Local Optimization Often is Ill-conditioned in Genetic Programming for Symbolic Regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Kronberger:2022:SYNASC,
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author = "Gabriel Kronberger",
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booktitle = "2022 24th International Symposium on Symbolic and
Numeric Algorithms for Scientific Computing (SYNASC)",
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title = "Local Optimization Often is Ill-conditioned in Genetic
Programming for Symbolic Regression",
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year = "2022",
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pages = "304--310",
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abstract = "Gradient-based local optimisation has been shown to
improve results of genetic programming (GP) for
symbolic regression. Several state-of-the-art GP
implementations use iterative nonlinear least squares
(NLS) algorithms such as the Levenberg-Marquardt
algorithm for local optimisation. The effectiveness of
NLS algorithms depends on appropriate scaling and
conditioning of the optimisation problem. This has so
far been ignored in symbolic regression and GP
literature. In this study we use a singular value
decomposition of NLS Jacobian matrices to determine the
numeric rank and the condition number. We perform
experiments with a GP implementation and six different
benchmark datasets. Our results show that
rank-deficient and ill-conditioned Jacobian matrices
occur frequently and for all datasets. The issue is
less extreme when restricting GP tree size and when
using many non-linear functions in the function set.",
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keywords = "genetic algorithms, genetic programming, Jacobian
matrices, Scientific computing, Benchmark testing,
Approximation algorithms, Iterative algorithms,
Optimisation, Evolutionary computing and Nonlinear
approximation, Least squares methods, Gradient
methods",
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DOI = "doi:10.1109/SYNASC57785.2022.00055",
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ISSN = "2470-881X",
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month = sep,
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notes = "Also known as \cite{10130940}",
- }
Genetic Programming entries for
Gabriel Kronberger
Citations