A Hybrid Genetic Programming Method in Optimization and Forecasting: A Case Study of the Broadband Penetration in OECD Countries

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

@Article{Salpasaranis:2012:AOR,
  author =       "Konstantinos Salpasaranis and Vasilios Stylianakis",
  title =        "A Hybrid Genetic Programming Method in Optimization
                 and Forecasting: A Case Study of the Broadband
                 Penetration in {OECD} Countries",
  journal =      "Advances in Operations Research",
  year =         "2012",
  volume =       "2012",
  pages =        "Article ID 904797",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Hindawi Publishing Corporation",
  bibdate =      "2012-10-31",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/advor/advor2012.html#KonstantinosS12",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.470.517",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.470.517",
  URL =          "http://downloads.hindawi.com/journals/aor/2012/904797.pdf",
  DOI =          "doi:10.1155/2012/904797",
  size =         "32 pages",
  abstract =     "The introduction of a hybrid genetic programming
                 method (hGP) in fitting and forecasting of the
                 broadband penetration data is proposed. The hGP uses
                 some well-known diffusion models, such as those of
                 Gompertz, Logistic, and Bass, in the initial population
                 of the solutions in order to accelerate the algorithm.
                 The produced solutions models of the hGP are used in
                 fitting and forecasting the adoption of broadband
                 penetration. We investigate the fitting performance of
                 the hGP, and we use the hGP to forecast the broadband
                 penetration in OECD (Organisation for Economic
                 Co-operation and Development) countries. The results of
                 the optimised diffusion models are compared to those of
                 the hGP-generated models. The comparison indicates that
                 the hGP manages to generate solutions with
                 high-performance statistical indicators. The hGP
                 cooperates with the existing diffusion models, thus
                 allowing multiple approaches to forecasting. The
                 modified algorithm is implemented in the Python
                 programming language, which is fast in execution time,
                 compact, and user friendly.",
  notes =        "Electrical and Computer Engineering Department,
                 University of Patras, 26500 Rio Patra, Greece.

                 Also known as \cite{journals/advor/KonstantinosS12}",
}

Genetic Programming entries for Konstantinos Salpasaranis Vasilios Stylianakis

Citations