A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming
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gp-bibliography.bib Revision:1.8081
- @Misc{radwan2024comparisonrecentalgorithmssymbolic,
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title = "A Comparison of Recent Algorithms for Symbolic
Regression to Genetic Programming",
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author = "Yousef A. Radwan and Gabriel Kronberger and
Stephan Winkler",
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howpublished = "ArXiv 2406.03585",
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year = "2024",
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month = "5 " # jun,
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keywords = "genetic algorithms, genetic programming, Symbolic
regression, Machine learning, Transformers, Domain
Knowledge, ANN, Neural Networks",
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primaryclass = "cs.LG",
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URL = "https://arxiv.org/abs/2406.03585",
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URL = "https://arxiv.org/abs/2406.03585v1",
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size = "15 pages",
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abstract = "Symbolic regression is a machine learning method with
the goal to produce interpretable results. Unlike other
machine learning methods such as, e.g. random forests
or neural networks, which are opaque, symbolic
regression aims to model and map data in a way that can
be understood by scientists. Recent advancements, have
attempted to bridge the gap between these two fields;
new methodologies attempt to fuse the mapping power of
neural networks and deep learning techniques with the
explanatory power of symbolic regression. we examine
these new emerging systems and test the performance of
an end-to-end transformer model for symbolic regression
versus the reigning traditional methods based on
genetic programming that have spearheaded symbolic
regression throughout the years. We compare these
systems on novel datasets to avoid bias to older
methods who were improved on well-known benchmark
datasets. Our results show that traditional GP methods
as implemented e.g., by Operon still remain superior to
two recently published symbolic regression methods.",
- }
Genetic Programming entries for
Yousef A Radwan
Gabriel Kronberger
Stephan M Winkler
Citations