Linear programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{FLAIRS99-019,
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title = "Linear programming",
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author = "Jin Li and Edward P. K. Tsang",
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booktitle = "Proceedings of Twelth International Florida Artificial
Intelligence Research Society Conference",
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year = "1999",
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publisher = "AAAI Press",
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keywords = "genetic algorithms, genetic programming",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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language = "en",
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oai = "oai:CiteSeerX.psu:10.1.1.538.6697",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.538.6697",
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URL = "https://www.aaai.org/Papers/FLAIRS/1999/FLAIRS99-019.pdf",
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size = "5 pages",
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abstract = "Recent studies in finance domain suggest that
technical analysis may have merit to predictability of
stock. Technical rules are widely used for market
assessment and timing. For example, moving average
rules are used to make buy or sell decisions at each
day. In this paper, to explore the potential prediction
power of technical analysis, we present a genetic
programming based system FGP (Financial Genetic
Programming), which specialises in taking some well
known technical rules and adapting them to prediction
problems. FGP uses the power of genetic programming to
generate decision trees through efficient combination
of technical rules with self-adjusted thresholds. The
generated rules are more suitable for the prediction
problem at hand. FGP was tested extensively on
historical DJIA (Dow Jones Industrial Average) index
data through a specific prediction problem. Preliminary
results show that it outperforms commonly used,
non-adaptive, individual technical rules with respect
to prediction accuracy and average annualised rate of
return over two different out-of-sample test periods
(three and a half year in each period).",
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notes = "FLAIRS-99",
- }
Genetic Programming entries for
Jin Li
Edward P K Tsang
Citations