Investigation of simplification threshold and noise level of input data in numerical simplification of genetic programs
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- @InProceedings{Kinzett:2010:cec,
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author = "David Kinzett and Mengjie Zhang and Mark Johnston",
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title = "Investigation of simplification threshold and noise
level of input data in numerical simplification of
genetic programs",
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booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
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year = "2010",
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address = "Barcelona, Spain",
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month = "18-23 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4244-6910-9",
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abstract = "In tree based Genetic Programming (GP) there is a
tendency for program sizes to increase as the run
proceeds without a corresponding improvement in
fitness. This increases resource usage, both memory and
CPU time, and may result in over-fitting the training
data. Numerical simplification is a method for removing
redundant code from the program trees as the run
proceeds. Compared with the canonical genetic
programming method, numerical simplification can
generate much smaller programs, use much shorter
evolutionary training times and achieve comparable
effectiveness performance. A key parameter of this
method is the simplification threshold. This paper
examines whether there exists any relationship between
the noise level in the input data and the optimum value
for the simplification threshold and, if it exists,
what that relationship is. Our results suggest that
there is a relationship between the optimum value of
the simplification threshold and the level of noise in
the input data and that a lower bound for the optimum
simplification threshold is equal to the noise level
and an upper bound is five times the noise level.",
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DOI = "doi:10.1109/CEC.2010.5586181",
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notes = "WCCI 2010. Also known as \cite{5586181}",
- }
Genetic Programming entries for
David Kinzett
Mengjie Zhang
Mark Johnston
Citations