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Divide and Conquer: Genetic Programming Based on Multiple Branches Encoding

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Genetic Programming (EuroGP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2610))

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Abstract

This paper describes an alternative genetic programming encoding, which is based on a rooted-node with fixed-content. This rooted node combines partial results of a set of multiple branches. Hence, this approach is named Multiple Branches Genetic Programming. It is tested on a symbolic regression problem and used on a Boolean domain to solve the even-n parity problem.

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References

  1. Haber, R. AND H. Unbenhauen (1990) Structure Identification of Non-Linear Dynamic Systems: A Survey of Input/Output Approaches. Automatica, 26 (4), pp. 651–677.

    Article  MATH  MathSciNet  Google Scholar 

  2. Iba, H., H. DE Garis AND T. Sato. (1994) Genetic Programming Using a Minimum Description Length. In Advances in Genetic Programming (Kinnear, Editor) pp. 265–284. MIT Press.

    Google Scholar 

  3. M.A. Kaboudan (2001) Genetically Evolved Models and Normality of Their Fitted Residuals, Journal of Economic Dynamics & Control, 25, 1719–1749.

    Article  MATH  Google Scholar 

  4. Kang, Z., Y.Li, H. DE Garis AND L-S. Kang (2002) A Two Levels Evolutionary Modeling System for Financial Data. In Proc. of the GECCO, (W.B. Langdon et al., editors), pp. 1113–1118. New York City, New York, Morgan Kaufmann.

    Google Scholar 

  5. J.R. Koza (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press.

    Google Scholar 

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

    Google Scholar 

  7. Langdon, W.B. (1998) Genetic Programming and Data Structures. Kluwer Academic Publishers.

    Google Scholar 

  8. N.I. Nikolaev AND H. Iba (2001) Accelerated Genetic Programming of Polinomials, Genetic Programming and Evolvable Machines 2(3), pp. 23–1–275.

    Article  Google Scholar 

  9. Nordin, P., F. Francone AND W. Bhanzaf (1996) Explicitly Defined Introns and Destructive Crossover in Genetic Programming. In: (Angeline, P.J. and Kinnear, K.E., editors) Advances in Genetic Programming 2, chapte 6, pp. 111–134. MIT Press.

    Google Scholar 

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

    Google Scholar 

  11. Oliver-Morales. C. AND K. Rodríguez-Vázquez (2002) MBGP in Modelling and Prediction, Proc. of the GECCO (E.B. Langdon, et al., editors), pp. 892. New York City, New York, Morgan Kauf

    Google Scholar 

  12. Poli, R. AND W.B. Langdon (2001) Foundations of Genetic Programming. Springer-Verlag.

    Google Scholar 

  13. Rodríguez-Vázquez, K. AND P.J. Fleming (1998) Multiobjective Genetic Programming for Non-Linear System Identification. Ellectronics Letters, 34(9), pp. 930–931.

    Article  Google Scholar 

  14. Rodríguez-Vázquez, K. AND P.J. Fleming (1999) A Genetic Algorithm for Subset Selection in System Identification. Second Mexican International Conference on Computer Science ENC’99, México.

    Google Scholar 

  15. Soule, T. AND J.A Foster (1997) Code Size and Depth Flows in Genetic Programming. Proc. Second Annual Conf. on Genetic Programming, (J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba, R.L. Riolo, editors), pp. 313–320. Morgan Kaufmann.

    Google Scholar 

  16. Whigham, P.A. AND J. Keukelaar (2001) Evolving Structures-Optimising Contents. IEEE Proc. of Congress on Evolutionary Computation, pp. 1228–1235.

    Google Scholar 

  17. Zhang, B.T. AND H. Mühlenbein (1996) Adaptive Fitness Functions for Dynamic Growing/ Pruning. In Advances in Genetic Programming, Vol. 2. (Angeline and Kinnear, Editors), MIT Press.

    Google Scholar 

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Author information

Authors and Affiliations

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

    Katya Rodríguez-Vázquez & Carlos Oliver-Morales

Authors
  1. Katya Rodríguez-Vázquez
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  2. Carlos Oliver-Morales
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Editor information

Editors and Affiliations

  1. Computer Science and Information Systems, University of Limerick, Limerick, Ireland

    Conor Ryan

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

    Terence Soule

  3. Department of Computer Science, Free University of Amsterdam, 1081, HV Amsterdam, The Netherlands

    Maarten Keijzer

  4. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, UK

    Edward Tsang  & Riccardo Poli  & 

  5. Department of Computer Science, University of Coimbra, Polo II, Pinhal Marrocos, 3030-290, Coimbra, Portugal

    Ernesto Costa

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© 2003 Springer-Verlag Berlin Heidelberg

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Rodríguez-Vázquez, K., Oliver-Morales, C. (2003). Divide and Conquer: Genetic Programming Based on Multiple Branches Encoding. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds) Genetic Programming. EuroGP 2003. Lecture Notes in Computer Science, vol 2610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36599-0_20

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  • DOI: https://doi.org/10.1007/3-540-36599-0_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00971-9

  • Online ISBN: 978-3-540-36599-0

  • eBook Packages: Springer Book Archive

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