Skip to main content
SpringerLink
Log in
Book cover

European Conference on Genetic Programming

EuroGP 2018: Genetic Programming pp 237–252Cite as

Geometric Crossover in Syntactic Space

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10781))

Abstract

This paper presents a geometric crossover operator for Tree-Based Genetic Programming that acts on the syntactic space, where each expression tree is represented in prefix notation. The proposed operator is compared to the standard subtree crossover on a symbolic regression problem, on the Santa Fe Ant Trail and on a classification problem. Statistically validated results show that the individuals produced using this method are significantly smaller than those produced by the subtree crossover, and have similar or better performance in the target tasks.

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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Moraglio, A.: Towards a Geometric Unification of Evolutionary Algorithms. Ph.D. thesis, Department of Computer Science, University of Essex (2007)

    Google Scholar 

  2. Galván-López, E., McDermott, J., O’Neill, M., Brabazon, A.: Defining locality as a problem difficulty measure in genetic programming. In: Genetic Programming and Evolvable Machines (2011)

    Google Scholar 

  3. Moraglio, A., Poli, R.: Geometric landscape of homologous crossover for syntactic trees. In: 2005 IEEE Congress on Evolutionary Computation (2005)

    Google Scholar 

  4. Poli, R., Langdon, W.B.: Genetic programming with one-point crossover. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds.) Soft Computing in Engineering Design and Manufacturing. Springer, London (1998). https://doi.org/10.1007/978-1-4471-0427-8_20

    Google Scholar 

  5. Krawiec, K., Lichocki, P.: Approximating geometric crossover in semantic space. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation (2009)

    Google Scholar 

  6. Moraglio, A., Krawiec, K., Johnson, C.G.: Geometric semantic genetic programming. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 21–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32937-1_3

    Chapter  Google Scholar 

  7. Vanneschi, L., Castelli, M., Manzoni, L., Silva, S.: A new implementation of geometric semantic GP and its application to problems in pharmacokinetics. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) EuroGP 2013. LNCS, vol. 7831, pp. 205–216. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37207-0_18

    Chapter  Google Scholar 

  8. Brameier, M., Banzhaf, W.: Linear Genetic Programming. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-31030-5

    MATH  Google Scholar 

  9. Ferreira, C.: Gene expression programming in problem solving. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds.) Soft Computing and Industry: Recent Applications. Springer, London (2002). https://doi.org/10.1007/978-1-4471-0123-9_54

    Google Scholar 

  10. Paterson, M., Dančík, V.: Longest common subsequences. In: Prívara, I., Rovan, B., Ruzička, P. (eds.) MFCS 1994. LNCS, vol. 841, pp. 127–142. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58338-6_63

    Chapter  Google Scholar 

  11. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. The MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  12. Koza, J.R.: Genetic programming: On the Programming of Computers by Means of Natural Selection. MIT press, Cambridge (1992)

    MATH  Google Scholar 

  13. 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 

  14. Lichman, M.: UCI Machine Learning Repository (2013). http://archive.ics.uci.edu/ml

Download references

Acknowledgement

This article is based upon work from COST Action CA15140: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), supported by COST (European Cooperation in Science and Technology), www.cost.eu. Support by national funds through the Portuguese Foundation for Science and Technology (FCT) and by the European Regional Development Fund (FEDER) through COMPETE 2020 – Operational Program for Competitiveness and Internationalization (POCI) is also acknowledged. J. Macedo acknowledges the Portuguese Foundation for Science and Technology for Ph.D. studentship SFRH/BD/129673/2017.

Author information

Authors and Affiliations

  1. ISR, Department of Electrical and Computer Engineering, University of Coimbra, 3030 290, Coimbra, Portugal

    João Macedo

  2. CISUC, Department of Informatics Engineering, University of Coimbra, 3030 290, Coimbra, Portugal

    João Macedo, Carlos M. Fonseca & Ernesto Costa

Authors
  1. João Macedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos M. Fonseca
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ernesto Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to João Macedo .

Editor information

Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    Mauro Castelli

  2. Brno University of Technology, Brno, Czech Republic

    Lukas Sekanina

  3. Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. University of Parma, Parma, Italy

    Stefano Cagnoni

  5. University of Cádiz, Cádiz, Spain

    Pablo García-Sánchez

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Macedo, J., Fonseca, C.M., Costa, E. (2018). Geometric Crossover in Syntactic Space. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2018. Lecture Notes in Computer Science(), vol 10781. Springer, Cham. https://doi.org/10.1007/978-3-319-77553-1_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77553-1_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77552-4

  • Online ISBN: 978-3-319-77553-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation