Genetic Programming for Instance Transfer Learning in Symbolic Regression
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- @Article{Chen:2020:CYB,
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author = "Qi Chen and Bing Xue and Mengjie Zhang",
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journal = "IEEE Transactions on Cybernetics",
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title = "Genetic Programming for Instance Transfer Learning in
Symbolic Regression",
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year = "2022",
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volume = "52",
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number = "1",
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pages = "25--38",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Task
analysis, Estimation, Multitasking, Machine learning,
Data models, Cybernetics, instance weighting, transfer
learning",
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ISSN = "2168-2275",
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DOI = "doi:10.1109/TCYB.2020.2969689",
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abstract = "Transfer learning has attracted more attention in the
machine-learning community recently. It aims to improve
the learning performance on the domain of interest with
the help of the knowledge acquired from a similar
domain(s). However, there is only a limited number of
research on tackling transfer learning in genetic
programming for symbolic regression. This article
attempts to fill this gap by proposing a new instance
weighting framework for transfer learning in genetic
programming-based symbolic regression. In the new
framework, differential evolution is employed to search
for optimal weights for source-domain instances, which
helps genetic programming to identify more useful
source-domain instances and learn from them. Meanwhile,
a density estimation method is used to provide good
starting points to help the search for the optimal
weights while discarding some irrelevant or less
important source-domain instances before learning
regression models. The experimental results show that
compared with genetic programming and support vector
regression that learn only from the target instances,
and learning from a mixture of instances from the
source and target domains without any transfer learning
component, the proposed method can evolve regression
models which not only achieve notably better
cross-domain generalization performance in stability
but also reduce the trend of overfitting effectively.
Meanwhile, these models are generally much simpler than
those generated by the other GP methods.",
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notes = "Also known as \cite{9007621}",
- }
Genetic Programming entries for
Qi Chen
Bing Xue
Mengjie Zhang
Citations