Advances in applying genetic programming to machine learning, focussing on classification problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "S. M. Winkler and M. Affenzeller and S. Wagner",
  title =        "Advances in applying genetic programming to machine
                 learning, focussing on classification problems",
  booktitle =    "20th International Parallel and Distributed Processing
                 Symposium, IPDPS 2006",
  year =         "2006",
  pages =        "8 pp.?",
  address =      "Rhodes Island",
  month =        "25-29 " # apr # " 2006",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0054-6",
  DOI =          "doi:10.1109/IPDPS.2006.1639524",
  abstract =     "A genetic programming based approach for solving
                 classification problems is presented in this paper.
                 Classification is understood as the act of placing an
                 object into a set of categories, based on the object's
                 properties; classification algorithms are designed to
                 learn a function which maps a vector of object features
                 into one of several classes. This is done by analysing
                 a set of input-output examples (training samples) of
                 the function. Here we present a method based on the
                 theory of genetic algorithms and genetic programming
                 that interprets classification problems as optimisation
                 problems: Each presented instance of the classification
                 problem is interpreted as an instance of an
                 optimization problem, and a solution is found by a
                 heuristic optimisation algorithm. The major new aspects
                 presented in this paper are suitable genetic operators
                 for this problem class (mainly the creation of new
                 hypotheses by merging already existing ones and their
                 detailed evaluation) we have designed and implemented.
                 The experimental part of the paper documents the
                 results produced using new hybrid variants of genetic
                 algorithms as well as investigated parameter
  notes =        "CD-ROM

                 Dept. of Software Eng., Upper Austrian Univ. of Appl.
                 Sci., Hagenberg, Austria",

Genetic Programming entries for Stephan M Winkler Michael Affenzeller Stefan Wagner