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International Conference on Evolutionary Programming

EP 1998: Evolutionary Programming VII pp 251–260Cite as

Numeric mutation as an improvement to symbolic regression in genetic programming

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

Abstract

A weakness of genetic programming (GP) is the difficulty it suffers in discovering useful numeric constants for the terminal nodes of the s-expression trees. We examine a solution to this problem, called numeric mutation, based, roughly, on simulated annealing. We provide empirical evidence to demonstrate that this method provides a statistically significant improvement in GP system performance for symbolic regression problems. GP runs are more likely to find a solution, and successful runs use fewer generations.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Florida Atlantic University, 33431, Boca Raton, Florida

    Thomas Fernandez & Matthew Evett

Authors
  1. Thomas Fernandez
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  2. Matthew Evett
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Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Fernandez, T., Evett, M. (1998). Numeric mutation as an improvement to symbolic regression in genetic programming. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040778

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  • DOI: https://doi.org/10.1007/BFb0040778

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

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