225",
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year = "2007",
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note = "Part Special Issue on Visual Information Processing",
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ISSN = "0031-3203",
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DOI = "DOI:10.1016/j.patcog.2007.01.003",
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URL = "http://www.sciencedirect.com/science/article/B6V14-4MVVSM4-5/2/2085e138e1b34ae21d5e76438ae3fc70",
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keywords = "genetic algorithms, genetic programming,
Classification, Evolutionary computation,
Multi-population genetic programming",
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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.",
- }
Genetic Programming entries for
Mick Jung-Yi Lin
Hao-Ren Ke
Been-Chian Chien
Wei-Pang Yang
Citations