GENETIC PROGRAMMING WITH EMBEDDED FEATURES OF SYMBOLIC COMPUTATIONS

Yaroslav V. Borcheninov, Yuri S. Okulovsky

Abstract

Genetic programming is a methodology, widely used in data mining for obtaining the analytic form that describes a given experimental data set. In some cases, genetic programming is complemented by symbolic computations that simplify found expressions. We propose to unify the induction of genetic programming with the deduction of symbolic computations in one genetic algorithm. Our approach was implemented as the .NET library and successfully tested at various data mining problems: function approximation, invariants finding and classification.

References

  1. Drayton, P., Albahari, B., and Neward, T. (2002). C# in a Nutshell. O'Reilly.
  2. Goldberg, D. (1986). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
  3. Kinzett, D., Johnston, M., and Zhang, M. (2008). Numerical simplification for bloat control and analysis of building blocks in genetic programming. Evolutionary Intelligence, 4.
  4. Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA.
  5. Koza, J. R. (1994). Genetic programming for economic modeling. In Intelligent Systems for Finance and Business.
  6. Mori, N., McKay, B., Hoai, N. X., Essam, D., and Takeuchi, S. (2009). A new method for simplifying algebraic expressions in genetic programming called equivalent decision simplification. Journal of Advanced Computational Intelligence and Intelligent Informatics, 13(14):237-238.
  7. Poli, R., Langdon, W. B., McPhee, N. F., and Koza, J. R. (2008). A Field Guide to Genetic Programming.
  8. Robertson, A. P. and Dumont, C. (2002). Design of robot calibration models using genetic programming. In Mayorga, R. V. and Rios, A. S.-D. L., editors, Proceedings of the Third International Symposium on Rob. and Autom., volume 3, pages 449-454.
  9. Schmidt, M. and Lipson, H. (2009). Distilling freeform natural laws from experimental data. Science, 324(5923):81-85.
  10. Zhang, M. and Wong, P. (2008). Genetic programming for medical classification: a program simplification approach. Genetic Programming and Evolvable Machines, 9(2):229-255.
  11. Zhang, M., Wong, P., and Qian, D. (2006). Online program simplification in genetic programming. Simulated Evolution and Learning - SEAL, pages 592-600.
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Paper Citation


in Harvard Style

V. Borcheninov Y. and S. Okulovsky Y. (2011). GENETIC PROGRAMMING WITH EMBEDDED FEATURES OF SYMBOLIC COMPUTATIONS . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011) ISBN 978-989-8425-79-9, pages 468-471. DOI: 10.5220/0003682004760479


in Bibtex Style

@conference{kdir11,
author={Yaroslav V. Borcheninov and Yuri S. Okulovsky},
title={GENETIC PROGRAMMING WITH EMBEDDED FEATURES OF SYMBOLIC COMPUTATIONS},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)},
year={2011},
pages={468-471},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003682004760479},
isbn={978-989-8425-79-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2011)
TI - GENETIC PROGRAMMING WITH EMBEDDED FEATURES OF SYMBOLIC COMPUTATIONS
SN - 978-989-8425-79-9
AU - V. Borcheninov Y.
AU - S. Okulovsky Y.
PY - 2011
SP - 468
EP - 471
DO - 10.5220/0003682004760479