GPSO: A Framework for Optimization of Genetic Programming Classifier Expressions for Binary Classification Using Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Jabeen:2012:ijicic,
  author =       "Hajira Jabeen and Abdul Rauf Baig",
  title =        "GPSO: A Framework for Optimization of Genetic
                 Programming Classifier Expressions for Binary
                 Classification Using Particle Swarm Optimization",
  journal =      "International journal of innovative computing,
                 information and control",
  year =         "2012",
  volume =       "8",
  number =       "1 A",
  pages =        "233--242",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming,
                 classification, particle swarm optimisation,
                 optimisation, expressions",
  ISSN =         "1349-418X",
  publisher =    "ICIC international",
  URL =          "http://www.ijicic.org/ijicic-10-06097.pdf",
  size =         "10 pages",
  abstract =     "Genetic Programming (GP) is an emerging classification
                 tool known for its flexibility, robustness and
                 lucidity. However, GP suffers from a few limitations
                 like long training time, bloat and lack of convergence.
                 In this paper, we have proposed a hybrid technique that
                 overcomes these drawbacks by improving the performance
                 of GP evolved classifiers using Particle Swarm
                 Optimisation (PSO). This hybrid classification
                 technique is a two-step process. In the first phase, we
                 have used GP for evolution of arithmetic classifier
                 expressions (ACE). In the second phase, we add weights
                 to these expressions and optimise them using PSO. We
                 have compared the performance of proposed frame- work
                 (GPSO) with the GP classification technique over twelve
                 benchmark data sets. The results conclude that the
                 proposed optimisation strategy outperforms GP with
                 respect to classification accuracy and less
                 computation.",
}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

Citations