Combining Objective Response Detectors Using Genetic Programming

  • Leonardo Bonato FelixEmail author
  • Quenaz Bezerra Soares
  • Antonio Mauricio Ferreira Leite Miranda de Sá
  • David Martin Simpson
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)


Many Objective Response Detectors (ORD) have been proposed based on ratios extracted from statistical methods. This work proposes a new approach to automatically generate ORD techniques, based on the combination of the existing ones by genetic programming. In this first study of this kind, the best ORD functions obtained with this approach were about 4% more sensitive than the best original ORD. It is concluded that genetic programming applied to create ORD functions has a potential to find non-obvious functions with better performances than established alternatives.


Objective Response Detection Genetic programming Evoked responses 



This research was supported by the Brazilian agency CNPq, CAPES and FAPEMIG.

Conflict of Interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Dobie, R.A., Wilson, M.J.: Analysis of auditory evoked potentials by magnitude-squared coherence. Ear Hear. 10, 2–13 (1889)CrossRefGoogle Scholar
  2. 2.
    Fridman, J., Zappulla, R., Bergelson, M., Greenblatt, E., Malis, L., Morrell, F., Hoeppner, T.: Application of phase spectral analysis for brain stem auditory evoked potential detection in normal subjects and patients with posterior fossa tumors. Audiology 23, 99–113 (1984)CrossRefGoogle Scholar
  3. 3.
    Dobie, R., Wilson, M.J.: Objective response detection in the frequency domain. Electroencephalogr. Clin. Neurophysiol. 88, 516–524 (1993)CrossRefGoogle Scholar
  4. 4.
    Shumway, R.H.: Applied Statistical Time Series Analysis, 1st edn. Prentice-Hall, New Jersey (1988)Google Scholar
  5. 5.
    Ram, K.R., Lal, S.P., Ahmed, M.R.: Design and optimization of airfoils and a 20 kW wind turbine using multi-objective genetic algorithm and HARP Opt code. Renewable Energy 30, 1e12 (2018)Google Scholar
  6. 6.
    Pak, T.C., Ri, Y.C.: Optimum designing of the vapor compression heat pump using system using genetic algorithm. Appl. Therm. Eng. 147, 492–500 (2019)CrossRefGoogle Scholar
  7. 7.
    Sahin, F.E.: Open-source optimization algorithms for optical design. Optik 178, 1016–1022 (2019)CrossRefGoogle Scholar
  8. 8.
    Lee, C.K.H.: A review of applications of genetic algorithms in operations management. Eng. Appl. Artif. Intell. 76, 1–12 (2018)CrossRefGoogle Scholar
  9. 9.
    Mostafa, N., Horta, N., Ravelo-García, A.G., Morgado-Dias, F.: Analog active filter design using a multi objective genetic algorithm. Int. J. Electron. Commun. 93, 83–94 (2018)CrossRefGoogle Scholar
  10. 10.
    Penchalaiah, D., Kumar, G.N., Gade, M.M., Talole, S.E.: Optimal compensator design using genetic algorithm. IFAC-PapersOnLine 51, 518–523 (2018)CrossRefGoogle Scholar
  11. 11.
    Hernandez-Beltran, J.E., Diaz-Ramirez, V.H., Trujillo, L., Legrand, P.: Design of estimators for restoration of images degraded by haze using genetic programming. Swarm Evol. Comput. 2019, 49–63 (2019)CrossRefGoogle Scholar
  12. 12.
    Verdier, C.F., Mazo, M.: Formal controller synthesis via genetic programming. IFAC-PapersOnLine 50, 7205–7210 (2017)CrossRefGoogle Scholar
  13. 13.
    Mehr, A.D., Nourani, V., Kahya, E., Hrnjica, B., Sattar, A.M.A., Yaseen, Z.M.: Genetic programming in water resources engineering: A state-of-the-art review. J. Hydrol. 566, 643–667 (2018)CrossRefGoogle Scholar
  14. 14.
    Shafer, C.A.: Data structures & algorithm analysis in Java, 3rd edn. Dover Publications, Mineola (2011)Google Scholar
  15. 15.
    Felix, L.B., Rocha, P.F.F., Mendes, E.M.A.M., Miranda de Sá, A.M.F.L.: Multivariate approach for estimating the local spectral F-test and its application to the EEG during photic stimulation. Comput. Methods Programs Biomed. 162, 87–91 (2018)CrossRefGoogle Scholar
  16. 16.
    Goldenholz, D.M., Ahlfors, S.P., Hämäläinen, M.S., Sharon, D., Ishitobi, M., Vaina, L.M., Stufflebeam, S.M.: Mapping the signal to noise ratios of cortical sources in magnetoencephalography and electroencephalography. Hum. Brain Map. 30.4, 1077–1086 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringFederal University of ViçosaViçosaBrazil
  2. 2.Institute of Sound and Vibration Research, University of SouthamptonSouthamptonUK
  3. 3.Biomedical Engineering Program/COPPEFederal University of Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations