Skip to main content
SpringerLink
Log in

Symbolic Regression Problems by Genetic Programming with Multi-branches

  • Conference paper
Book cover MICAI 2004: Advances in Artificial Intelligence (MICAI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2972))

Included in the following conference series:

Abstract

This work has the aim of exploring the area of symbolic regression problems by means of Genetic Programming. It is known that symbolic regression is a widely used method for mathematical function approximation. Previous works based on Genetic Programming have already dealt with this problem, but considering Koza’s GP approach. This paper introduces a novel GP encoding based on multi-branches. In order to show the use of the proposed multi-branches representation, a set of testing equations has been selected. Results presented in this paper show the advantages of using this novel multi-branches version of GP.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Angeline, P.J.: Parse trees. In: Back, T., Fogel, D.B., Michalewickz, T. (eds.) Evolutionary Computation 1, basic algorithms and operators (1996)

    Google Scholar 

  2. Angeline, P.J., Pollack, J.B.: Co – evolving high level representation. In: Langton, C.G. (ed.) Artificial life III, pp. 55–71. Addison Wesley, Reading (1994)

    Google Scholar 

  3. Cramer, N.L.: A representation for the adaptative generation of simple sequencial programs. In: Grefenstette, J.J. (ed.) Proc. 1st. Int. Conference on Genetic Algorithms, Pittsburg, PA, Hillsdale, NJ, pp 183 –187 (July 1985)

    Google Scholar 

  4. Harries, K., Smith, P.W.H.: Code Growth, Explicitly Defined Introns and Alternative Selection Schemes. Evolutionary Computation 6(4), 346–364 (1998)

    Google Scholar 

  5. Hinchiffe, M., Hiden, H., McKay, B., Willis, M., Tham, M., Barton, G.: Modelling Chemical Process System using multi-gene Programming Algorithm. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1996 Conference, Stanford University, C.A, pp. 56–65. Stanford Bookstore, Stanford (1996)

    Google Scholar 

  6. Ivakhnenko, A.G.: Polynomial theory of complex systems IEEE Transactions on Systems, Man, and Cybernetics, 364–378 (1971)

    Google Scholar 

  7. Keijzer, M.: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Keijzer, M., Babovic, V.: Genetic Programming, Ensemble Methods and the Bias/Variance Tradeoff – introductory Investigations. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 76–90. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  9. Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  10. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  11. Langdon, W.B.: Size Fair and Homologous Tree Genetic Programming Crossovers. In: Morgan Kaufmannm, Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 1092–1097 (1999)

    Google Scholar 

  12. Langdon, W.B., Poli, R.: Fitness Causes Bloat. In: Chawdhry, P.K., Roy, R., Pan, R.K. (eds.) Second On-line World Conference on Soft Computing in Engineering Design and Manufacturing, pp. 13–22. Springer, London (1997)

    Google Scholar 

  13. Nordin, P., Francone, F., Banzhaf, W.: Explicitly defined introns and destructive crossover in genetic programming. In: Angeline, P.J., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming 2, ch. 6, pp. 111–134. MIT Press, Cambridge (1996)

    Google Scholar 

  14. Nordin, J.P., Banzhaf, W.: Complexity Compression and Evolution. In: Eshelman, L. (ed.) Proceedings of Sixth International Conference of Genetic Algorithms, Pittsburgh, Morgan Kaufmann, San Mateo (1995)

    Google Scholar 

  15. Nikolaev, I.N., Iba, H.: Accelerated Genetic Programming of Polynomials. Genetic Programming and Evolvable Machines 2(3), 231–258 (2001)

    Article  MATH  Google Scholar 

  16. Oliver, M.C.: Programación Genética Multi - Ramas en el Modelado y Predicción de Datos Climatológicos. Tesis de Maestría. Universidad Autonoma de México, D.F., México, spanish (2002)

    Google Scholar 

  17. Oliver, M.C., y Rodríguez, K.V.: Estructuta de Arbol vs Estructura Polinomial con Programación Genética en el Modelado de Variables Climatológicas. en el 1er Congreso Español de Algoritmos Geneticos y Bioinspirados. Del 6 al 8 de Febrero de, Merida, España (2002)

    Google Scholar 

  18. Poli, R.: Genetic programming for image analysis. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Proc. Genetic Programming 1996, pp. 363–368. MIT Press, Cambridge (1996)

    Google Scholar 

  19. Rodríguez, V.K., Oliver, M.C.: Divide and Conquer: Genetic Programming Based on Multiple Branches Encoding. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E.P.K., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, Springer, Heidelberg (2003)

    Google Scholar 

  20. Rosca, J.P., Ballard, D.H.: Discovery of subroutines in genetic programming. In: Angeline, P.J., Kinnear, K. (eds.) Advances in Genetic Programming, vol. 2, pp. 177–202. MIT press, Cambridge (1996)

    Google Scholar 

  21. Salustowicks, R.P., Schmidhuber, J.: Probabilistc Incremental Program Evolution. Evolutionary Computation 5(2), 123–141 (1997)

    Article  Google Scholar 

  22. Streeter, M., Becker, L.A.: Automated Discovery of Numerical Aproximation Formulae via Genetic Programming. In: Spector, L., Goodman, E.D., Wu, A., Langdom, W.B., Voigth, H.M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Conference (GECCO 2001), San Francisco CA, July 7-11, pp. 147–154. Morgam Kaufmann, San Francisco (2001)

    Google Scholar 

  23. Soule, T., Foster, J.A., Dickinson, J.: Code growth in genetic programming. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, July 28-31, pp. 215–223. MIT Press, Cambridge (1996)

    Google Scholar 

  24. Topchy, A., Punch, W.F.: Faster Genetic Based on Local Gradient Search of Numeric Leaf Values. In: Spector, L., Goodman, E.D., Wu, A., Langdom, W.B., Voigth, H.M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Conference (GECCO 2001), San Francisco CA, July 7-11, pp. 155–162. Morgam Kaufmann, San Francisco (2001)

    Google Scholar 

  25. Sims, K.: Interactive Evolution of Equations for Procedural Models. The Visual Computer 9, 466–476 (1993)

    Article  Google Scholar 

  26. Zhang, B.-T., Muhlenbein, H.: Balancing Accuracy and Parsimony in Genetic Programming. Evolutionary Computation 3(1), 17–38

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DISCA, IIMAS, National Autonomous University of Mexico, Circuito Escolar Ciudad Universitaria, Mexico City, 04510, Mexico

    Carlos Oliver Morales & Katya Rodríguez Vázquez

Authors
  1. Carlos Oliver Morales
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katya Rodríguez Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Department, Tecnológico de Monterrey, Campus Estado de México, Carretera al lago de Guadalupe, Km 3.5, Atizapán, 52926, Mexico

    Raúl Monroy

  2. Instituto de Investigaciones Electricas, Reforma # 113, Col. Palmira, 62490, Morelos, Cuernavaca, Mexico

    Gustavo Arroyo-Figueroa

  3. Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro No. 1, 72840, Puebla, México

    Luis Enrique Sucar

  4. Centro de Investigación en Computación – IPN, Av. Juan de Dios Batíz, esquina con Miguel Othón de Mendizábal, Ciudad de México, 07738, México

    Humberto Sossa

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Oliver Morales, C., Rodríguez Vázquez, K. (2004). Symbolic Regression Problems by Genetic Programming with Multi-branches. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_74

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24694-7_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21459-5

  • Online ISBN: 978-3-540-24694-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation