Active Learning Informs Symbolic Regression Model Development in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{haut:2023:GECCOcomp,
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author = "Nathan Haut and Bill Punch and Wolfgang Banzhaf",
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title = "Active Learning Informs Symbolic Regression Model
Development in Genetic Programming",
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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 = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "587--590",
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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, active
learning, symbolic regression: Poster",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583133.3590577",
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size = "4 pages",
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abstract = "Active learning for genetic programming using model
ensemble uncertainty was explored across a range of
uncertainty metrics to determine if active learning can
be used with GP to minimize training set sizes by
selecting maximally informative samples to guide
evolution. The choice of uncertainty metric was found
to have a significant impact on the success of active
learning to inform model development in genetic
programming. Differential evolution was found to be an
effective optimizer, likely due to the non-convex
nature of the uncertainty space, while differential
entropy was found to be an effective uncertainty
metric. Uncertainty-based active learning was compared
to two random sampling methods and the results show
that active learning successfully identified
informative samples and can be used with GP to reduce
required training set sizes to arrive at a solution.",
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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
Nathaniel Haut
William F Punch
Wolfgang Banzhaf
Citations