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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Lin20072211,

• author = "Jung-Yi Lin and Hao-Ren Ke and Been-Chian Chien and Wei-Pang Yang",
• title = "Designing a classifier by a layered multi-population genetic programming approach",
• journal = "Pattern Recognition",
• volume = "40",
• number = "8",
• pages = "2211--2225",
• year = "2007",
• note = "Part Special Issue on Visual Information Processing",
• ISSN = "0031-3203",
• DOI = "DOI:10.1016/j.patcog.2007.01.003",
• URL = "http://www.sciencedirect.com/science/article/B6V14-4MVVSM4-5/2/2085e138e1b34ae21d5e76438ae3fc70",
• keywords = "genetic algorithms, genetic programming, Classification, Evolutionary computation, Multi-population genetic programming",
• 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