Combining different meta-heuristics to improve the predictability of a Financial Forecasting algorithm
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- @InProceedings{Aluko:2014:CIFEr,
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author = "Babatunde Aluko and Dafni Smonou and
Michael Kampouridis and Edward Tsang",
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booktitle = "IEEE Conference on Computational Intelligence for
Financial Engineering Economics (CIFEr 2104)",
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title = "Combining different meta-heuristics to improve the
predictability of a Financial Forecasting algorithm",
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year = "2014",
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month = "27-28 " # mar,
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pages = "333--340",
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size = "8 pages",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CIFEr.2014.6924092",
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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