Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection

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@Article{Karakasis:2008:TEC,
  title =        "Efficient Evolution of Accurate Classification Rules
                 Using a Combination of Gene Expression Programming and
                 Clonal Selection",
  author =       "Vasileios K. Karakasis and Andreas Stafylopatis",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2008",
  month =        dec,
  volume =       "12",
  number =       "6",
  pages =        "662--678",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, artificial immune systems, data
                 mining, pattern classificationCLONALG algorithm,
                 classification rules, clonal selection algorithm,
                 clonal selection principle, data class antigen, data
                 mining tasks, genotype-phenotype coincidence, hybrid
                 evolutionary technique, immune system, receptor editing
                 step",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2008.920673",
  abstract =     "A hybrid evolutionary technique is proposed for data
                 mining tasks, which combines a principle inspired by
                 the immune system, namely the clonal selection
                 principle, with a more common, though very efficient,
                 evolutionary technique, gene expression programming
                 (GEP). The clonal selection principle regulates the
                 immune response in order to successfully recognize and
                 confront any foreign antigen, and at the same time
                 allows the amelioration of the immune response across
                 successive appearances of the same antigen. On the
                 other hand, gene expression programming is the
                 descendant of genetic algorithms and genetic
                 programming and eliminates their main disadvantages,
                 such as the genotype-phenotype coincidence, though it
                 preserves their advantageous features. In order to
                 perform the data mining task, the proposed algorithm
                 introduces the notion of a data class antigen, which is
                 used to represent a class of data, the produced rules
                 are evolved by our clonal selection algorithm (CSA),
                 which extends the recently proposed CLONALG algorithm.
                 In CSA, among other new features, a receptor editing
                 step has been incorporated. Moreover, the rules
                 themselves are represented as antibodies that are coded
                 as GEP chromosomes in order to exploit the flexibility
                 and the expressiveness of such encoding. The proposed
                 hybrid technique is tested on a set of benchmark
                 problems in comparison to GEP. In almost all problems
                 considered, the results are very satisfactory and
                 outperform conventional GEP both in terms of prediction
                 accuracy and computational efficiency.",
  notes =        "Also known as \cite{4633339}",
}

Genetic Programming entries for Vasileios K Karakasis Andreas-Georgios Stafylopatis

Citations