Skip to main content
SpringerLink
Log in
Book cover

Genetic Programming Theory and Practice XVI pp 193–207Cite as

Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal

  • Chapter
  • First Online:

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

In the era of Deep Learning and Big Data, the place of Genetic Programming (GP) within the Machine Learning area seems difficult to define. Whether it is due to technical constraints or conceptual barriers, GP is currently not a paradigm of choice for the development of state-of-the-art machine learning systems. Nonetheless, there are important features of the GP approach that make it unique and should continue to be actively explored and studied. In this work we focus on two aspects of GP that have previously received little or no attention, particularly in tree-based GP for symbolic regression. First, on the potential of GP to perform transfer learning, where solutions evolved for one problem are transferred to another. Second, on the potential of GP individuals to detect the true underlying structure of an input dataset and detect anomalies in the input data, what are known as outliers. This work presents initial results on both issues, with the goal of fostering discussion and showing that there is still untapped potential in the GP paradigm.

This is a preview of subscription content, 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   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Notes

  1. 1.

    Experimental evidence more or less confirmed the No-Free-Lunch theorem in many domains where, on average, many algorithms tended to perform similarly.

References

  1. Castelli, M., Trujillo, L., Vanneschi, L., Popovi, A.: Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings 102, 67–74 (2015)

    Article  Google Scholar 

  2. Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Transactions on Evolutionary Computation 15(5), 591–607 (2011)

    Article  Google Scholar 

  3. Chitty, D.M.: Faster GPU based genetic programming using A two dimensional stack. CoRR abs/1601.00221 (2016)

    Google Scholar 

  4. Dozal, L., Olague, G., Clemente, E., Hernández, D.E.: Brain programming for the evolution of an artificial dorsal stream. Cognitive Computation 6(3), 528–557 (2014)

    Article  Google Scholar 

  5. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  6. Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press (2008)

    Google Scholar 

  7. Fortin, F.A., et al.: DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research 13, 2171–2175 (2012)

    MathSciNet  MATH  Google Scholar 

  8. Friedman, J.H.: Multivariate adaptive regression splines. Ann. Statist. 19(1), 1–67 (1991)

    Article  MathSciNet  Google Scholar 

  9. Galván-López, E., Vazquez-Mendoza, L., Schoenauer, M., Trujillo, L.: On the Use of Dynamic GP Fitness Cases in Static and Dynamic Optimisation Problems. In: EA 2017- International Conference on Artificial Evolution, pp. 1–14. Paris, France (2017)

    Google Scholar 

  10. Gonçalves, I., Silva, S.: Balancing learning and overfitting in genetic programming with interleaved sampling of training data. In: K. Krawiec, et al. (eds.) Genetic Programming, LNCS, vol. 7831, pp. 73–84. Springer Berlin Heidelberg (2013)

    Chapter  Google Scholar 

  11. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)

    Google Scholar 

  12. Hubert, M., Rousseeuw, P.J., Van Aelst, S.: High-breakdown robust multivariate methods. Statist. Sci. 23 (2008)

    Article  MathSciNet  Google Scholar 

  13. Kotanchek, M., et al.: Pursuing the Pareto Paradigm: Tournaments, Algorithm Variations and Ordinal Optimization, pp. 167–185. Springer US (2007)

    Google Scholar 

  14. López, U., Trujillo, L., Martinez, Y., Legrand, P., Naredo, E., Silva, S.: RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming, pp. 114–130. Springer International Publishing, Cham (2017)

    Google Scholar 

  15. Martínez, Y., Trujillo, L., Legrand, P., Galván-López, E.: Prediction of expected performance for a genetic programming classifier. Genetic Programming and Evolvable Machines 17(4), 409–449 (2016)

    Article  Google Scholar 

  16. McConaghy, T.: Genetic Programming Theory and Practice IX, chap. FFX: Fast, Scalable, Deterministic Symbolic Regression Technology, pp. 235–260. Springer New York, New York, NY (2011)

    Google Scholar 

  17. Miranda, L.F., Oliveira, L.O.V.B., Martins, J.F.B.S., Pappa, G.L.: How noisy data affects geometric semantic genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17, pp. 985–992. ACM, New York, NY, USA (2017)

    Google Scholar 

  18. Moraglio, A., Krawiec, K., Johnson, C.G.: Parallel Problem Solving from Nature - PPSN XII: 12th International Conference, Taormina, Italy, September 1–5, 2012, Proceedings, Part I, chap. Geometric Semantic Genetic Programming, pp. 21–31. Springer Berlin Heidelberg, Berlin, Heidelberg (2012)

    Google Scholar 

  19. Muñoz, L., Silva, S., Trujillo, L.: M3GP: multiclass classification with GP. In: P. Machado, et al. (eds.) 18th European Conference on Genetic Programming, LNCS, vol. 9025, pp. 78–91. Springer, Copenhagen (2015)

    Google Scholar 

  20. Muñoz, L., Trujillo, L., Silva, S., Vanneschi, L.: Evolving multidimensional transformations for symbolic regression with m3gp. Memetic Computing (2018). https://doi.org/10.1007/s12293-018-0274-5

  21. Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evolutionary Computation 6, 1–24 (2012)

    Article  Google Scholar 

  22. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. on Knowl. and Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  23. Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S.: A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing 2016 (1), 67 (2016)

    Article  Google Scholar 

  24. Roberts, S.C., Howard, D., Koza, J.R.: Evolving modules in genetic programming by subtree encapsulation. In: Proceedings of the 4th European Conference on Genetic Programming, EuroGP ’01, pp. 160–175. Springer-Verlag, Berlin, Heidelberg (2001)

    MATH  Google Scholar 

  25. Spector, L.: Assessment of problem modality by differential performance of lexicase selection in genetic programming: a preliminary report. In: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, GECCO Companion ’12, pp. 401–408. ACM (2012)

    Google Scholar 

  26. Tran, C.T., Zhang, M., Andreae, P., Xue, B.: Genetic programming based feature construction for classification with incomplete data. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17, pp. 1033–1040. ACM, New York, NY, USA (2017)

    Google Scholar 

  27. Trujillo, L., Muñoz, L., Galván-López, E., Silva, S.: Neat genetic programming. Inf. Sci. 333, 21–43 (2016)

    Article  Google Scholar 

  28. Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and buildings 49, 560–567 (2012)

    Article  Google Scholar 

  29. 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 Transactions on Evolutionary Computation 13(2), 333–349 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by CONACYT (Mexico) Fronteras de la Ciencia 2015-2 Project No. FC-2015-2/944 and TecNM project no. 6826-18-P, and first and third authors were respectively supported by CONACYT graduate scholarship No. 302526 and No. 573397.

Author information

Authors and Affiliations

  1. Instituto Tecnológico de Tijuana, Tijuana, B.C., México

    Leonardo Trujillo, Luis Muñoz, Uriel López & Daniel E. Hernández

Authors
  1. Leonardo Trujillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Uriel López
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel E. Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leonardo Trujillo .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, John R. Koza Chair, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Cognitive Science, Hampshire College, Amherst, MA, USA

    Lee Spector

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Leigh Sheneman

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Trujillo, L., Muñoz, L., López, U., Hernández, D.E. (2019). Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal. In: Banzhaf, W., Spector, L., Sheneman, L. (eds) Genetic Programming Theory and Practice XVI. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-030-04735-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04735-1_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04734-4

  • Online ISBN: 978-3-030-04735-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation