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
N1 - Funding Information:
We would like to express our appreciation to the anonymous reviewers for their useful suggestions and revision. We also wish to thank Dr. Hsinchun Chen and Jiexun Li for many helpful discussions and comments. About the Author —JUNG-YI LIN was born in Taitung, Taiwan. He received the M.S. degree in Computer Science and Information Engineering from I-Shou University in 2002. He is currently a Ph.D. candidate in Computer Science, National Chiao Tung University, HsinChu, Taiwan. Lin is currently a visiting scholar at Artificial Intelligence Lab, Department of MIS, University of Arizona, Arizona, USA. His research interests include machine learning, data mining, and knowledge discovery. About the Author —HAO-REN KE was born on June 29, 1967 in Taipei, Taiwan, Republic of China. He received the B.S. degree in 1989 and his Ph.D. degree in 1993, both in Computer and Information Science, from National Chiao Tung University. Now he is a professor of the Library, and Institute of Information Management, National Chiao Tung University (NCTU). He is also the associate director of the NCTU Library. His research interests include digital library, digital museum, information retrieval, web service, and data mining. He can be contacted at: [email protected]. About the Author —BEEN-CHIAN CHIEN received the Ph.D. in Computer Science and Information Engineering from National Chiao Tung University in 1992. He was an associate professor of the Department of Computer Science and Information Engineering, I-Shou University, Kaohsiung, Taiwan, from 1996 to 2004. Currently, he is a professor and the head of the department of computer science and information engineering, national university of Tainan, Tainan, Taiwan. His current research activities involve machine learning, content-based image retrieval, intelligent information retrieval and data mining. About the Author —WEI-PANG YANG was born on May 17, 1950 in Hualien, Taiwan. He received the B.S. degree in mathematics from National Taiwan Normal University in 1974, and the M.S. and Ph.D. degrees from the National Chiao Tung University in 1979 and 1984, respectively, both in Computer Engineering. He was a professor of the Department of CSIE and Department of CIS at the National Chiao Tung University, Hsinchu, Taiwan. He was a visiting scholar at the Harvard University and at the University of Washington. He was the Director of the Computer Center of National Chiao Tung University. Dr. Yang is currently the Head of the Department of Information Management and is the Dean of College of Management. His research interests include database theory and application, information retrieval, data miming, digital library, and digital museum.
PY - 2007/8
Y1 - 2007/8
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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U2 - 10.1016/j.patcog.2007.01.003
DO - 10.1016/j.patcog.2007.01.003
M3 - Article
AN - SCOPUS:34247119090
SN - 0031-3203
VL - 40
SP - 2211
EP - 2225
JO - Pattern Recognition
JF - Pattern Recognition
IS - 8
ER -