Genetic Programming with Dynamic Fitness for a Remote Sensing Application
Created by W.Langdon from
gp-bibliography.bib Revision:1.8355
- @InProceedings{FonluptPPSN2000,
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author = "Cyril W. B. Fonlupt and Denis Robilliard",
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title = "Genetic Programming with Dynamic Fitness for a Remote
Sensing Application",
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booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th
International Conference",
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editor = "Marc Schoenauer and Kalyanmoy Deb and
G{\"u}nter Rudolph and Xin Yao and Evelyne Lutton and
Juan Julian Merelo and Hans-Paul Schwefel",
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year = "2000",
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publisher = "Springer Verlag",
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address = "Paris, France",
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month = "16-20 " # sep,
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volume = "1917",
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series = "LNCS",
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pages = "191--200",
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keywords = "genetic algorithms, genetic programming, Coastal sea
water, K1, K2",
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isbn13 = "978-3-540-45356-7",
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URL = "
http://www-lil.univ-littoral.fr/~robillia/Publis/lil-00-2.ps.gz",
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URL = "
https://rdcu.be/enmLp",
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DOI = "
doi:10.1007/3-540-45356-3_19",
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size = "10 pages",
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abstract = "The ocean color problem consists in evaluating ocean
components concentrations (phytoplankton, sediment,
yellow substance) from sunlight reflectance or
luminance values at selected wavelengths in the visible
band. The interest of this application increases with
the availability of new satellite sensors. Moreover
monitoring phytoplankton concentrations is a key point
for a wide set of problems ranging from greenhouse
effect to industrial fishing and signaling toxic algae
blooms. To our knowledge, it is the first attempt at
this regression problem with Genetic Programming (GP).
We show that GP outperforms traditional polynomial fits
and rivals artificial neural nets in the case of open
ocean waters. We improve on previous works by also
solving a range of coastal waters types, providing
detailed results on estimation errors. Some experiments
were conducted with a dynamic fitness GP algorithm in
order to speed up computing time through a process of
progressive learning.",
- }
Genetic Programming entries for
Cyril Fonlupt
Denis Robilliard
Citations