Genetic Operators Impact on Genetic Algorithms Based Variable Selection

  • Marco VannucciEmail author
  • Valentina Colla
  • Silvia Cateni
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 193)


This paper faces the problem of variables selection through the use of a genetic algorithm based metaheuristic approach. The method is based on the evolution of a population of variables subsets, which is led by the genetic operators determining their selection and improvement through the algorithm generations. The impact of different genetic operators expressly designed for this purpose is assessed through a test campaign. The results show that the use of specific operators can lead to remarkable improvements in terms of selection quality.


Varables selection Genetic algorithms Genetic operators 


  1. 1.
    Cateni, S., Colla, V.: The importance of variable selection for neural networks-based classification in an industrial context. Smart Innov. Syst. Technol. 54, 363–370 (2016)CrossRefGoogle Scholar
  2. 2.
    Cateni, S., Colla, V.: Variable selection for efficient design of machine learning-based models: efficient approaches for industrial applications. Commun. Comput. Inf. Sci. 629, 352–366 (2016)Google Scholar
  3. 3.
    Cateni, S., Colla, V., Vannucci, M.: General purpose input variables extraction: a genetic algorithm based procedure give a gap. In: 2009 9th International Conference on Intelligent Systems Design and Applications, pp. 1278–1283. IEEE (2009)Google Scholar
  4. 4.
    Cateni, S., Colla, V., Vannucci, M.: Variable selection through genetic algorithms for classification purpose. In: Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010, pp. 6–11 (2010)Google Scholar
  5. 5.
    Cateni, S., Colla, V., Vannucci, M.: A genetic algorithm-based approach for selecting input variables and setting relevant network parameters of a som-based classifier. Int. J. Simul. Syst. Sci. Technol. 12(2), 30–37 (2011)Google Scholar
  6. 6.
    Cateni, S., Colla, V., Vannucci, M.: A hybrid feature selection method for classification purposes. In: Proceedings—UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014, pp. 39–44 (2014)Google Scholar
  7. 7.
    Colla, V., Matino, I., Dettori, S., Cateni, S., Matino, R.: Reservoir computing approaches applied to energy management in industry, Communications in Computer and Information Science, pp. 66–69, vol. 1000. Springer (2019)Google Scholar
  8. 8.
    Colla, V., Vannucci, M., Bacchi, L., Valentini, R.: Neural networks-based prediction of hardenability of high performance carburizing steels for automotive applications. La Metallurgia Italiana 112(1), 47–53 (2020)Google Scholar
  9. 9.
    Dimatteo, A., Vannucci, M., Colla, V.: Prediction of hot deformation resistance during processing of microalloyed steels in plate rolling process. Int. J. Adv. Manuf. Technol. 66(9–12), 1511–1521 (2013)CrossRefGoogle Scholar
  10. 10.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)Google Scholar
  11. 11.
    He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: Advances in Neural Information Processing Systems, pp. 507–514 (2006)Google Scholar
  12. 12.
    Latorre Carmona, P., Sotoca, J.M., Pla, F.: Filter-type variable selection based on information measures for regression tasks. Entropy 14(2), 323–343 (2012)CrossRefGoogle Scholar
  13. 13.
    Mitchell, T.J., Beauchamp, J.J.: Bayesian variable selection in linear regression. J. Am. Stat. Assoc. 83(404), 1023–1032 (1988)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Raftery, A.E., Dean, N.: Variable selection for model-based clustering. J. Am. Stat. Assoc. 101(473), 168–178 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Roobaert, D., Karakoulas, G., Chawla, N.V.: Information gain, correlation and support vector machines. In: Feature extraction, pp. 463–470. Springer (2006)Google Scholar
  16. 16.
    Sebban, M., Nock, R.: A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recog. 35(4), 835–846 (2002)CrossRefGoogle Scholar
  17. 17.
    Sgarbi, M., Colla, V., Cateni, S., Higson, S.: Pre-processing of data coming from a laser-emat system for non-destructive testing of steel slabs. ISA Trans. 51(1), 181–188 (2012)CrossRefGoogle Scholar
  18. 18.
    Vannucci, M., Colla, V.: Fuzzy adaptation of crossover and mutation rates in genetic algorithms based on population performance. J. Intell. Fuzzy Syst. 28(4), 1805–1818 (2015)CrossRefGoogle Scholar
  19. 19.
    Vannucci, M., Colla, V.: Quality improvement through the preventive detection of potentially defective products in the automotive industry by means of advanced artificial intelligence techniques. In: Intelligent Decision Technologies 2019, pp. 3–12. Smart Innovation, Systems and Technologies, Springer (2019)Google Scholar
  20. 20.
    Vannucci, M., Colla, V., Dettori, S.: Fuzzy adaptive genetic algorithm for improving the solution of industrial optimization problems. IFAC-PapersOnLine 49(12), 1128–1133 (2016)CrossRefGoogle Scholar
  21. 21.
    Wu, S., Flach, P.A.: Feature selection with labelled and unlabelled data. ECML/PKDD 2, 156–167 (2002)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Marco Vannucci
    • 1
    Email author
  • Valentina Colla
    • 1
  • Silvia Cateni
    • 1
  1. 1.Scuola Superiore Sant’Anna, Istituto TeCIPPisaItaly

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