Structural Risk Minimisation-Driven Genetic Programming for Enhancing Generalisation in Symbolic Regression
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- @Article{Chen:ieeeTEVC,
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author = "Qi Chen and Mengjie Zhang and Bing Xue",
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journal = "IEEE Transactions on Evolutionary Computation",
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title = "Structural Risk Minimisation-Driven Genetic
Programming for Enhancing Generalisation in Symbolic
Regression",
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year = "2019",
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volume = "23",
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number = "4",
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pages = "703--717",
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month = aug,
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression, Generalisation, Structural Risk
Minimisation, Vapnik-Chervonenkis Dimension",
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DOI = "doi:10.1109/TEVC.2018.2881392",
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ISSN = "1089-778X",
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size = "15 pages",
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abstract = "Generalisation ability, which reflects the prediction
ability of a learnt model, is an important property in
genetic programming for symbolic regression. Structural
risk minimisation is a framework providing a reliable
estimation of the generalisation performance of
prediction models. Introducing the framework into
genetic programming has the potential to drive the
evolutionary process towards models with good
generalisation performance. However, this is tough due
to the difficulty in obtaining the Vapnik-Chervonenkis
dimension of nonlinear models. To address this
difficulty, this paper proposes a structural risk
minimisation-driven genetic programming approach, which
uses an experimental method (instead of theoretical
estimation) to measure the Vapnik-Chervonenkis
dimension of a mixture of linear and nonlinear
regression models for the first time. The experimental
method has been conducted using uniform and non-uniform
settings. The results show that our method has
impressive generalisation gains over standard genetic
programming and genetic programming with the 0.632
bootstrap, and that the proposed method using the
non-uniform setting has further improvement than its
counterpart using the uniform setting. Further analyses
reveal that the proposed method can evolve more compact
models, and that the behavioural difference between
these compact models and the target models is much
smaller than their counterparts evolved by the other
genetic programming methods.",
-
notes = "Also known as \cite{8536418}",
- }
Genetic Programming entries for
Qi Chen
Mengjie Zhang
Bing Xue
Citations