A Vectorial Approach to Genetic Programming

  • Irene AzzaliEmail author
  • Leonardo Vanneschi
  • Sara Silva
  • Illya Bakurov
  • Mario Giacobini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11451)


Among the various typologies of problems to which Genetic Programming (GP) has been applied since its origins, symbolic regression is one of the most popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a panel representation where each observation corresponds to a collection on a subject at a precise time instant. However, representing data in this form may imply a loss of information: for instance, the algorithm may not be able to recognize observations belonging to the same subject and their recording order. To maintain the source of knowledge supplied by ordered sequences as time series, we propose a new approach to GP that keeps instances of the same observation together in a vector, introducing vectorial variables as terminals. This new representation allows aggregate functions in the primitive GP set, included with the purpose of describing the behaviour of vectorial variables. In this work, we perform a comparative analysis of vectorial GP (VE-GP) against standard GP (ST-GP). Experiments are conducted on different benchmark problems to highlight the advantages of this new approach.


Genetic programming Vector-based representation Panel data regression 



This work was partially supported by FCT through funding of LASIGE Research Unit (UID/CEC/00408/2019), BioISI Research Unit (UID/MULTI/04046/2013), and projects INTERPHENO (PTDC/ASP-PLA/28-726/2017), PERSEIDS (PTDC/EMS-SIS/0642/2014), OPTOX (PTDC/CTA-AMB/30056/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DS-AIPA/DS/0022/2018) and PREDICT (PTDC/CCI-CIF/29877/2017).


  1. 1.
    Poli, R., Langdon, W., McPhee, N.: A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd. (2008).
  2. 2.
    Dermofal, D.: Time-series cross-sectional and panel data models. Spat. Anal. Soc. Sci. 32, 141–157 (2015). Scholar
  3. 3.
    Guo, H., Jack, L.B., Nandi, A.K.: Automated feature extraction using genetic programming for bearing condition monitoring. In: Proceedings of the 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, pp. 519–528 (2004).
  4. 4.
    De-Falco, I., Della-Cioppa, A., Tarantino, E.: A genetic programming system for time series prediction and its application to el niño forecast. Soft Comput.: Methodol. Appl.cations 32, 151–162 (2005). Scholar
  5. 5.
    Holladay, K., Robbins, K.A.: Evolution of signal processing algorithm using vector based genetic programming. In: 15th International Conference on Digital Signal Processing, pp. 503–506 (2007).
  6. 6.
    Bartashevich, P., Bakurov, I., Mostaghim, S., Vanneschi, L.: Evolving PSO algorithm design in vector fields using geometric semantic GP. In: GECCO 2018: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 262–263 (2018).
  7. 7.
    Alfaro, E.C., Sharman, K., Esparcia-Alcázar, A.: Genetic programming and serial processing for time series classification. Evol. Comput. 22, 265–285 (2013). Scholar
  8. 8.
    Silva, S., Almeida, J.: GPLAB a genetic programming toolbox for MATLAB (2007).
  9. 9.
    McDermott, J., O’Reilly, U.M., Luke, S., White, D.: A community-led effort towards improving experimentation in genetic programming.
  10. 10.
    Vargha, A., Delaney, H.D.: A critique and improvement of the CL common language effect size statistics of McGraw and Wong. J. Educ. Behav. Stat. 25(2), 101–132 (2000). Scholar
  11. 11.
    Luke, S., Panait, L.: Lexicographic parsimony pressure. In: GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 829–836 (2002)Google Scholar
  12. 12.
    Bisanzio, D., et al.: Spatio-temporal patterns of distribution of West Nile virus vectors in eastern Piedmont region, Italy. Parasites Vectors 4, 230 (2011). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DAMU - Data Analysis and Modeling Unit, Department of Veterinary SciencesUniversity of TorinoTurinItaly
  2. 2.NOVA Information Management School (NOVA IMS)Universidade Nova de LisboaLisbonPortugal
  3. 3.LASIGE, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
  4. 4.BioISI – Biosystems & Integrative Sciences Institute, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal

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