CLONAL-GP Framework for Artificial Immune System Inspired Genetic Programming for Classification

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

@InProceedings{DBLP:conf/kes/JabeenB10,
  author =       "Hajira Jabeen and Abdul Rauf Baig",
  title =        "CLONAL-GP Framework for Artificial Immune System
                 Inspired Genetic Programming for Classification",
  booktitle =    "14th International Conference on Knowledge-Based and
                 Intelligent Information and Engineering Systems, KES
                 2010, Part I",
  year =         "2010",
  editor =       "Rossitza Setchi and Ivan Jordanov and 
                 Robert J. Howlett and Lakhmi C. Jain",
  series =       "Lecture Notes in Computer Science",
  volume =       "6276",
  pages =        "61--68",
  address =      "Cardiff",
  month =        sep # " 8-10",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-15386-0",
  DOI =          "doi:10.1007/978-3-642-15387-7_10",
  abstract =     "This paper presents a novel framework for artificial
                 immune system (AIS) inspired evolution in Genetic
                 Programming (GP). A typical GP system uses the
                 reproduction operators mimicking the phenomena of
                 natural evolution to search for efficient classifiers.
                 The proposed framework uses AIS inspired clonal
                 selection algorithm to evolve classifiers using GP. The
                 clonal selection principle states that, in human immune
                 system, high affinity cells that recognise the invading
                 antigens are selected to proliferate. Furthermore,
                 these cells undergo hyper mutation and receptor editing
                 for maturation. In this paper, we propose a
                 computational implementation of the clonal selection
                 principle. The motivation for using non-Darwinian
                 evolution includes avoidance of bloat, training time
                 reduction and simpler classifiers. We have performed
                 empirical analysis of proposed framework over a
                 benchmark dataset from UCI repository. The CLONAL-GP is
                 contrasted with two variants of GP based classification
                 mechanisms and results are found encouraging.",
  notes =        "KES (1)",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

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