Genetic programming: Current trends and applications in computational finance
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{3092,
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author = "Gabriel K. Kronberger and Michael Affenzeller and
Stefan Fink",
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title = "Genetic programming: Current trends and applications
in computational finance",
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booktitle = "Recent advances in computational finance",
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publisher = "Nova Science Publishers, Inc.",
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year = "2013",
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editor = "Nikolaos S. Thomaidis and Gordon H. Dash",
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chapter = "6",
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pages = "99--115",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "9781626181236",
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URL = "http://research.fh-ooe.at/en/publication/3092",
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URL = "https://www.novapublishers.com/catalog/product_info.php?products_id=39839",
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abstract = "Genetic programming (GP) is a general problem solving
approach that uses evolutionary dynamics to find
computer programs that solve the specified problems
when executed. GP has been applied to a wide range of
problems in various domains; in particular, for solving
problems in quantitative finance and in econometrics.
In this chapter we describe the fundamentals of GP and
evolutionary algorithms and give a brief survey of
relevant literature and results that have been achieved
using GP in financial applications. We also present in
detail how GP can be applied for identification of
variable interaction networks and for prediction of
multivariate nonlinear time-series. Furthermore, we
demonstrate these two approaches using two practical
examples, namely the identification of dependencies of
leading indicators and economic variables in the US
economy, and the prediction of European interest rate
swap rates.",
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notes = "University of Applied Science Upper Austria, Austria,
and others",
- }
Genetic Programming entries for
Gabriel Kronberger
Michael Affenzeller
Stefan Fink
Citations