Application of Fixed-Structure Genetic Programming for Classification

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

@InProceedings{conf/icira/WuM12,
  author =       "Xiaojun Wu and Yue Ma",
  title =        "Application of Fixed-Structure Genetic Programming for
                 Classification",
  booktitle =    "Proceedings of the 5th International Conference
                 Intelligent Robotics and Applications, ICIRA 2012, Part
                 I",
  year =         "2012",
  editor =       "Chun-Yi Su and Subhash Rakheja and Honghai Liu",
  volume =       "7506",
  series =       "Lecture Notes in Computer Science",
  pages =        "22--33",
  address =      "Montreal, Canada",
  month =        oct # " 3-5",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Classifier
                 systems, Data mining, Classification boundary",
  isbn13 =       "978-3-642-33508-2",
  DOI =          "doi:10.1007/978-3-642-33509-9_3",
  bibdate =      "2012-10-14",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icira/icira2012-1.html#WuM12",
  size =         "12 pages",
  abstract =     "There are three improvements based on GP algorithm in
                 this paper and a fixed-structure GP algorithm for
                 classification was proposed. Traditional GP algorithm
                 relies on non-fixed-length tree structure to describe
                 the classification problems. This algorithm uses a
                 fixed-length linear structure instead of the
                 traditional structure and optimises the leaf nodes
                 coefficients based on the hill-climbing algorithm.
                 Meanwhile, aiming at the samples on the classification
                 boundaries, an optimisation method of classification
                 boundaries is proposed which makes the classification
                 boundaries continuously tend to the optimal solutions
                 in the program evolution process. At the end, an
                 experiment is made by using this improved algorithm and
                 a two-categories sample set with classification
                 boundary is correctly classified (This sample set is an
                 accurate data set from UCI database) Then it shows the
                 analysis of classification results and the
                 classification model produced by this algorithm. The
                 experimental results indicates that the GP
                 classification algorithm with fixed structure could
                 improve the classification accuracy rate and accelerate
                 the solutions convergence speed, which is of great
                 significance in the practical application of
                 classification systems based on GP algorithm.",
}

Genetic Programming entries for Xiaojun Wu Yue Ma

Citations