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Hybridization of Analytic Programming and Differential Evolution for Time Series Prediction

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Hybrid Artificial Intelligent Systems (HAIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10334))

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Abstract

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of the hybrid connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the applicability and differences in performance of AP driven by basic canonical strategy of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). An experiment with three case studies has been carried out here with the several time series consisting of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

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References

  1. Zelinka, I., Davendra, D., Senkerik, R., Jasek, R., Oplatkova, Z.: Analytical programming - a novel approach for evolutionary synthesis of symbolic structures. In: Kita, E. (ed.) Evolutionary Algorithms. InTech, Rijeka (2011)

    Google Scholar 

  2. Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  3. Wang, W.-C., Chau, K.-W., Cheng, C.-T., Qiu, L.: A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol. 374(3), 294–306 (2009)

    Article  Google Scholar 

  4. Santini, M., Tettamanzi, A.: Genetic programming for financial time series prediction. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, Andrea G.B., Langdon, William B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 361–370. Springer, Heidelberg (2001). doi:10.1007/3-540-45355-5_29

    Chapter  Google Scholar 

  5. Pallit, A., Popovic, D.: Computational Intelligence in Time Series Forecasting. Springer, Heidelberg (2005)

    Google Scholar 

  6. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Massachusetts (1992)

    MATH  Google Scholar 

  7. Zelinka, I., Oplatková, Z., Nolle, L.: Boolean symmetry function synthesis by means of arbitrary evolutionary algorithms-comparative study. Int. J. Simul. Syst. Sci. Technol. 6(9), 44–56 (2005)

    Google Scholar 

  8. Oplatková, Z., Zelinka, I.: Investigation on artificial ant using analytic programming. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 949–950. ACM (2006)

    Google Scholar 

  9. Zelinka, I., Chen, G., Celikovsky, S.: Chaos synthesis by means of evolutionary algorithms. Int. J. Bifurcat. Chaos 18(04), 911–942 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  10. Senkerik, R., Oplatkova, Z., Zelinka, I., Davendra, D.: Synthesis of feedback controller for three selected chaotic systems by means of evolutionary techniques: analytic programming. Math. Comput. Model. 57(1–2), 57–67 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  11. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution - A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  12. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)

    Article  Google Scholar 

  13. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  14. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution–an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  15. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  16. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  17. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)

    Article  Google Scholar 

  18. Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009)

    Article  Google Scholar 

  19. Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Trans. Evol. Comput. 15(1), 32–54 (2011)

    Article  Google Scholar 

  20. Mallipeddi, R., Suganthan, P.N., Pan, Q.-K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)

    Article  Google Scholar 

  21. Brest, J., Korošec, P., Šilc, J., Zamuda, A., Bošković, B., Maučec, M.S.: Differential evolution and differential ant-stigmergy on dynamic optimisation problems. Int. J. Syst. Sci. 44(4), 663–679 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  22. Tanabe, R., Fukunaga, A.S.: Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665. IEEE (2014)

    Google Scholar 

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Acknowledgements

This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by project NPU I No. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, partially supported by Grant SGS 2017/134 of VSB-Technical University of Ostrava; and by Internal Grant Agency of Tomas Bata University under the projects No. IGA/Cebia-Tech/2017/004.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Roman Senkerik, Adam Viktorin, Michal Pluhacek & Tomas Kadavy

  2. Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. Listopadu 15, 708 33, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

Authors
  1. Roman Senkerik
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  2. Adam Viktorin
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  3. Michal Pluhacek
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  4. Tomas Kadavy
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  5. Ivan Zelinka
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Correspondence to Roman Senkerik .

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Editors and Affiliations

  1. University of La Rioja , Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  2. University of La Rioja , Logroño, La Rioja, Spain

    Rubén Urraca

  3. University of A Coruña , Ferrol, La Coruña, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Senkerik, R., Viktorin, A., Pluhacek, M., Kadavy, T., Zelinka, I. (2017). Hybridization of Analytic Programming and Differential Evolution for Time Series Prediction. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_58

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  • DOI: https://doi.org/10.1007/978-3-319-59650-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59649-5

  • Online ISBN: 978-3-319-59650-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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