Data Mining based on Gene Expression Programming and Clonal Selection

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

@InProceedings{Karakasis:2006:CEC,
  author =       "Vassilios K. Karakasis and Andreas Stafylopatis",
  title =        "Data Mining based on Gene Expression Programming and
                 Clonal Selection",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation",
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "1621--1628",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, AIS, Gene
                 Expression Programming",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/CEC.2006.1688353",
  size =         "8 pages",
  abstract =     "A hybrid evolutionary technique is proposed for data
                 mining tasks, which combines the Clonal Selection
                 Principle with Gene Expression Programming (GEP). The
                 proposed algorithm introduces the notion of Data Class
                 Antigens, which is used to represent a class of data.
                 The produced rules are evolved by a clonal selection
                 algorithm, which extends the recently proposed CLONALG
                 algorithm. In the present algorithm, among other new
                 features, a receptor editing step has been
                 incorporated. Moreover, the rules themselves are
                 represented as antibodies, which are coded as GEP
                 chromosomes, in order to exploit the flexibility and
                 the expressiveness of such encoding. The algorithm is
                 tested on some benchmark problems of the UCI
                 repository, and in particular on the set of MONK
                 problems and the Pima Indians Diabetes problem. In both
                 problems, the results in terms of prediction accuracy
                 are very satisfactory, albeit slightly less accurate
                 than those obtained by a standard GEP technique. In
                 terms of convergence rate and computational efficiency,
                 however, the technique proposed here markedly
                 outperforms the standard GEP algorithm.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for Vasileios K Karakasis Andreas-Georgios Stafylopatis

Citations