The Gene Expression Programming Applied to Demand Forecast

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

@InProceedings{conf/softcomp/BittencourtSLAO10,
  author =       "Evandro Bittencourt and Sidney Schossland and 
                 Raul Landmann and Denio {Murilo de Aguiar} and 
                 Adilson Gomes {De Oliveira}",
  title =        "The Gene Expression Programming Applied to Demand
                 Forecast",
  booktitle =    "Soft Computing Models in Industrial and Environmental
                 Applications, 5th International Workshop (SOCO 2010)",
  year =         "2010",
  editor =       "Emilio Corchado and Paulo Novais and Cesar Analide and 
                 Javier Sedano",
  volume =       "73",
  series =       "Advances in Intelligent and Soft Computing",
  pages =        "197--200",
  address =      "Guimaraes, Portugal",
  month =        jun,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  isbn13 =       "978-3-642-13160-8",
  DOI =          "doi:10.1007/978-3-642-13161-5_25",
  abstract =     "This paper examines the use of artificial intelligence
                 (in particular the application of Gene Expression
                 Programming, GEP) to demand forecasting. In the world
                 of production management, many data that are produced
                 in function of the of economic activity characteristics
                 in which they belong, may suffer, for example,
                 significant impacts of seasonal behaviours, making the
                 prediction of future conditions difficult by means of
                 methods commonly used. The GEP is an evolution of
                 Genetic Programming,which is part of the Genetic
                 Algorithms. GEP seeks for mathematical functions,
                 adjusting to a given set of solutions using a type of
                 genetic heuristics from a population of random
                 functions. In order to compare the GEP, we have used
                 the others quantitatives method. Thus, from a data set
                 of about demand of consumption of twelve products line
                 metal fittings, we have compared the forecast data.",
  bibdate =      "2010-11-04",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/softcomp/soco2010.html#BittencourtSLAO10",
}

Genetic Programming entries for Evandro Bittencourt Sidney Schossland Raul Landmann Denio Murilo de Aguiar Adilson Gomes De Oliveira

Citations