Variable neighborhood programming for symbolic regression (original) (raw)

References

  1. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
    MATH Google Scholar
  2. Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
    MATH Google Scholar
  3. Cai, W., Pacheco-Vega, A., Sen, M., Yang, K.: Heat transfer correlations by symbolic regression. Int. J. Heat Mass Transf. 49(23–24), 4352 (2006)
    Article Google Scholar
  4. Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 44(8), 1761 (2011)
    Article Google Scholar
  5. Bouaziz, S., Dhahri, H., Alimi, A.M., Abraham, A.: A hybrid learning algorithm for evolving flexible beta basis function neural tree model. Neurocomputing 117, 107 (2013)
    Article Google Scholar
  6. Castelli, M., Trujillo, L., Vanneschi, L.: Energy consumption forecasting using semantic-based genetic programming with local search optimizer. Comput. Intell. Neurosci. 2015, 971908 (2015)
    Article Google Scholar
  7. De Arruda Pereira, M., Davis Júnior, C.A., Gontijo Carrano, E., De Vasconcelos, J.A.A.: A niching genetic programming-based multi-objective algorithm for hybrid data classification. Neurocomputing 133, 342 (2014)
    Article Google Scholar
  8. Choi, W.J., Choi, T.S.: Genetic programming-based feature transform and classification for the automatic detection of pulmonary nodules on computed tomography images. Inf. Sci. 212, 57 (2012)
    Article Google Scholar
  9. Lane, F., Azad, R., Ryan, C.: On effective and inexpensive local search techniques in genetic programming regression. In: Parallel Problem Solving from Nature: PPSN XIII. Lecture Notes in Computer Science, vol. 8672. Springer (2014)
  10. Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic programming via iterated local search for dynamic job shop scheduling. IEEE Trans. Cybern. 45(1), 1 (2015)
    Article Google Scholar
  11. Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097 (1997)
    Article MathSciNet Google Scholar
  12. Elleuch, S., Jarboui, B., Mladenovic, N.: Variable neighborhood programming: a new automatic programming method in artificial intelligence. Technical report, G-2016-92, GERAD, Montreal (2016)
  13. Elleuch, S., Hansen, P., Jarboui, B., Mladenović, N.: New VNP for automatic programming. Electron. Notes Discrete Math. 58, 191–198 (2017)
    Article Google Scholar
  14. Ghaddar, B., Sakr, N., Asiedu, Y.: Spare parts stocking analysis using genetic programming. Eur. J. Oper. Res. 252(1), 136 (2016)
    Article Google Scholar
  15. Ly, D.L., Lipson, H.: Learning symbolic representations of hybrid dynamical systems. J. Mach. Learn. Res. 13, 3585 (2012)
    MathSciNet MATH Google Scholar
  16. Deklel, A.K., Saleh, M.A., Hamdy, A.M., Saad, E.M.: Transfer learning with long term artificial neural network memory (LTANN-MEM) and neural symbolization algorithm (NSA) for solving high dimensional multi-objective symbolic regression problems. In: 2017 34th National Radio Science Conference (NRSC), pp. 343–352. IEEE (2017)
  17. Arnaldo, I., Krawiec, K., O’Reilly, U.M.: Multiple regression genetic programming. In: Proceedings of the 2014 Conference on Genetic and Evolutionary Computation: GECCO ’14, pp. 879–886. ACM Press, New York (2014)
  18. Karaboga, D., Ozturk, C., Karaboga, N., Gorkemli, B.: Artificial bee colony programming for symbolic regression. Inf. Sci. 209, 1 (2012)
    Article Google Scholar
  19. Uy, N.Q., Hoai, N.X., O’Neill, M., McKay, R.I., Galván-López, E.: Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet. Program. Evol. Mach. 12(2), 91 (2011)
    Article Google Scholar
  20. Peng, Y., Yuan, C., Qin, X., Huang, J., Shi, Y.: An improved gene expression programming approach for symbolic regression problems. Neurocomputing 137, 293 (2014)
    Article Google Scholar
  21. Icke, I., Bongard, J.C.: Improving genetic programming based symbolic regression using deterministic machine learning. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1763–1770. IEEE (2013)
  22. Rad, H.I., Feng, J., Iba, H.: GP-RVM: genetic programming-based symbolic regression using relevance vector machine. arXiv:1806.02502v (2018)
  23. Mladenović, N., Urošević, D.: Variable neighborhood search for the K-cardinality tree. In: Metaheuristics: Computer Decision-Making, Applied Optimization. Springer, Boston (2003)
  24. Hao, C., Ni, J., Wang, N., Yoshimura, T.: Interconnection allocation between functional units and registers in high-level synthesis. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 25(3), 1140 (2017)
    Article Google Scholar
  25. Brimberg, J., Mladenović, N., Todosijević, R., Urošević, D.: Less is more: solving the max-mean diversity problem with variable neighborhood search. Inf. Sci. 382–383, 179 (2017)
    Article Google Scholar
  26. Costa, L.R., Aloise, D., Mladenović, N.: Less is more: basic variable neighborhood search heuristic for balanced minimum sum-of-squares clustering. Inf. Sci. 415–416, 247 (2017)
    Article Google Scholar
  27. Mladenović, N., Todosijević, R., Urošević, D.: Less is more: basic variable neighborhood search for minimum differential dispersion problem. Inf. Sci. 326, 160 (2016)
    Article Google Scholar
  28. Concalves-de Silva, K., Aloise, D., Xavier-de Souza, S., Mladenovic, N.: Less is more: Simplified Nelder-Mead method for large unconstrained optimization, Yugoslav J. Oper. Res. 28 (2), 153–169 (2018)
  29. Mladenović, N., Alkandari, A., Pei, J., Todosijević, R., Pardalos, P.M.: Less is more approach: basic variable neighborhood search for the obnoxious p-median problem. Int. Trans. Oper. Res. 27, 480–493 (2019)
    Article MathSciNet Google Scholar
  30. Stadtmüller, U.: Asymptotic properties of nonparametric curve estimates. Period. Math. Hung. 17(2), 83 (1986)
    Article MathSciNet Google Scholar
  31. Brown, B.M., Chen, S.X.: Beta-Bernstein smoothing for regression curves with compact support. Scand. J. Stat. 26(1), 47 (1999)
    Article MathSciNet Google Scholar
  32. Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19(1), 1 (1991)
    MathSciNet MATH Google Scholar
  33. De Boor, C.: A Practical Guide to Splines: With 32 Figures. Springer, Berlin (2001)
    MATH Google Scholar
  34. Hoai, N., McKay, R., Essam, D., Chau, R.: Solving the symbolic regression problem with tree-adjunct grammar guided genetic programming: the comparative results. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No.02TH8600), vol. 2, pp. 1326–1331. IEEE (2002)
  35. Johnson, C.G.: Genetic Programming Crossover: Does It Cross over?, pp. 97–108. Springer, Berlin (2009)
    Book Google Scholar
  36. Keijzer, M.: Improving Symbolic Regression with Interval Arithmetic and Linear Scaling, pp. 70–82. Springer, Berlin (2003)
    MATH Google Scholar
  37. Hoang, T.H., Essam, D., McKay, B., Hoai, N.X.: Building on success in genetic programming: adaptive variation and developmental evaluation. In: Advances in Computation and Intelligence, pp. 137–146. Springer, Berlin (2007)
  38. Wong, P., Zhang, M.: SCHEME: Caching subtrees in genetic programming. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2678–2685. IEEE (2008)

Download references