Adapting to Concept Drift with Genetic Programming for Classifying Streaming Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Murray Smith and Vic Ciesielski",
  title =        "Adapting to Concept Drift with Genetic Programming for
                 Classifying Streaming Data",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "5026--5033",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7748327",
  abstract =     "Concept drift in data streams is a change in the
                 underlying distribution that can cause algorithms that
                 are classifying them to have increased error. There is
                 a need for algorithms that can adapt to these changes.
                 Genetic programming is one such algorithm that can
                 adapt to streaming data however its use in this area is
                 somewhat unexplored. It is hoped that because genetic
                 programming is a population based method the variety of
                 solutions tested every generation will enable it to
                 adapt quickly. The adaptation speed can be increased by
                 determining suitable parameters and settings. In order
                 to test these ideas, experiments were run on several
                 synthetic and one world streaming data set. The results
                 found that genetic programming was capable of adapting
                 quickly to concept drift and that increased",
  notes =        "WCCI2016",

Genetic Programming entries for Murray Smith Victor Ciesielski