Extending Local Search in Geometric Semantic Genetic Programming
Conference paper
First Online:
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.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.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.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.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.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.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.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.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.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.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.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.Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT press, Cambridge (1992)zbMATHGoogle Scholar
- 13.Koza, J.R.: Human-competitive results produced by genetic programming. Genet. Program. Evolvable Mach. 11(3–4), 251–284 (2010)CrossRefGoogle Scholar
- 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.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.Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, vol. 379. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 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.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.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.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.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.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