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Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems

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Genetic Programming (EuroGP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2038))

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Abstract

In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three gp variants.

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References

  1. Dimitris Achlioptas, Lefteris M. Kirousis, Evangelos Kranakis, Danny Krizanc, Michael S.O. Molloy, and Yannis C. Stamatiou. Random constraint satisfaction a more accurate picture. In Gert Smolka, editor, Principles and Practice of Constraint Programming-CP97, pages 107–120. Springer-Verlag, 1997.

    Google Scholar 

  2. With S. Ar, R. Lipton, and R. Rubinfeld. Reconstructing algebraic functions from erroneous data. SIAM Journal on Computing, 28(2):487–510, 1999.

    Article  MathSciNet  Google Scholar 

  3. A.K. Cline. Six subprograms for curve fitting using splines under tension. Commun. ACM, 17(4):220–223, April 1974.

    Article  MathSciNet  Google Scholar 

  4. J. Eggermont, A.E. Eiben, and J.I. van Hemert. Adapting the fitness function in GP for data mining. In R. Poli, P. Nordin, W.B. Langdon, and T.C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP’99, volume 1598 of LNCS, pages 195–204, Goteborg, Sweden, 26-27 May 1999. Springer-Verlag.

    Chapter  Google Scholar 

  5. A.E. Eiben, J.K. van der Hauw, and J.I. van Hemert. Graph coloring with adaptive evolutionary algorithms. Journal of Heuristics, 4(1):25–46, 1998.

    Article  MATH  Google Scholar 

  6. A.E. Eiben and J.I. van Hemert. SAW-ing EAs: adapting the fitness function for solving constrained problems, chapter 26, pages 389–402. McGraw-Hill, London, 1999.

    Google Scholar 

  7. C. Gathercole and P. Ross. Dynamic training subset selection for supervised learning in genetic programming. In Proceedings of the Parallel Problem Solving from Nature III Conference, pages 312–321, 1994.

    Google Scholar 

  8. Chris Gathercole and Peter Ross. Tackling the boolean even N parity problem with genetic programming and limited-error fitness. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 119–127, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann.

    Google Scholar 

  9. J.R. Koza. Genetic Programming. MIT Press, 1992.

    Google Scholar 

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

    MATH  Google Scholar 

  11. J.J. Merelo, J. Carpio, P. Castillo, V.M. Rivas, and G. Romero. Evolving objects, 1999. Available at http://geneura.ugr.es/~jmerelo/EOpaper/.

  12. Z. Michalewicz, K. Deb, M. Schmidt,, and T. Stidsen. Test-case generator for nonlinear continuous parameter optimization techniques. IEEE Transactions on Evolutionary Computation, 4(3), 2000.

    Google Scholar 

  13. David S. Moore and George P. McCabe. Introduction to the Practice of Statistics. W.H. Freeman and Company, New York, 3rd edition, 1998.

    Google Scholar 

  14. J. Paredis. Co-evolutionary computation. Artificial Life, 2(4):355–375, 1995.

    Article  Google Scholar 

  15. Byoung-Tak Zhang. Bayesian methods for efficient genetic programming. Genetic Programming And Evolvable Machines, 1(3):217–242, July 2000.

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Leiden University, Leiden

    Jeroen Eggermont & Jano I. van Hemert

Authors
  1. Jeroen Eggermont
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  2. Jano I. van Hemert
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Editor information

Editors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham

    Julian Miller

  2. Computer Science Institute, University of Lausanne, Lausanne

    Marco Tomassini

  3. Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy

    Pier Luca Lanzi

  4. Computer Science and Information Systems, University of Limerick, Limerick

    Conor Ryan

  5. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  6. Department of Computer Science, University College London, London

    William B. Langdon

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© 2001 Springer-Verlag Berlin Heidelberg

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Eggermont, J., van Hemert, J.I. (2001). Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds) Genetic Programming. EuroGP 2001. Lecture Notes in Computer Science, vol 2038. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45355-5_3

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  • DOI: https://doi.org/10.1007/3-540-45355-5_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41899-3

  • Online ISBN: 978-3-540-45355-0

  • eBook Packages: Springer Book Archive

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