Skip to main content
SpringerLink
Log in

Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator

  • Conference paper
  • First Online:
Intelligent Systems (BRACIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13073))

Included in the following conference series:

  • 563 Accesses

Abstract

Bloat is a common issue regarding Genetic Programming (GP), specially noted in Symbolic Regression (SR) problems. Due to this, GP tends to generate a huge amount of ineffective code that could be avoided or removed. Code editing is one of many approaches to avoid bloat. The objective in this strategy is to mutate or remove subtrees which do not contribute to the final solution. Two known methods of redundant code removal, the Rule Based Simplification (RBS) and Equivalent Decision Simplification (EDS) are extended in a new operator presented in this paper, called Dynamic Operator with RBS and EDS (DORE). This operator gives the algebraic simplification table used by RBS the potential to learn from reductions performed by EDS. An initial benchmark highlighted how the RBS table can grow as much as 86% with DORE, and reducing the time spent on simplification by 16.83%. Experiments with the other three SR problems were performed showing a considerable improvement on fitness of the generated programs, besides a slight reduction in the population of the average tree size.

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 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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

References

  1. Castelli, M., Manzoni, L., Mariot, L., Saletta, M.: Extending local search in geometric semantic genetic programming, pp. 775–787, August 2019. https://doi.org/10.1007/978-3-030-30241-2_64

  2. Chen, C., Luo, C., Jiang, Z.: Block building programming for symbolic regression. Neurocomputing 275, 1973–1980 (2018). https://doi.org/10.1016/j.neucom.2017.10.047

    Article  Google Scholar 

  3. Fay, M.P., Proschan, M.A.: Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat. Surv. 4, 1 (2010)

    Article  MathSciNet  Google Scholar 

  4. Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)

    MathSciNet  Google Scholar 

  5. Haeri, M.A., Ebadzadeh, M.M., Folino, G.: Statistical genetic programming for symbolic regression. Appl. Soft Comput. 60, 447–469 (2017)

    Article  Google Scholar 

  6. Hagiwara, M.: Pseudo-hill climbing genetic algorithm (PHGA) for function optimization. In: Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), vol. 1, pp. 713–716 (1993). https://doi.org/10.1109/IJCNN.1993.714013

  7. Hooper, D.C., Flann, N.S.: Improving the accuracy and robustness of genetic programming through expression simplification. In: Proceedings of the 1st Annual Conference on Genetic Programming, p. 428. MIT Press, Cambridge (1996)

    Google Scholar 

  8. Keijzer, M.: Improving symbolic regression with interval arithmetic and linear scaling. In: Ryan, C., Soule, T., Keijzer, M., Tsang, E., Poli, R., Costa, E. (eds.) EuroGP 2003. LNCS, vol. 2610, pp. 70–82. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36599-0_7

    Chapter  Google Scholar 

  9. Koza, J.R., Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)

    Google Scholar 

  10. McDermott, J., et al.: Genetic programming needs better benchmarks. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 791–798 (2012)

    Google Scholar 

  11. Naoki, M., McKay, B., Xuan, N., Daryl, E., Takeuchi, S.: A new method for simplifying algebraic expressions in genetic programming called equivalent decision simplification. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 171–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02481-8_24

    Chapter  Google Scholar 

  12. Moritz, P., et al.: Ray: a distributed framework for emerging AI applications. CoRR (2017). http://arxiv.org/abs/1712.05889

  13. Poli, R., McPhee, N.F.: Parsimony pressure made easy: solving the problem of bloat in GP. In: Borenstein, Y., Moraglio, A. (eds.) Theory and Principled Methods for the Design of Metaheuristics. NCS, pp. 181–204. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-33206-7_9

    Chapter  MATH  Google Scholar 

  14. Silva, S., Dignum, S., Vanneschi, L.: Operator equalisation for bloat free genetic programming and a survey of bloat control methods. Genetic Program. Evol. Mach. 13, 197–238 (2012). https://doi.org/10.1007/s10710-011-9150-5

    Article  Google Scholar 

  15. Sivanandam, S., Deepa, S.: Genetic Programming, pp. 131–163. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-73190-0_6

  16. Trujillo, L., Muñoz, L., Galván-López, E., Silva, S.: Neat genetic programming: controlling bloat naturally. Inf. Sci. 333, 21–43 (2015). https://doi.org/10.1016/j.ins.2015.11.010

    Article  Google Scholar 

  17. Uy, N.Q., Hien, N.T., Hoai, N.X., O’Neill, M.: Improving the generalisation ability of genetic programming with semantic similarity based crossover. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 184–195. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12148-7_16

    Chapter  Google Scholar 

  18. Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, R.I., Galván-López, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program Evolvable Mach. 12(2), 91–119 (2011)

    Article  Google Scholar 

  19. Vladislavleva, E.J., Smits, G.F., Den Hertog, D.: Order of nonlinearity as a complexity measure for models generated by symbolic regression via pareto genetic programming. IEEE Trans. Evol. Comput. 13(2), 333–349 (2008)

    Article  Google Scholar 

  20. Wong, P., Zhang, M.: Algebraic simplification of gp programs during evolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 927–934, GECCO 2006. Association for Computing Machinery, New York, NY, USA (2006). https://doi.org/10.1145/1143997.1144156

Download references

Author information

Authors and Affiliations

  1. ICET, Universidade Federal de Viçosa, Florestal, Brazil

    Gustavo F. V. de Oliveira & Marcus H. S. Mendes

Authors
  1. Gustavo F. V. de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcus H. S. Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gustavo F. V. de Oliveira .

Editor information

Editors and Affiliations

  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Oliveira, G.F.V., Mendes, M.H.S. (2021). Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13073. Springer, Cham. https://doi.org/10.1007/978-3-030-91702-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-91702-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91701-2

  • Online ISBN: 978-3-030-91702-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation