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Accelerated Genetic Programming

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Book cover Recent Advances in Soft Computing (MENDEL 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 837))

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Abstract

Symbolic regression by the genetic programming is one of the options for obtaining a mathematical model for known data of output dependencies on inputs. Compared to neural networks (MLP), they can find a model in the form of a relatively simple mathematical relationship. The disadvantage is their computational difficulty. The following text describes several algorithm adjustments to enable acceleration and wider usage of the genetic programming. The performance of the resulting program was verified by several test functions containing several percent of the noise. The results are presented in graphs. The application is available at www.zpp.wz.cz/g.

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Notes

  1. 1.

    [9] uses term “cost function”, when looking for minimum

References

  1. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992). ISBN 978-0262111706

    MATH  Google Scholar 

  2. Banzhaf, W., et al.: Genetic Programming, an Introduction. Morgan Kaufmann Publishers, Inc., San Francisco (1998). ISBN 978-1-55860-510-7

    Book  Google Scholar 

  3. Barmpalexisa, P., Kachrimanisa, K., Tsakonasb, A., Georgarakis, E.: Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation. In: Chemo metrics and Intelligent Laboratory Systems, vol. 107, no. 1, pp. 75–82. Elsevier, May 2011

    Google Scholar 

  4. Zelinka, I.: Analytic programming by means of soma algorithm. In: Proceedings of 8th International Conference on Soft Computing Mendel 2002, Brno, Czech Republic, pp. 93–101 (2002). ISBN 80-214-2135-5

    Google Scholar 

  5. Weisser R., Ošmera, P., Matoušek, R.: Transplant evolution with modified schema of differential evolution: optimization structure of controllers. In: International Conference on Soft Computing MENDEL, Brno (2010)

    Google Scholar 

  6. Brandejsky, T.: Genetic programming algorithm with constants pre-optimization of modified candidates of new population. In: Mendel 2004, Brno, pp. 34–38 (2004)

    Google Scholar 

  7. Brandejsky, T.: Small populations in GPA-ES algorithm. In: Mendel 2013, pp. 31–36. ISBN 978-802144755-4

    Google Scholar 

  8. Brandejsky, T.: Influence of random number generators on GPA-ES algorithm efficiency. In: Advances in Intelligent Systems and Computing, vol. 576, pp. 26–33. Springer International Publishing AG (2017)

    Google Scholar 

  9. Roupec, J.: Advanced genetic algorithms for engineering design problems. Eng. Mech. 17(5–6), 407–417 (2011)

    Google Scholar 

  10. Kivinen, J., Warmuth, M.K.: Exponentiated gradient versus gradient descent for linear predictors. Inf. Comput. 132(1), 1–63 (1997). https://doi.org/10.1006/inco.1996.2612

    Article  MathSciNet  MATH  Google Scholar 

  11. Azad, R.M.A., Ryan, C.: A simple approach to lifetime learning in genetic programming-based symbolic regression. Evol. Comput. 22(2), 287–317 (2014)

    Article  Google Scholar 

  12. Castelli, M., et al.: The influence of population size in geometric semantic GP. In: Swarm and Evolutionary Computation, vol. 32, pp. 110–120. Elsevier BV (2017). ISSN 2210-6502

    Google Scholar 

  13. Affenzeller, M., et al.: Dynamic observation of genotypic and phenotypic diversity for different symbolic regression gp variants. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017, pp. 1553–1558. ACM, New York (2017)

    Google Scholar 

  14. Ošmera, P., Roupec, J.: Limited lifetime genetic algorithms in comparison with sexual reproduction based GAs. In: Proceedings of MENDEL 2000, Brno, Czech Republic, pp. 118–126 (2000)

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Mechanical Engineering, Czech Technical University in Prague, Technická 4, 166 07, Prague 6, Czech Republic

    Vladimír Hlaváč

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  1. Vladimír Hlaváč
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Correspondence to Vladimír Hlaváč .

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Editors and Affiliations

  1. Department of Applied Computer Science, Institute of Automation and Computer Science, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Hlaváč, V. (2019). Accelerated Genetic Programming. In: Matoušek, R. (eds) Recent Advances in Soft Computing . MENDEL 2017. Advances in Intelligent Systems and Computing, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-319-97888-8_9

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