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.
| Original language | English |
|---|---|
| Pages (from-to) | 2211-2225 |
| Number of pages | 15 |
| Journal | Pattern Recognition |
| Volume | 40 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2007 Aug 1 |
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Keywords
- Classification
- Evolutionary computation
- Multi-population genetic programming
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
Designing a classifier by a layered multi-population genetic programming approach. / Lin, Jung Yi; Ke, Hao-Ren; Chien, Been Chian; Yang, Wei Pang.
In: Pattern Recognition, Vol. 40, No. 8, 01.08.2007, p. 2211-2225.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Designing a classifier by a layered multi-population genetic programming approach
AU - Lin, Jung Yi
AU - Ke, Hao-Ren
AU - Chien, Been Chian
AU - Yang, Wei Pang
PY - 2007/8/1
Y1 - 2007/8/1
N2 - 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.
AB - 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.
KW - Classification
KW - Evolutionary computation
KW - Multi-population genetic programming
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UR - http://www.scopus.com/inward/citedby.url?scp=34247119090&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2007.01.003
DO - 10.1016/j.patcog.2007.01.003
M3 - Article
AN - SCOPUS:34247119090
VL - 40
SP - 2211
EP - 2225
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 8
ER -