Short-term load forecasting using Cartesian Genetic Programming: An efficient evolutive strategy: Case: Australian electricity market

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Giacometto:2015:IECON,

• author = "Francisco Giacometto and Enric Sala and Konstantinos Kampouropoulos and Luis Romeral",
• booktitle = "41st Annual Conference of the IEEE Industrial Electronics Society, IECON 2015",
• title = "Short-term load forecasting using Cartesian Genetic Programming: An efficient evolutive strategy: Case: Australian electricity market",
• year = "2015",
• pages = "005087--005094",
• abstract = "Currently, the Cartesian Genetic Programming approaches applied to regression problems tackle the evolution strategy from a static point of view. They are confident on the evolving capacity of the genetic algorithm, with less attention being paid over alternative methods to enhance the generalisation error of the trained models or the convergence time of the algorithm. On this article, we propose a novel efficient strategy to train models using Cartesian Genetic Programming at a faster rate than its basic implementation. This proposal achieves greater generalisation and enhances the error convergence. Finally, the complete methodology is tested using the Australian electricity market as a case study.",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
• DOI = "doi:10.1109/IECON.2015.7392898",
• month = nov,
• notes = "Also known as \cite{7392898}",

}

Genetic Programming entries for Francisco Giacometto Enric Sala Konstantinos Kampouropoulos Luis Romeral

Citations