Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
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- @Misc{DBLP:journals/corr/abs-2308-00672,
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author = "Nathan Haut and Wolfgang Banzhaf and Bill Punch",
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title = "Active Learning in Genetic Programming: Guiding
Efficient Data Collection for Symbolic Regression",
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howpublished = "arXiv",
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volume = "abs/2308.00672",
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year = "2023",
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month = "31 " # jul,
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keywords = "genetic algorithms, genetic programming",
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URL = "https://doi.org/10.48550/arXiv.2308.00672",
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DOI = "doi:10.48550/ARXIV.2308.00672",
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eprinttype = "arXiv",
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eprint = "2308.00672",
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timestamp = "Mon, 21 Aug 2023 01:00:00 +0200",
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biburl = "https://dblp.org/rec/journals/corr/abs-2308-00672.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "26 pages",
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abstract = "we examines various methods of computing uncertainty
and diversity for active learning in genetic
programming. We found that the model population in
genetic programming can be exploited to select
informative training data points by using a model
ensemble combined with an uncertainty metric. We
explored several uncertainty metrics and found that
differential entropy performed the best. We also
compared two data diversity metrics and found that
correlation asa diversity metric performs better than
minimum Euclidean distance, although there are some
drawbacks that prevent correlation from being used on
all problems. Finally, we combined uncertainty and
diversity using a Pareto optimisation approach to allow
both to be considered in a balanced way to guide the
selection of informative and unique data points for
training.",
- }
Genetic Programming entries for
Nathaniel Haut
Wolfgang Banzhaf
William F Punch
Citations