Grammar-based Vectorial Genetic Programming for Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Fleck:2021:GPTP,
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author = "Philipp Fleck and Stephan Winkler and
Michael Kommenda and Michael Affenzeller",
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title = "Grammar-based Vectorial Genetic Programming for
Symbolic Regression",
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booktitle = "Genetic Programming Theory and Practice XVIII",
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year = "2021",
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editor = "Wolfgang Banzhaf and Leonardo Trujillo and
Stephan Winkler and Bill Worzel",
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series = "Genetic and Evolutionary Computation",
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pages = "21--43",
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address = "East Lansing, USA",
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month = "19-21 " # may,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-16-8112-7",
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DOI = "doi:10.1007/978-981-16-8113-4_2",
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abstract = "Vectorial Genetic Programming (GP) is a young branch
of GP, where the training data for symbolic models not
only include regular, scalar variables, but also allow
vector variables. Also, the models abilities are
extended to allow operations on vectors, where most
vector operations are simply performed component-wise.
Additionally, new aggregation functions are introduced
that reduce vectors into scalars, allowing the model to
extract information from vectors by itself, thus
eliminating the need of prior feature engineering that
is otherwise necessary for traditional GP to use vector
data. And due to the white-box nature of symbolic
models, the operations on vectors can be as easily
interpreted as regular operations on scalars. In this
paper, we extend the ideas of vectorial GP of previous
authors, and propose a grammar-based approach for
vectorial GP that can deal with various challenges
noted. To evaluate grammar-based vectorial GP, we have
designed new benchmark functions that contain both
scalar and vector variables, and show that traditional
GP falls short very quickly for certain scenarios.
Grammar-based vectorial GP, however, is able to solve
all presented benchmarks.",
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notes = "Part of \cite{Banzhaf:2021:GPTP} published after the
workshop in 2022",
- }
Genetic Programming entries for
Philipp Fleck
Stephan M Winkler
Michael Kommenda
Michael Affenzeller
Citations