Application of Soft Computing Techniques to Classification of Licensed Subjects

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

  author =       "Jiri Kubalik and Marcel Jirina and Oldrich Stary and 
                 Lenka Lhotska and Jan Suchy",
  title =        "Application of Soft Computing Techniques to
                 Classification of Licensed Subjects",
  booktitle =    "Emerging Solutions for Future Manufacturing Systems:
                 IFP TC 5 / WG 5.5 Sixth IFIP International Conference
                 on Information Technology for Balanced Automation
                 Systems in Manufacturing and Services",
  year =         "2004",
  editor =       "Luis M. Camarinha-Matos",
  volume =       "159",
  series =       "IFIPAICT",
  pages =        "481--488",
  address =      "27--29 September",
  month =        "Vienna, Austria",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-22829-7",
  DOI =          "doi:10.1007/0-387-22829-2_52",
  URL =          "",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "",
  URL =          "",
  size =         "8 pages",
  abstract =     "This paper presents an application of soft computing
                 techniques to the construction of decision support tool
                 used for identifying the economically unstable licensed
                 subjects. The work has been initiated by the Czech
                 Energy Regulatory Office whose main mission is to guard
                 the regular heat supply without significant
                 disturbances. Thus the main goal is to develop a tool
                 for automatic identification of the companies that
                 could cancel the supply due to economic problems
                 without detailed examination of each company. In order
                 to achieve the goal two approaches have been chosen.
                 The first one is based on development of an aggregate
                 evaluation criterion for assessing the firms. The other
                 one uses artificial neural networks and multivariate
                 decision trees induced with genetic programming for
                 classification of the firms.",
  notes =        "published 2005",

Genetic Programming entries for Jiri Kubalik Marcel Jirina Oldrich Stary Lenka Lhotska Jan Suchy