Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability
Created by W.Langdon from
gp-bibliography.bib Revision:1.7325
- @InProceedings{fong:2023:GECCOcomp,
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author = "Kei Sen Fong and Shelvia Wongso and Mehul Motani",
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title = "Evolutionary Symbolic Regression: Mechanisms from the
Perspectives of Morphology and Adaptability",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Alberto Moraglio",
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pages = "21--22",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, symbolic
regression",
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isbn13 = "9798400701191",
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DOI = "
doi:10.1145/3583133.3595830",
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size = "2 pages",
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abstract = "Symbolic Regression (SR) is the task of finding
closed-form analytical expressions that describe the
relationship between variables in a dataset. In this
work, werethink SR and introduce mechanisms from two
perspectives: morphology and adaptability. Morphology:
Man-made heuristics are typically utilized in SR
algorithms to influence the morphology (or structure)
of candidate expressions, potentially introducing
unintentional bias and data leakage. To address this
issue, we create a depth-aware mathematical language
model trained on terminal walks of expression trees, as
a replacement to these heuristics. Adaptability: We
promote alternating fitness functions across
generations, eliminating equations that perform well in
only one fitness function and as a result, discover
expressions that are closer to the true functional
form. We demonstrate this by alternating fitness
functions that quantify faithfulness to values (via
MSE) and empirical derivatives (via a novel
theoretically justified fitness metric coined MSEDI).
Proof-of-concept: We combine these ideas into a
minimalistic evolutionary SR algorithm that outperforms
a suite of benchmark and state of-the-art SR algorithms
in problems with unknown constants added, which we
claim are more reflective of SR performance for
real-world applications. Our claim is then strengthened
by reproducing the superior performance on real-world
regression datasets from SRBench. This Hot-of-the-Press
paper summarizes the work K.S. Fong, S. Wongso and M.
Motani, {"}Rethinking Symbolic Regression: Morphology
and Adaptability in the Context of Evolutionary
Algorithms{"}, The Eleventh International Conference on
Learning International Conference on Learning
Representations (ICLR'23).",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Kei Sen Fong
Shelvia Wongso
Mehul Motani
Citations