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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
T. Inamura. Brittle/ductile phenomena observed in computer simulations of machining defect-free monocrystalline silicon. Annals of the CIRP, 46:31–34, 1997.
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.
J. R. Koza. Genetic Programming:On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, 1992.
H.-P. Schwefel. Evolution and Optimum Seeking. Wiley-Interscience, USA, 1995.
S. Shimada, N. Ikawa, H. Tanaka, and J. Uchikoshi. Structure of Micromachined Surface Simulated by Molecular Dynamics Analysis. Annals of the CIRP, 1994.
G. Warnecke. Spanbildung bei metallischen Werkstoffen. Technischer Verlag Resch, Munich, 1974.
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.
K. Weinert, T. Surmann, and J. Mehnen. Evolutionary surface reconstruction using CSG-NURBS-hybrids. Reihe CI 114/01, SFB 531, University of Dortmund, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/3-540-45984-7_11
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43378-1
Online ISBN: 978-3-540-45984-2
eBook Packages: Springer Book Archive