Application of Genetic Programming Classification in an Industrial Process Resulting in Greenhouse Gas Emission Reductions

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

@InProceedings{Lotz:2010:EvoENVIRONMENT,
  author =       "Marco Lotz and Sara Silva",
  title =        "Application of Genetic Programming Classification in
                 an Industrial Process Resulting in Greenhouse Gas
                 Emission Reductions",
  booktitle =    "EvoENVIRONMENT",
  year =         "2010",
  editor =       "Cecilia {Di Chio} and Anthony Brabazon and 
                 Gianni A. {Di Caro} and Marc Ebner and Muddassar Farooq and 
                 Andreas Fink and Jorn Grahl and Gary Greenfield and 
                 Penousal Machado and Michael O'Neill and 
                 Ernesto Tarantino and Neil Urquhart",
  volume =       "6025",
  series =       "LNCS",
  pages =        "131--140",
  address =      "Istanbul",
  month =        "7-9 " # apr,
  organisation = "EvoStar",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-12241-5",
  DOI =          "doi:10.1007/978-3-642-12242-2_14",
  abstract =     "This paper compares Genetic Programming and the
                 Classification and Regression Trees algorithm as data
                 driven modelling techniques on a case study in the
                 ferrous metals and steel industry in South Africa.
                 These industries are responsible for vast amounts of
                 greenhouse gas production, and greenhouse gas emission
                 reduction incentives exist that can fund these
                 abatement technologies. Genetic Programming is used to
                 derive pure classification rule sets, and to derive a
                 regression model used for classification, and both
                 these results are compared to the results obtained by
                 decision trees, regarding accuracy and human
                 interpretability. Considering the overall simplicity of
                 the rule set obtained by Genetic Programming, and the
                 fact that its accuracy was not surpassed by any of the
                 other methods, we consider it to be the best approach,
                 and highlight the advantages of using a rule based
                 classification system. We conclude that Genetic
                 Programming can potentially be used as a process model
                 that reduces greenhouse gas production.",
  notes =        "EvoENVIRONMENT'2010 held in conjunction with
                 EuroGP'2010 EvoCOP2010 EvoBIO2010",
}

Genetic Programming entries for Marco Lotz Sara Silva

Citations