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A New View on Symbolic Regression

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Book cover Genetic Programming (EuroGP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2278))

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Abstract

Symbolic regression is a widely used method to reconstruct mathematical correlations. This paper presents a new graphical representation of the individuals reconstructed in this process. This new three dimensional representation allows the user to recognize certain possibilities to improve his setup of the process parameters. Furthermore this new representation allows a wider usage of the generated three dimensional objects with nearly every CAD program for further use. To show the practical usage of this new representation it was used to reconstruct mathematical descriptions of the dynamics in a machining process namely in orthogonal cutting.

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References

  1. T. Inamura. Brittle/ductile phenomena observed in computer simulations of machining defect-free monocrystalline silicon. Annals of the CIRP, 46:31–34, 1997.

    Article  Google Scholar 

  2. R. E. Keller, J. Mehnen, W. Banzhaf, and K. Weinert. Surface reconstruction from 3D point data with a genetic programming / evolution strategy hybrid, volume 3 of Advances in Genetic Programming, chapter 2, pages 41–65. MIT Press, 1999.

    Google Scholar 

  3. J. R. Koza. Genetic Programming:On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, 1992.

    MATH  Google Scholar 

  4. H.-P. Schwefel. Evolution and Optimum Seeking. Wiley-Interscience, USA, 1995.

    Google Scholar 

  5. S. Shimada, N. Ikawa, H. Tanaka, and J. Uchikoshi. Structure of Micromachined Surface Simulated by Molecular Dynamics Analysis. Annals of the CIRP, 1994.

    Google Scholar 

  6. G. Warnecke. Spanbildung bei metallischen Werkstoffen. Technischer Verlag Resch, Munich, 1974.

    Google Scholar 

  7. K. Weinert and M. Stautner. Reconstruction of Particle Flow Mechanisms with Symbolic Regression via Genetic Programming. In Lee Spector et al., editor, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 1439–1443, San Francisco, USA, 7-11 July 2001. Morgan Kaufmann.

    Google Scholar 

  8. K. Weinert, T. Surmann, and J. Mehnen. Evolutionary surface reconstruction using CSG-NURBS-hybrids. Reihe CI 114/01, SFB 531, University of Dortmund, 2001.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of Machining Technology, University of Dortmund, Germany

    Klaus Weinert & Marc Stautner

Authors
  1. Klaus Weinert
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  2. Marc Stautner
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    James A. Foster

  2. INRIA Rocquencourt, B.P. 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton

  3. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, UK

    Julian Miller

  4. Department of Computer Science and Information Systems, University of Limerick, Schuman Building, Limerick, Ireland

    Conor Ryan

  5. Dip. Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Andrea Tettamanzi

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© 2002 Springer-Verlag Berlin Heidelberg

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Weinert, K., Stautner, M. (2002). A New View on Symbolic Regression. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds) Genetic Programming. EuroGP 2002. Lecture Notes in Computer Science, vol 2278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45984-7_11

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  • DOI: https://doi.org/10.1007/3-540-45984-7_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43378-1

  • Online ISBN: 978-3-540-45984-2

  • eBook Packages: Springer Book Archive

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