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:1.7615

@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 Vasilis Stilianakis

Citations