GP made faster with semantic surrogate modelling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Kattan:2016:IS,
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author = "Ahmed Kattan and Alexandros Agapitos and
Yew-Soon Ong and Ateq A. Alghamedi and Michael O'Neill",
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title = "{GP} made faster with semantic surrogate modelling",
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journal = "Information Sciences",
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volume = "355-356",
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pages = "169--185",
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year = "2016",
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keywords = "genetic algorithms, genetic programming, Surrogate
modelling, K-NN, Symbolic regression, Classification,
Time-series forecasting",
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ISSN = "0020-0255",
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DOI = "doi:10.1016/j.ins.2016.03.030",
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URL = "http://www.sciencedirect.com/science/article/pii/S0020025516301992",
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abstract = "Genetic Programming (GP) is known to be expensive in
cases where the fitness evaluation is computationally
demanding, i.e., object detection, programmatic
compression, image processing applications. The paper
introduces a method that reduces the amount of fitness
evaluations that are required to obtain good solutions.
We consider the supervised learning setting, where a
training set of input vectors are collectively mapped
to a vector of outputs, and then a loss function is
used to map the vector of outputs to a scalar fitness
value. Saving of fitness evaluations is achieved
through the use of two components. The first component
is surrogate model that predicts trees output for a
particular input vector xi based on the similarity
between xi and other input vectors in the training set
for which the candidate solution has been already
evaluated with. The second component, is a simple
linear equation to control the size of a sub-training
set that is used to train GP trees. This linear
equation allows the size of the sub-training set to
dynamically increase or decrease based on the status of
the search. The proposed method referred to as SSGP.
Empirical results in 17 different problems, from three
different categories, demonstrate that SSGP is able to
obtain solutions of similar quality with those obtained
using several benchmark GP systems, but with a much
smaller computation time. The simplicity of the
proposed method and the ease of its implementation is
one of the most appealing aspects of its future
utility.",
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notes = "Also known as \cite{KattanINS2016}",
- }
Genetic Programming entries for
Ahmed Kattan
Alexandros Agapitos
Yew-Soon Ong
Ateq A Alghamedi
Michael O'Neill
Citations