Application of Cellular Genetic Programming in Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Aleksandra Takac",
  title =        "Application of Cellular Genetic Programming in Data
  booktitle =    "Znalosti",
  year =         "2004",
  editor =       "Vaclav Snasel and Michal Kratky",
  address =      "Brno, Czech Republic",
  month =        "25-27 " # feb,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "12 pages",
  abstract =     "Paper examines application of genetic programming
                 framework in the problem of knowledge discovery in
                 databases, more precisely in the task of
                 classification. Genetic programming possesses certain
                 advantages that make it suitable for application in
                 data mining, such as robustness of algorithm or its
                 convenient structure for rule generation to name a few.
                 This study focuses on one type of parallel genetic
                 algorithms ? cellular (diffusion) model. Emphasis is
                 placed on the improvement of efficiency and scalability
                 of data mining algorithm, which could be achieved by
                 integration of algorithm with databases and by
                 employing a cellular framework, as well as examining
                 parallel approaches. Cellular model of genetic
                 programming that exploits SQL queries is implemented
                 and applied to classification task. Achieved results
                 are compared with other machine learning algorithms.",
  notes =        "",

Genetic Programming entries for Aleksandra Takac