Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Aluko:2014:CIFEr,
  author =       "Babatunde Aluko and Dafni Smonou and 
                 Michael Kampouridis and Edward Tsang",
  booktitle =    "IEEE Conference on Computational Intelligence for
                 Financial Engineering Economics (CIFEr 2104)",
  title =        "Combining different meta-heuristics to improve the
                 predictability of a Financial Forecasting algorithm",
  year =         "2014",
  month =        "27-28 " # mar,
  pages =        "333--340",
  size =         "8 pages",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIFEr.2014.6924092",
  abstract =     "Hyper-heuristics have successfully been applied to a
                 vast number of search and optimisation problems. One of
                 the novelties of hyper-heuristics is the fact that they
                 manage and automate the meta-heuristic's selection
                 process. In this paper, we implemented and analysed a
                 hyper-heuristic framework on three meta-heuristics
                 namely Simulated Annealing, Tabu Search, and Guided
                 Local Search, which had successfully been applied in
                 the past to a Financial Forecasting algorithm called
                 EDDIE. EDDIE uses Genetic Programming to extract and
                 learn from historical data in order to predict future
                 financial market movements. Results show that the
                 algorithm's effectiveness has been improved, thus
                 making the combination of meta-heuristics under a
                 hyper-heuristic framework an effective Financial
                 Forecasting approach.",
  notes =        "Also known as \cite{6924092}",
}

Genetic Programming entries for Babatunde Aluko Dafni Smonou Michael Kampouridis Edward P K Tsang

Citations