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Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression

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Genetic Programming (EuroGP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13223))

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Abstract

Genetic Programming (GP) based symbolic regression is prone to generating complex models which often overfit the training data and generalise poorly onto unseen data. To address this issue, many pieces of research have been devoted to controlling the model complexity of GP. One recent work aims to control model complexity using a new representation called Adaptive Weighted Splines. With its semi-structured characteristic, the Adaptive Weighted Splines representation can control the model complexity explicitly, which was demonstrated to be significantly better than its tree-based counterpart at generalising to unseen data. This work seeks to significantly extend the previous work by proposing a multi-objective GP algorithm with the Adaptive Weighted Splines representation, which utilises parsimony pressure to further control the model complexity, as well as improve the interpretability of the learnt models. Experimental results show that, compared with single-objective GP with the Adaptive Weighted Splines and multi-objective tree-based GP with parsimony pressure, the new multi-objective GP method generally obtains superior fronts and produces better generalising models. These models are also significantly smaller and more interpretable.

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Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Christian Raymond, Qi Chen, Bing Xue & Mengjie Zhang

Authors
  1. Christian Raymond
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  2. Qi Chen
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  3. Bing Xue
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  4. Mengjie Zhang
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Corresponding author

Correspondence to Qi Chen .

Editor information

Editors and Affiliations

  1. University of Trieste, Trieste, Italy

    Eric Medvet

  2. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  3. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

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Raymond, C., Chen, Q., Xue, B., Zhang, M. (2022). Multi-objective Genetic Programming with the Adaptive Weighted Splines Representation for Symbolic Regression. In: Medvet, E., Pappa, G., Xue, B. (eds) Genetic Programming. EuroGP 2022. Lecture Notes in Computer Science, vol 13223. Springer, Cham. https://doi.org/10.1007/978-3-031-02056-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-02056-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02055-1

  • Online ISBN: 978-3-031-02056-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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