Advertisement

Extending Local Search in Geometric Semantic Genetic Programming

  • Mauro Castelli
  • Luca Manzoni
  • Luca MariotEmail author
  • Martina Saletta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11804)

Abstract

In this paper we continue the investigation of the effect of local search in geometric semantic genetic programming (GSGP), with the introduction of a new general local search operator that can be easily customized. We show that it is able to obtain results on par with the current best-performing GSGP with local search and, in most cases, better than standard GSGP.

Notes

Acknowledgments

This work was partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Azad, R.M.A., Ryan, C.: A simple approach to lifetime learning in genetic programming-based symbolic regression. Evol. Comput. 22(2), 287–317 (2014)CrossRefGoogle Scholar
  3. 3.
    Castelli, M., Manzoni, L., Vanneschi, L., Silva, S., Popovič, A.: Self-tuning geometric semantic genetic programming. Genet. Program. Evolvable Mach. 17(1), 55–74 (2016)CrossRefGoogle Scholar
  4. 4.
    Castelli, M., Trujillo, L., Vanneschi, L.: Energy consumption forecasting using semantic-based genetic programming with local search optimizer. Comput. Intell. Neurosci. 2015, 57 (2015)CrossRefGoogle Scholar
  5. 5.
    Castelli, M., Trujillo, L., Vanneschi, L., Popovič, A.: Prediction of relative position of ct slices using a computational intelligence system. Appl. Soft Comput. 46, 537–542 (2016)CrossRefGoogle Scholar
  6. 6.
    Castelli, M., Trujillo, L., Vanneschi, L., Silva, S., et al.: Geometric semantic genetic programming with local search. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 999–1006. ACM (2015)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Castelli, M., Vanneschi, L., Trujillo, L., Popovič, A.: Stock index return forecasting: semantics-based genetic programming with local search optimiser. Int. J. Bio-Inspired Comput. 10(3), 159–171 (2017)CrossRefGoogle Scholar
  9. 9.
    Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. Trans. Evol. Computat. 15(5), 591–607 (2011)CrossRefGoogle Scholar
  10. 10.
    Enríquez-Zárate, J., et al.: Automatic modeling of a gas turbine using genetic programming: an experimental study. Appl. Soft Comput. 50, 212–222 (2017)CrossRefGoogle Scholar
  11. 11.
    Hajek, P., Henriques, R., Castelli, M., Vanneschi, L.: Forecasting performance of regional innovation systems using semantic-based genetic programming with local search optimizer. Comput. Oper. Res. 106, 179–190 (2019)CrossRefGoogle Scholar
  12. 12.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT press, Cambridge (1992)zbMATHGoogle Scholar
  13. 13.
    Koza, J.R.: Human-competitive results produced by genetic programming. Genet. Program. Evolvable Mach. 11(3–4), 251–284 (2010)CrossRefGoogle Scholar
  14. 14.
    Trujillo, L., et al.: Local search is underused in genetic programming. In: Riolo, R., Worzel, B., Goldman, B., Tozier, B. (eds.) Genetic Programming Theory and Practice XIV. GEC, pp. 119–137. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-97088-2_8CrossRefGoogle Scholar
  15. 15.
    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_3CrossRefGoogle Scholar
  16. 16.
    Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, vol. 379. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Topchy, A., Punch, W.F.: Faster genetic programming based on local gradient search of numeric leaf values. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, GECCO2001, pp. 155–162, Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  18. 18.
    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_18CrossRefGoogle Scholar
  19. 19.
    Vanneschi, L., Castelli, M., Silva, S.: Measuring bloat, overfitting and functional complexity in genetic programming. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 877–884. ACM (2010)Google Scholar
  20. 20.
    Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)CrossRefGoogle Scholar
  21. 21.
    Z-Flores, E., Trujillo, L., Schütze, O., Legrand, P.: Evaluating the effects of local search in genetic programming. In: Tantar, A.-A., Tantar, E., Sun, J.-Q., Zhang, W., Ding, Q., Schütze, O., Emmerich, M., Legrand, P., Del Moral, P., Coello Coello, C.A. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. AISC, vol. 288, pp. 213–228. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07494-8_15CrossRefGoogle Scholar
  22. 22.
    Zhang, M., Smart, W.: Genetic programming with gradient descent search for multiclass object classification. In: Keijzer, M., O’Reilly, U.-M., Lucas, S., Costa, E., Soule, T. (eds.) EuroGP 2004. LNCS, vol. 3003, pp. 399–408. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24650-3_38CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mauro Castelli
    • 1
  • Luca Manzoni
    • 2
  • Luca Mariot
    • 2
    Email author
  • Martina Saletta
    • 1
  1. 1.NOVA Information Management School (NOVA IMS)Universidade Nova de LisboaLisbonPortugal
  2. 2.Dipartimento di Informatica Sistemistica e Comunicazione (DISCo)Università degli Studi di Milano BicoccaMilanItaly

Personalised recommendations