Genetic programming for non-linear equation fitting to chaotic data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7615
- @InCollection{oakley:1997:HECcd,
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author = "E. Howard N. Oakley",
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title = "Genetic programming for non-linear equation fitting to
chaotic data",
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booktitle = "Handbook of Evolutionary Computation",
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publisher = "Oxford University Press",
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publisher_2 = "Institute of Physics Publishing",
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year = "1997",
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editor = "Thomas Baeck and David B. Fogel and
Zbigniew Michalewicz",
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chapter = "section G4.3",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7503-0392-1",
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URL = "
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
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broken = "doi:10.1201/9781420050387.ptg",
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URL = "
https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921",
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size = "5 pages",
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abstract = "Current techniques for the investigation of chaos in
data series require long, noise-free experimental
measurements which are seldom available in biological
and medical work. Genetic programming was seen to offer
potential in a number of ways, and was therefore
initially used to forecast future data values from very
short and noisy input data. Genetic programming proved
to be as effective a forecasting tool as others
advocated in the literature. Forecasting error
initially increased quickly with increasing length of
prediction, then increased more slowly, according to a
biphasic pattern described previously; the gradients of
each limb may be used as a crude indicator of the sum
of positive Lyapunov exponents. Although no
S-expression ever exactly replicated that used to
generate the data, fittest S-expressions did yield
useful structural data. Furthermore, the efficacy of
forecasting remained high even when noise was added to
the data series. The application of genetic programming
to original and surrogate data series may be a useful
test between chaos and randomness. Runs on surrogate
series failed to achieve the high fitness values seen
with real data, and were distinguished by shallow and
homogeneous populations of S-expressions.",
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notes = "Mackey and Glass, 1977",
- }
Genetic Programming entries for
Howard Oakley
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