Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
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- @Article{Haut:ieeeTEC,
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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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journal = "IEEE Transactions on Evolutionary Computation",
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note = "Early Access",
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keywords = "genetic algorithms, genetic programming, Training,
Data models, Uncertainty, Mathematical models,
Measurement, Machine learning, Labeling, Genetic
programming, Training data, Computational modeling,
Active learning, symbolic regression",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2024.3471341",
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size = "13 pages",
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abstract = "This paper 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 as a 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 optimization approach to allow
both to be considered in a balanced way to guide the
selection of informative and unique data points for
training.",
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notes = "also known as \cite{10700803} See also
\cite{DBLP:journals/corr/abs-2308-00672}",
- }
Genetic Programming entries for
Nathaniel Haut
Wolfgang Banzhaf
William F Punch
Citations