Explainable Artificial Intelligence by Genetic Programming: A Survey
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Mei:TEVC,
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author = "Yi Mei and Qi Chen and Andrew Lensen and Bing Xue and
Mengjie Zhang",
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title = "Explainable Artificial Intelligence by Genetic
Programming: A Survey",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2023",
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volume = "27",
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number = "3",
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pages = "621--641",
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month = jun,
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keywords = "genetic algorithms, genetic programming, XAI,
Explainable artificial intelligence, Machine learning,
Task analysis, Predictive models, Adaptation models,
Training, Measurement",
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ISSN = "1941-0026",
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DOI = "doi:10.1109/TEVC.2022.3225509",
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size = "21 pages",
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abstract = "Explainable artificial intelligence has received great
interest in the recent decade, due to its importance in
critical application domains such as self-driving cars,
law and healthcare. Genetic programming is a powerful
evolutionary algorithm for machine learning. Compared
with other standard machine learning models such as
neural networks, the models evolved by GP tend to be
more interpretable due to their model structure with
symbolic components. However, interpretability has not
been explicitly considered in genetic programming until
recently, following the surge in popularity of
explainable artificial intelligence. This paper
provides a comprehensive review of the studies on
genetic programming that can potentially improve the
model interpretability, both explicitly and implicitly,
as a byproduct. We group the existing studies related
to explainable artificial intelligence by genetic
programming into two categories. The first category
considers the intrinsic interpretability, aiming to
directly evolve more interpretable (and effective)
models by genetic programming. The second category
focuses on post-hoc interpretability, which uses
genetic programming to explain other black-box machine
learning models, or explain the models evolved by
genetic programming by simpler models such as linear
models. This comprehensive survey demonstrates the
strong potential of genetic programming for improving
the interpretability of machine learning models and
balancing the complex trade-off between model accuracy
and interpretability.",
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notes = "Also known as \cite{9965435}",
- }
Genetic Programming entries for
Yi Mei
Qi Chen
Andrew Lensen
Bing Xue
Mengjie Zhang
Citations