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Conference of the Spanish Association for Artificial Intelligence

CAEPIA 2018: Advances in Artificial Intelligence pp 209–218Cite as

An Empirical Validation of a New Memetic CRO Algorithm for the Approximation of Time Series

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

Abstract

The exponential increase of available temporal data encourages the development of new automatic techniques to reduce the number of points of time series. In this paper, we propose a novel modification of the coral reefs optimization algorithm (CRO) to reduce the size of the time series with the minimum error of approximation. During the evolution, the solutions are locally optimised and reintroduced in the optimization process. The hybridization is performed using two well-known state-of-the-art algorithms, namely Bottom-Up and Top-Down. The resulting algorithm, called memetic CRO (MCRO), is compared against standard CRO, its statistically driven version (SCRO) and their hybrid versions (HCRO and HSCRO, respectively). The methodology is tested in 15 time series collected from different sources, including financial problems, oceanography data, and cardiology signals, among others, showing that the best results are obtained by MCRO.

Supported by the projects TIN2017-85887-C2-1-P, TIN2017-85887-C2-2-P, TIN2014-54583-C2-1-R, TIN2014-54583-C2-2-R and TIN2015-70308-REDT of the Spanish Ministry of Economy and Competitiveness (MINECO), and FEDER funds (FEDER EU). Antonio M. Durán-Rosal’s research has been subsidised by the FPU Predoctoral Program of the Spanish Ministry of Education, Culture and Sport (MECD), grant reference FPU14/03039.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein Building, 14071, Córdoba, Spain

    Antonio Manuel Durán-Rosal, Pedro Antonio Gutiérrez & César Hervás-Martínez

  2. Department of Signal Processing and Communications, Universidad de Alcalá, Madrid, Spain

    Sancho Salcedo-Sanz

Authors
  1. Antonio Manuel Durán-Rosal
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  2. Pedro Antonio Gutiérrez
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  3. Sancho Salcedo-Sanz
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  4. César Hervás-Martínez
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Corresponding author

Correspondence to Antonio Manuel Durán-Rosal .

Editor information

Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

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Durán-Rosal, A.M., Gutiérrez, P.A., Salcedo-Sanz, S., Hervás-Martínez, C. (2018). An Empirical Validation of a New Memetic CRO Algorithm for the Approximation of Time Series. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-00374-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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