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
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[9] uses term “cost function”, when looking for minimum
References
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992). ISBN 978-0262111706
Banzhaf, W., et al.: Genetic Programming, an Introduction. Morgan Kaufmann Publishers, Inc., San Francisco (1998). ISBN 978-1-55860-510-7
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
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
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)
Brandejsky, T.: Genetic programming algorithm with constants pre-optimization of modified candidates of new population. In: Mendel 2004, Brno, pp. 34–38 (2004)
Brandejsky, T.: Small populations in GPA-ES algorithm. In: Mendel 2013, pp. 31–36. ISBN 978-802144755-4
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)
Roupec, J.: Advanced genetic algorithms for engineering design problems. Eng. Mech. 17(5–6), 407–417 (2011)
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
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)
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
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)
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)
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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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DOI: https://doi.org/10.1007/978-3-319-97888-8_9
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