Layered Multi-Population Genetic Programming And Its Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Jung-Yi_Lin:thesis,
  author =       "Jung-Yi Lin",
  title =        "Layered Multi-Population Genetic Programming And Its
                 Applications",
  school =       "Computer Science, National Chiao Tung University
                 (NCTU)",
  year =         "2007",
  address =      "HsinChu, Taiwan",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming,
                 multi-population genetic programming, classification,
                 classifier design, feature selection, feature
                 construction, evolutionary computation",
  URL =          "http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/ccd=CYyGVt/result#result",
  URL =          "http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dnclcdr&s=id=%22095NCTU5394045%22.&searchmode=basic",
  URL =          "http://hdl.handle.net/11536/53780",
  URL =          "http://ir.nctu.edu.tw/bitstream/11536/53780/1/381501.pdf",
  size =         "106 pages",
  abstract =     "This study focuses on a proposed method based on
                 genetic programming (GP). Genetic programming is a
                 prominent technique of evolutionary computation (EC).
                 It mimics the evolution mechanism of biological
                 environment to determine optimal solutions for given
                 training instances. Many researchers have been devoted
                 to enhance effectiveness and efficiency of genetic
                 programming.

                 The applications of the proposed method include
                 classification and feature processing. Classification
                 problems play an important role in the development of
                 knowledge engineering. Hidden relations that can be
                 used as a basis for classification are often unclear
                 and not easily elucidated. Thus, many machine learning
                 algorithms have arisen to solve such problems. Feature
                 selection and feature generation are two important
                 techniques dealing with features. Feature selection is
                 capable of removing useless, irrelevant, redundant, and
                 noisy features. Feature generation generates new useful
                 features that could improve classification accuracy.

                 In this study we propose a layered multi-population
                 genetic programming method to solve classification
                 problems. The proposed method that can complete feature
                 selection and feature construction simultaneously is
                 also proposed. The layered multipopulation genetic
                 programming method employs layer architecture to
                 arrange multiple populations. A layer is composed of a
                 number of populations. Each population evolves to
                 generate a discriminant function. A set of discriminant
                 functions generated by one layer will be integrated and
                 be transformed by the successive layer. To improve the
                 learning performance, an adaptive mutation probability
                 tuning method is proposed. Moreover, a
                 statistical-based method is proposed to solve
                 multi-category classification problems. Several
                 experiments on classical classification problems and
                 real-world medical problems are conducted using
                 different configurations. Experimental results show
                 that the proposed methods are accurate and effective.",
  notes =        "In english. Supervisor: Dr. Wei-Pang Yang, Dr.
                 Been-Chian Chien

                 312 004D:2 96-3 003639188",
}

Genetic Programming entries for Mick Jung-Yi Lin

Citations