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Dynamical memetization in coral reef optimization algorithms for optimal time series approximation

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Abstract

The huge amount of data chronologically collected in short periods of time by different devices and technologies is an important challenge in the analysis of times series. This problem has produced the development of new automatic techniques to reduce the number of points in the resulting time series, in order to facilitate their processing and analysis. In this paper, we propose a new modification of a coral reefs optimization algorithm (CRO) to tackle the problem of reducing the size of the time series minimizing the approximation error. The modification includes a memetization procedure (hybridization with a local search procedure) of the standard algorithm to improve its quality when finding a promising solution in a given searching area. The memetization process is applied to the worse individuals of the algorithm at the beginning, and only to the best ones at the end of the algorithm’s convergence, resulting in a dynamical search approach called dynamic memetic CRO (DMCRO). The proposed DMCRO performance is compared in this paper against other state-of-the-art CRO algorithms, such as the standard one, its statistically driven version (SCRO) and two different hybrid versions (HCRO and HSCRO, respectively), and the standard memetic version (MCRO). All the algorithms compared have been tested in 15 time series approximation, collected from different sources, including financial problems, oceanography data, and cardiology signals, among others, showing that the best results are obtained by DMCRO.

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References

  1. Bermejo, E., Chica, M., Damas, S., Salcedo-Sanz, S., Cordón, O.: Coral reef optimization with substrate layers for medical image registration. Swarm Evol. Comput. 42, 138–159 (2018)

    Article  Google Scholar 

  2. Camacho-Gómez, C., Wang, X., Pereira, E., Díaz, I., Salcedo-Sanz, S.: Active vibration control design using the coral reefs optimization with substrate layer algorithm. Eng. Struct. 157, 14–26 (2018)

    Article  Google Scholar 

  3. Chakrabarti, K., Keogh, E., Mehrotra, S., Pazzani, M.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. (TODS) 27(2), 188–228 (2002)

    Article  Google Scholar 

  4. Chatfield, C.: The Analysis of Time Series: An Introduction. CRC Press, Boca Raton (2016)

    MATH  Google Scholar 

  5. Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The ucr time series classification archive (2015). www.cs.ucr.edu/~eamonn/time_series_data/. Accessed 3 July 2018

  6. Chung, F.L., Fu, T.C., Ng, V., Luk, R.W.: An evolutionary approach to pattern-based time series segmentation. IEEE Trans. Evol. Comput. 8(5), 471–489 (2004)

    Article  Google Scholar 

  7. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  8. Durán-Rosal, A., Hervás-Martínez, C., Tallón-Ballesteros, A., Martínez-Estudillo, A., Salcedo-Sanz, S.: Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks. Ocean Eng. 117, 292–301 (2016)

    Article  Google Scholar 

  9. Durán-Rosal, A.M., Gutiérrez, P.A., Martínez-Estudillo, F.J., Hérvas-Martínez, C.: Simultaneous optimisation of clustering quality and approximation error for time series segmentation. Inf. Sci. 442, 186–201 (2018)

    Article  MathSciNet  Google Scholar 

  10. Durán-Rosal, A.M., Gutiérrez, P.A., Salcedo-Sanz, S., Hervás-Martínez, C.: An empirical validation of a new memetic CRO algorithm for the approximation of time series. In: Conference of the Spanish Association for Artificial Intelligence, pp. 209–218. Springer (2018)

  11. Durán-Rosal, A.M., Gutiérrez, P.A., Salcedo-Sanz, S., Hervás-Martínez, C.: A statistically-driven coral reef optimization algorithm for optimal size reduction of time series. Appl. Soft Comput. 63, 139–153 (2018)

    Article  Google Scholar 

  12. Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)

    Article  MathSciNet  MATH  Google Scholar 

  13. Geurts, P.: Pattern extraction for time series classification. In: European Conference on Principles of Data Mining and Knowledge Discovery, pp. 115–127. Springer (2001)

  14. National buoy data center. National Oceanic and Atmospheric Administration of the USA (NOAA) (2015). http://www.ndbc.noaa.gov/. Accessed 3 July 2018

  15. Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: a survey and novel approach. In: Data Mining in Time Series Databases, pp. 1–21 (2004)

  16. Liao, T.W.: Clustering of time series data—a survey. Pattern Recognit 38(11), 1857–1874 (2005)

    Article  MATH  Google Scholar 

  17. Martínez-Estudillo, A.C., Hervás-Martínez, C., Martínez-Estudillo, F.J., García-Pedrajas, N.: Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Trans. Syst. Man Cybern Part B (Cybernetics) 36(3), 534–545 (2005)

    Article  MATH  Google Scholar 

  18. Moody, G., Mark, R.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45–50 (2001)

    Article  Google Scholar 

  19. Nikolaou, A., Gutiérrez, P.A., Durán, A., Dicaire, I., Fernández-Navarro, F., Hervás-Martínez, C.: Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm. Clim. Dyn. 44(7–8), 1919–1933 (2015)

    Article  Google Scholar 

  20. Pérez-Ortiz, M., Durán-Rosal, A.M., Gutiérrez, P., Sánchez-Monedero, J., Nikolaou, A., Fernández-Navarro, F., Hervás Martínez, C.: On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing 326–327, 3–14 (2019)

    Article  Google Scholar 

  21. Salcedo-Sanz, S.: A review on the coral reefs optimization algorithm: new development lines and current applications. Prog. Artif. Intell. 6, 1–15 (2017)

    Article  Google Scholar 

  22. Salcedo-Sanz, S., Camacho-Gómez, C., Magdaleno, A., Pereira, E., Lorenzana, A.: Structures vibration control via tuned mass dampers using a co-evolution coral reefs optimization algorithm. J. Sound Vib. 393, 62–75 (2017)

    Article  Google Scholar 

  23. Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., Portilla-Figueras, A.: The coral reefs optimization algorithm: an efficient meta-heuristic for solving hard optimization problems. In: Proceedings of the 15th International Conference on Applied Stochastic Models and Data Analysis (ASMDA2013), Mataró, pp. 751–758 (2013)

  24. Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., Portilla-Figueras, J.: The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. Sci. World J. 2014, 1–15 (2014)

    Google Scholar 

  25. Salcedo-Sanz, S., García-Díaz, P., Portilla-Figueras, J., Ser, J.D., Gil-López, S.: A coral reefs optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion. Appl. Soft Comput. 24, 239–248 (2014)

    Article  Google Scholar 

  26. Salcedo-Sanz, S., Pastor-Sánchez, A., Prieto, L., Blanco-Aguilera, A., García-Herrera, R.: Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization—extreme learning machine approach. Energy Convers. Manag. 87, 10–18 (2014)

    Article  Google Scholar 

  27. Salcedo-Sanz, S., Sanchez-Garcia, J.E., Portilla-Figueras, J.A., Jimenez-Fernandez, S., Ahmadzadeh, A.M.: A coral-reef optimization algorithm for the optimal service distribution problem in mobile radio access networks. Trans. Emerg. Telecommun. Technol. 25(11), 1057–1069 (2014)

    Article  Google Scholar 

  28. Salotti, M.: An efficient algorithm for the optimal polygonal approximation of digitized curves. Pattern Recognit. Lett. 22(2), 215–221 (2001)

    Article  MATH  Google Scholar 

  29. Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explor. Newslett. 6(1), 7–19 (2004)

    Article  Google Scholar 

  30. Yan, C., Ma, J., Luo, H., Patel, A.: Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets. Chemom. Intell. Lab. Syst. 184, 102–111 (2019)

    Article  Google Scholar 

  31. Zellner, A., Palm, F.: Time series analysis and simultaneous equation econometric models. J. Econom. 2(1), 17–54 (1974)

    Article  MATH  Google Scholar 

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Acknowledgements

This work has been subsidized by the projects TIN2017-85887-C2-1-P, TIN2017-85887-C2-2-P and TIN2017-90567-REDT of the Spanish Ministry of Economy and Competitiveness (MINECO), and FEDER funds (FEDER EU). Antonio M. Durán-Rosal’s research has been subsidized by the FPU Predoctoral Program of the Spanish Ministry of Education, Culture and Sport (MECD), Grant reference FPU14/03039.

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Correspondence to Antonio M. Durán-Rosal.

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Durán-Rosal, A.M., Gutiérrez, P.A., Salcedo-Sanz, S. et al. Dynamical memetization in coral reef optimization algorithms for optimal time series approximation. Prog Artif Intell 8, 253–262 (2019). https://doi.org/10.1007/s13748-019-00176-0

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