A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4067

@InProceedings{conf/hais/JabeenB10,
  title =        "A Framework for Optimization of Genetic Programming
                 Evolved Classifier Expressions Using Particle Swarm
                 Optimization",
  author =       "Hajira Jabeen and Abdul Rauf Baig",
  booktitle =    "Hybrid Artificial Intelligence Systems, 5th
                 International Conference, {HAIS} 2010, San
                 Sebasti{\'a}n, Spain, June 23-25, 2010. Proceedings,
                 Part {I}",
  publisher =    "Springer",
  year =         "2010",
  volume =       "6076",
  editor =       "Manuel Gra{\~n}a Romay and Emilio Corchado and 
                 M. Teresa Garc{\'i}a-Sebast{\'i}an",
  isbn13 =       "978-3-642-13768-6",
  pages =        "56--63",
  series =       "Lecture Notes in Computer Science",
  URL =          "http://link.springer.com/chapter/10.1007%2F978-3-642-13769-3_7",
  DOI =          "doi:10.1007/978-3-642-13769-3_7",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic Programming has emerged as an efficient
                 algorithm for classification. It offers several
                 prominent features like transparency, flexibility and
                 efficient data modelling ability. However, GP requires
                 long training times and suffers from increase in
                 average population size during evolution. The aim of
                 this paper is to introduce a framework to increase the
                 accuracy of classifiers by performing a PSO based
                 optimisation approach. The proposed hybrid framework
                 has been found efficient in increasing the accuracy of
                 classifiers (expressed in the form of binary expression
                 trees) in comparatively lesser number of function
                 evaluations. The technique has been tested using five
                 datasets from the UCI ML repository and found
                 efficient.",
  bibdate =      "2010-06-25",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/hais/hais2010-1.html#JabeenB10",
}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

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