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Advances in Computational Intelligence Systems pp 481–494Cite as

Multi Objective Symbolic Regression

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 513))

Abstract

Symbolic regression has been a popular technique for some time. Systems typically evolve using a single objective fitness function, or where the fitness function is multi-objective the factors are combined using a weighted sum. This work uses a Non Dominated Sorting Strategy to rank the genomes. Using data derived from Swimming turns performed by elite athletes more information and better expressions can be generated than by using single, or even double objective functions. Symbolic regression, multi-objective, non dominated sorting, genetic programming.

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Author information

Authors and Affiliations

  1. Loughborough University, Loughborough, UK

    C. J. Hinde, N. Chakravorti & A. A. West

Authors
  1. C. J. Hinde
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  2. N. Chakravorti
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  3. A. A. West
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Corresponding author

Correspondence to C. J. Hinde .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg School of Computing and Communications, Lancaster, United Kingdom

    Plamen Angelov

  2. School of Computing, University of Portsmouth School of Computing, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  3. School of Comp. Sci. & Digital Media, Robert Gordon University School of Comp. Sci. & Digital Media, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth University Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth, United Kingdom

    Qiang Shen

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Hinde, C.J., Chakravorti, N., West, A.A. (2017). Multi Objective Symbolic Regression. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_31

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  • DOI: https://doi.org/10.1007/978-3-319-46562-3_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46561-6

  • Online ISBN: 978-3-319-46562-3

  • eBook Packages: EngineeringEngineering (R0)

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