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Automated Discovery of Symbolic Approximation Formulae using Genetic Programming

by Mohamed M. Khatib
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 13
Year of Publication: 2020
Authors: Mohamed M. Khatib
10.5120/ijca2020920053

Mohamed M. Khatib . Automated Discovery of Symbolic Approximation Formulae using Genetic Programming. International Journal of Computer Applications. 176, 13 ( Apr 2020), 29-34. DOI=10.5120/ijca2020920053

@article{ 10.5120/ijca2020920053,
author = { Mohamed M. Khatib },
title = { Automated Discovery of Symbolic Approximation Formulae using Genetic Programming },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2020 },
volume = { 176 },
number = { 13 },
month = { Apr },
year = { 2020 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number13/31262-2020920053/ },
doi = { 10.5120/ijca2020920053 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:42:25.646998+05:30
%A Mohamed M. Khatib
%T Automated Discovery of Symbolic Approximation Formulae using Genetic Programming
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 13
%P 29-34
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes the use of genetic programming to automate the discovery of symbolic approximation formulae. Results are presented involving discovery of numeric approximation formulae to common functions, which are compared to Padé approximations obtained through a symbolic mathematics package. Based on these results, we consider genetic programming to be a powerful and effective technique for the automated discovery of symbolic approximation formulae.

References
  1. Andre, D. & Koza, J. R. (1996) Parallel Genetic Programming: A scalable implementation using the transputer network architecture. In P. J. Angeline and K. E. Kinnear, Jr. (eds.), Advances in Genetic Programming 2, 317-338. Cambridge, MA: MIT Press.
  2. Andre, D. & Koza, J. R. (1996) Parallel Genetic Programming: A scalable implementation using the transputer network architecture. In P. J. Angeline and K. E. Kinnear, Jr. (eds.), Advances in Genetic Programming 2, 317-338. Cambridge, MA: MIT Press.
  3. Baker, G. A (1975) Essentials of Padé Approximants. New York: Academic Press.
  4. Blickle, T. & Thiele, L. (1995) A Comparison of Selection Schemes Used in Genetic Algorithms. TIK-Report 11, TIK Institut fur Technische Informatik und Kommunikationsnetze, Computer Engineering and Networks Laboratory, ETH, Swiss Federal Institute of Technology.
  5. Chellapilla, K. (1997) Evolving Computer Programs without Subtree Crossover. IEEE Transactions on Evolutionary Computation 1(3):209-216.
  6. Faires, D. & Burden, R. (1998) Numerical Methods, Brooks/Cole Publishing Company, USA.
  7. Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan: The University of Michigan Press.
  8. Iba, H., Kurita, T., de Garis, H., & Sato, T. (2019) System identification using structured genetic algorithms. In Stefanie Forrest, editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 279-286, San Mateo, CA, Morgan Kaufmann Publishers.
  9. Keane, M. A., Koza, J. R. & Rice, J. P. (1993) Finding an impulse response function using genetic programming. In Proceedings of the 1993 American Control Conference, 3:2345-2350.
  10. Koza, J. R. (1990a) Genetic Programming: A paradigm for genetically breeding populations of computer programs to solve problems. Stanford University Computer Science Department technical report STAN-CS-90-1314.
  11. Koza, J. R. (1990b). A Genetic Approach to Econometric Modeling. Presented at Sixth World Congress of the Econometric Society, Barcelona, Spain.
  12. Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.
  13. Koza, J.R., Bennett III, F.H. Andre, D. and Keane, M.A. (1999), “Genetic Programming III: Darwinian Invention and Problem Solving”, Morgan Kaufmann.
  14. Luke, S. & Spector, L. (1997) A Comparison of Crossover and Mutation in Genetic Programming. In J. R. Koza, K. Deb, M. Dorigo, D. B. Fogel, M. Garzon, H. Iba, and R. L. Riolo (eds.), Genetic Programming 1997: Proceedings of the Second Annual Conference, 240-248. San Mateo, CA: Morgan Kaufmann.
  15. Nordin, P. (1997) Evolutionary Program Induction of Binary Machine Code and its Applications. PhD thesis, der Universitat Dortmund am Fachereich Informatik.
  16. Ryan, C., Collins, J. & O'Neill, M. (2018). Grammatical Evolution: evolving programs for an arbitrary language. In W. Banzhaf, R. Poli, M. Schoenauer, and T. C. Fogarty (eds.), Proceedings of the First European Workshop on Genetic Programming, 1391:83-95. New York: Springer-Verlag.
  17. Thomas, B. (1996) Evolutionary Algorithms in Theory and Practice. Oxford University Press, Inc.
  18. Wolfgang, B., Peter, N., Robert, E. K., & Frank, D. F. (1998) Genetic Programming- an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers, San Francisco.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Programming Padé approximations Symbolic Regression

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