Evaluating Methods for Constant Optimization of Symbolic Regression Benchmark Problems
Created by W.Langdon from
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- @InProceedings{deMelo:2015:BRACIS,
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author = "Vinicius Veloso {de Melo} and Benjamin Fowler and
Wolfgang Banzhaf",
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booktitle = "2015 Brazilian Conference on Intelligent Systems
(BRACIS)",
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title = "Evaluating Methods for Constant Optimization of
Symbolic Regression Benchmark Problems",
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year = "2015",
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pages = "25--30",
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address = "Natal, Brazil",
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size = "6 pages",
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abstract = "Constant optimisation in symbolic regression is an
important task addressed by several researchers. It has
been demonstrated that continuous optimization
techniques are adequate to find good values for the
constants by minimizing the prediction error. In this
paper, we evaluate several continuous optimization
methods that can be used to perform constant
optimization in symbolic regression. We have selected
14 well-known benchmark problems and tested the
performance of diverse optimization methods in finding
the expected constant values, assuming that the correct
formula has been found. The results show that
Levenberg-Marquardt presented the highest success rate
among the evaluated methods, followed by Powell's and
Nelder-Mead's Simplex. However, two benchmark problems
were not solved, and for two other problems the
Levenberg-Marquardt was largely outperformed by
Nelder-Mead Simplex in terms of success rate. We
conclude that even though a symbolic regression
technique may find the correct formula, constant
optimization may fail, thus, this may also happen
during the search for a formula and may guide the
method towards the wrong solution. Also, the efficiency
of LM in finding high-quality solutions by using only a
few function evaluations could serve as inspiration for
the development of better symbolic regression
methods.",
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Curve-fitting, Least-squares, Nonlinear
regression",
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DOI = "doi:10.1109/BRACIS.2015.55",
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month = "4-7 " # nov,
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notes = "Benchmarks p27 'taken from
\cite{McDermott:2012:GECCO}'. Also known as
\cite{7423910}",
- }
Genetic Programming entries for
Vinicius Veloso de Melo
Benjamin Fowler
Wolfgang Banzhaf
Citations