Skip to main content
SpringerLink
Log in

SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14631))

Included in the following conference series:

  • 144 Accesses

Abstract

Geometric semantic genetic programming (GSGP) is a successful variant of genetic programming (GP), able to induce a unimodal error surface for all supervised learning problems. However, a limitation of GSGP is its tendency to generate offspring larger than their parents, resulting in continually growing program sizes. This leads to the creation of models that are often too complex for human comprehension. This paper presents a novel GSGP variant, the Semantic Learning algorithm with Inflate and deflate Mutations (SLIM_GSGP). SLIM_GSGP retains the essential theoretical characteristics of traditional GSGP, including the induction of a unimodal error surface and introduces a novel geometric semantic mutation, the deflate mutation, which generates smaller offspring than its parents. The study introduces four SLIM_GSGP variants and presents experimental results demonstrating that, across six symbolic regression test problems, SLIM_GSGP consistently evolves models with equal or superior performance on unseen data compared to traditional GSGP and standard GP. These SLIM_GSGP models are significantly smaller than those produced by traditional GSGP and are either smaller or of comparable size to standard GP models. Notably, the compactness of SLIM_GSGP models allows for human interpretation.

Supported by SPECIES Society.

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 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Note that this paper exclusively presents the definition of GSM in the context of symbolic regression problems; for GSO definitions in other domains, the reader is referred to [19].

  2. 2.

    For an explanation of the input variables of the Instanbul dataset (that represent stock market indicators), the reader is referred to [1].

References

  1. Akbilgic, O., Bozdogan, H., Balaban, M.E.: A novel hybrid RBF neural networks model as a forecaster. Stat. Comput. 24(3), 365–375 (2014). https://doi.org/10.1007/s11222-013-9375-7

    Article  MathSciNet  Google Scholar 

  2. Archetti, F., Lanzeni, S., Messina, E., Vanneschi, L.: Genetic programming for computational pharmacokinetics in drug discovery and development. Genet. Program Evolvable Mach. 8(4), 413–432 (2007)

    Article  Google Scholar 

  3. Bakurov, I., et al.: Geometric semantic genetic programming with normalized and standardized random programs. Genet. Program Evolvable Mach. 25, 6 (2024). https://doi.org/10.1007/s10710-024-09479-1

  4. Castelli, M., Manzoni, L.: GSGP-C++ 2.0: a geometric semantic genetic programming framework. SoftwareX 10, 100313 (2019). https://doi.org/10.1016/j.softx.2019.100313. https://www.sciencedirect.com/science/article/pii/S2352711019301736

  5. Castelli, M., Manzoni, L., Gonçalves, I., Vanneschi, L., Trujillo, L., Silva, S.: An analysis of geometric semantic crossover: a computational geometry approach. In: International Joint Conference on Computational Intelligence (2016)

    Google Scholar 

  6. Castelli, M., Trujillo, L., Vanneschi, L.: Energy consumption forecasting using semantic-based genetic programming with local search optimizer. Intell. Neurosci. 2015 (2015). https://doi.org/10.1155/2015/971908

  7. Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., Z-Flores, E., Legrand, P.: Geometric semantic genetic programming with local search. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO 2015, pp. 999–1006. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2739480.2754795

  8. Castelli, M., Vanneschi, L., Popovič, A.: Controlling individuals growth in semantic genetic programming through elitist replacement. Intell. Neurosci. 2016 (2016). https://doi.org/10.1155/2016/8326760

  9. Castelli, M., Vanneschi, L., Silva, S.: Prediction of high performance concrete strength using genetic programming with geometric semantic genetic operators. Expert Syst. Appl. 40(17), 6856–6862 (2013)

    Article  Google Scholar 

  10. Castelli, M., Vanneschi, L., Silva, S., Ruberto, S.: How to exploit alignment in the error space: two different GP models. In: Riolo, R., Worzel, W.P., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XII. GEC, pp. 133–148. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16030-6_8

    Chapter  Google Scholar 

  11. Dubitzky, W., Granzow, M., Berrar, D.P.: Fundamentals of Data Mining in Genomics and Proteomics. Springer, Cham (2006)

    Google Scholar 

  12. Galván, E., Schoenauer, M.: Promoting semantic diversity in multi-objective genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2019, pp. 1021–1029. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3321707.3321854

  13. Gonçalves, I., Silva, S., Fonseca, C.M.: On the generalization ability of geometric semantic genetic programming. In: Machado, P., et al. (eds.) EuroGP 2015. LNCS, vol. 9025, pp. 41–52. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16501-1_4

    Chapter  Google Scholar 

  14. Kommenda, M., Kronberger, G., Affenzeller, M., Winkler, S.M., Burlacu, B.: Evolving simple symbolic regression models by multi-objective genetic programming. In: Riolo, R., Worzel, B., Kotanchek, M., Kordon, A. (eds.) Genetic Programming Theory and Practice XIII. GEC, pp. 1–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-34223-8_1

    Chapter  Google Scholar 

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

    Google Scholar 

  16. Martins, J.F.B.S., Oliveira, L.O.V.B., Miranda, L.F., Casadei, F., Pappa, G.L.: Solving the exponential growth of symbolic regression trees in geometric semantic genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, pp. 1151–1158. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3205455.3205593

  17. McDermott, J., Agapitos, A., Brabazon, A., O’Neill, M.: Geometric semantic genetic programming for financial data. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 215–226. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45523-4_18

    Chapter  Google Scholar 

  18. Moraglio, A.: An efficient implementation of GSGP using higher-order functions and memoization. In: Johnson, C., Krawiec, K., Moraglio, A., O’Neill, M. (eds.) Semantic Methods in Genetic Programming, Ljubljana, Slovenia (2014). http://www.cs.put.poznan.pl/kkrawiec/smgp2014/uploads/Site/Moraglio2.pdf. Workshop at Parallel Problem Solving from Nature 2014 Conference

  19. 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 

  20. Moraglio, A., Mambrini, A.: Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 989–996. Association for Computing Machinery, New York (2013). https://doi.org/10.1145/2463372.2463492

  21. Moraglio, A., Mambrini, A.: Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression. In: Proceedings of the Annual International Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 989–996. ACM, New York (2013)

    Google Scholar 

  22. Parrott, D., Li, X., Ciesielski, V.: Multi-objective techniques in genetic programming for evolving classifiers. In: 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1141–1148 (2005). https://doi.org/10.1109/CEC.2005.1554819

  23. Pawlak, T.P., Krawiec, K.: Competent geometric semantic genetic programming for symbolic regression and Boolean function synthesis. Evol. Comput. 26(2), 177–212 (2018)

    Article  Google Scholar 

  24. Pietropolli, G., Manzoni, L., Paoletti, A., Castelli, M.: Combining geometric semantic GP with gradient-descent optimization. In: Medvet, E., Pappa, G., Xue, B. (eds.) EuroGP 2022. LNCS, vol. 13223, pp. 19–33. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-02056-8_2

    Chapter  Google Scholar 

  25. Rafiei, M.: Residential Building Data Set. UCI Machine Learning Repository (2018). https://doi.org/10.24432/C5S896

  26. Silva, S., Vanneschi, L.: Operator equalisation, bloat and overfitting: a study on human oral bioavailability prediction. In: Rothlauf, F. (ed.) Genetic and Evolutionary Computation Conference, GECCO 2009, Proceedings, Montreal, Québec, Canada, 8–12 July 2009, pp. 1115–1122. ACM (2009)

    Google Scholar 

  27. Tsanas, A., Xifara, A.: Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build. 49, 560–567 (2012). https://doi.org/10.1016/j.enbuild.2012.03.003. https://www.sciencedirect.com/science/article/pii/S037877881200151X

  28. Vanneschi, L.: An introduction to geometric semantic genetic programming. In: Schütze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds.) NEO 2015. SCI, vol. 663, pp. 3–42. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-44003-3_1

    Chapter  Google Scholar 

  29. Vanneschi, L., et al.: Improving maritime awareness with semantic genetic programming and linear scaling: prediction of vessels position based on AIS data. In: Mora, A.M., Squillero, G. (eds.) EvoApplications 2015. LNCS, vol. 9028, pp. 732–744. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16549-3_59

    Chapter  Google Scholar 

  30. Vanneschi, L., Castelli, M., Gonçalves, I., Manzoni, L., Silva, S.: Geometric semantic genetic programming for biomedical applications: a state of the art upgrade. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 177–184 (2017). https://doi.org/10.1109/CEC.2017.7969311

  31. 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 

  32. Vanneschi, L., Castelli, M., Scott, K., Trujillo, L.: Alignment-based genetic programming for real life applications. Swarm Evol. Comput. 44, 840–851 (2019). https://doi.org/10.1016/j.swevo.2018.09.006. https://www.sciencedirect.com/science/article/pii/S2210650218300208

  33. Vanneschi, L., Silva, S.: Lectures on Intelligent Systems. Natural Computing Series, Springer, Cham (2023). https://doi.org/10.1007/978-3-031-17922-8

    Book  Google Scholar 

  34. Vanneschi, L., Silva, S., Castelli, M., Manzoni, L.: Geometric semantic genetic programming for real life applications. In: Riolo, R., Moore, J.H., Kotanchek, M. (eds.) Genetic Programming Theory and Practice XI. GEC, pp. 191–209. Springer, New York (2014). https://doi.org/10.1007/978-1-4939-0375-7_11

    Chapter  Google Scholar 

  35. 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 (2009). https://doi.org/10.1109/TEVC.2008.926486

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.

Author information

Authors and Affiliations

  1. NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal

    Leonardo Vanneschi

Authors
  1. Leonardo Vanneschi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leonardo Vanneschi .

Editor information

Editors and Affiliations

  1. University of Torino, Grugliasco, Italy

    Mario Giacobini

  2. Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

  3. University of Trieste, Trieste, Italy

    Luca Manzoni

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vanneschi, L. (2024). SLIM_GSGP: The Non-bloating Geometric Semantic Genetic Programming. In: Giacobini, M., Xue, B., Manzoni, L. (eds) Genetic Programming. EuroGP 2024. Lecture Notes in Computer Science, vol 14631. Springer, Cham. https://doi.org/10.1007/978-3-031-56957-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56957-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56956-2

  • Online ISBN: 978-3-031-56957-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation