Correlation versus RMSE Loss Functions in Symbolic Regression Tasks
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Banzhaf:2022:GPTP.2,
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author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch",
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title = "Correlation versus {RMSE} Loss Functions in Symbolic
Regression Tasks",
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booktitle = "Genetic Programming Theory and Practice XIX",
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year = "2022",
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editor = "Leonardo Trujillo and Stephan M. Winkler and
Sara Silva and Wolfgang Banzhaf",
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series = "Genetic and Evolutionary Computation",
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pages = "31--55",
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address = "Ann Arbor, USA",
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month = jun # " 2-4",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-981-19-8459-4",
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DOI = "doi:10.1007/978-981-19-8460-0_2",
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abstract = "The use of correlation as a fitness function is
explored in symbolic regression tasks and its
performance is compared against a more typical RMSE
fitness function. Using correlation with an alignment
step to conclude the evolution led to significant
performance gains over RMSE as a fitness function.
Employing correlation as a fitness function led to
solutions being found in fewer generations compared to
RMSE. We also found that fewer data points were needed
in a training set to discover correct equations. The
Feynman Symbolic Regression Benchmark as well as
several other old and recent GP benchmark problems were
used to evaluate performance.",
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notes = "Part of \cite{Banzhaf:2022:GPTP} published after the
workshop in 2023",
- }
Genetic Programming entries for
Nathaniel Haut
Wolfgang Banzhaf
William F Punch
Citations