Overfitting or Poor Learning: A Critique of Current Financial Applications of GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{chen03,
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author = "Shu-Heng Chen and Tzu-Wen Kuo",
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title = "Overfitting or Poor Learning: A Critique of Current
Financial Applications of GP",
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booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
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year = "2003",
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editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
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volume = "2610",
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series = "LNCS",
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pages = "34--46",
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address = "Essex",
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publisher_address = "Berlin",
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month = "14-16 " # apr,
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organisation = "EvoNet",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-00971-X",
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DOI = "doi:10.1007/3-540-36599-0_4",
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abstract = "Motivated by a measure of predictability, this paper
uses the extracted signal ratio as a measure of the
degree of overfitting. With this measure, we examine
the performance of one type of overfitting-avoidance
design frequently used in financial applications of GP.
Based on the simulation results run with the software
Simple GP, we find that this design is not effective in
avoiding overfitting. Furthermore, within the range of
search intensity typically considered by these
applications, we find that underfitting, instead of
overfitting, is the more prevalent problem. This
problem becomes more serious when the data is generated
by a process that has a high degree of algorithmic
complexity. This paper, therefore, casts doubt on the
conclusions made by those early applications regarding
the poor performance of GP, and recommends that changes
be made to ensure progress.",
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notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
- }
Genetic Programming entries for
Shu-Heng Chen
Tzu-Wen Kuo
Citations