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

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@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