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

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 serie...

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Vydáno v:	Progress in artificial intelligence Ročník 8; číslo 2; s. 253 - 262
Hlavní autoři:	Durán-Rosal, Antonio M., Gutiérrez, Pedro A., Salcedo-Sanz, Sancho, Hervás-Martínez, César
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2019 Springer Nature B.V
Témata:	Algorithms Approximation Artificial Intelligence Cardiology Computational Intelligence Computer Imaging Computer Science Control Coral reefs Data Mining and Knowledge Discovery Mathematical analysis Mechatronics Natural Language Processing (NLP) Oceanography Optimization Pattern Recognition and Graphics Regular Paper Robotics Time series Vision Coral reefs optimization Memetic algorithms Segmentation Time series size reduction Dynamic strategy
ISSN:	2192-6352, 2192-6360
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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.
AbstractList	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.
Author	Durán-Rosal, Antonio M. Gutiérrez, Pedro A. Salcedo-Sanz, Sancho Hervás-Martínez, César
Author_xml	– sequence: 1 givenname: Antonio M. surname: Durán-Rosal fullname: Durán-Rosal, Antonio M. email: i92duroa@uco.es organization: Department of Computer Science and Numerical Analysis, Rabanales Campus, University of Córdoba – sequence: 2 givenname: Pedro A. surname: Gutiérrez fullname: Gutiérrez, Pedro A. organization: Department of Computer Science and Numerical Analysis, Rabanales Campus, University of Córdoba – sequence: 3 givenname: Sancho surname: Salcedo-Sanz fullname: Salcedo-Sanz, Sancho organization: Department of Signal Processing and Communications, Universidad de Alcalá – sequence: 4 givenname: César surname: Hervás-Martínez fullname: Hervás-Martínez, César organization: Department of Computer Science and Numerical Analysis, Rabanales Campus, University of Córdoba
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References	Bermejo, Chica, Damas, Salcedo-Sanz, Cordón (CR1) 2018; 42 CR15 CR14 CR13 Martínez-Estudillo, Hervás-Martínez, Martínez-Estudillo, García-Pedrajas (CR17) 2005; 36 Salcedo-Sanz, Del Ser, Landa-Torres, Gil-López, Portilla-Figueras (CR24) 2014; 2014 Chung, Fu, Ng, Luk (CR6) 2004; 8 Nikolaou, Gutiérrez, Durán, Dicaire, Fernández-Navarro, Hervás-Martínez (CR19) 2015; 44 Zellner, Palm (CR31) 1974; 2 CR10 Chakrabarti, Keogh, Mehrotra, Pazzani (CR3) 2002; 27 Durán-Rosal, Hervás-Martínez, Tallón-Ballesteros, Martínez-Estudillo, Salcedo-Sanz (CR8) 2016; 117 Salcedo-Sanz (CR21) 2017; 6 Chatfield (CR4) 2016 Salcedo-Sanz, Pastor-Sánchez, Prieto, Blanco-Aguilera, García-Herrera (CR26) 2014; 87 Pérez-Ortiz, Durán-Rosal, Gutiérrez, Sánchez-Monedero, Nikolaou, Fernández-Navarro, Hervás Martínez (CR20) 2019; 326–327 Durán-Rosal, Gutiérrez, Salcedo-Sanz, Hervás-Martínez (CR11) 2018; 63 Yan, Ma, Luo, Patel (CR30) 2019; 184 Salcedo-Sanz, Sanchez-Garcia, Portilla-Figueras, Jimenez-Fernandez, Ahmadzadeh (CR27) 2014; 25 CR5 Demšar (CR7) 2006; 7 Salcedo-Sanz, Camacho-Gómez, Magdaleno, Pereira, Lorenzana (CR22) 2017; 393 Camacho-Gómez, Wang, Pereira, Díaz, Salcedo-Sanz (CR2) 2018; 157 Friedman (CR12) 1940; 11 Liao (CR16) 2005; 38 Salcedo-Sanz, García-Díaz, Portilla-Figueras, Ser, Gil-López (CR25) 2014; 24 Salotti (CR28) 2001; 22 CR23 Weiss (CR29) 2004; 6 Durán-Rosal, Gutiérrez, Martínez-Estudillo, Hérvas-Martínez (CR9) 2018; 442 Moody, Mark (CR18) 2001; 20 M Pérez-Ortiz (176_CR20) 2019; 326–327 C Camacho-Gómez (176_CR2) 2018; 157 A Durán-Rosal (176_CR8) 2016; 117 176_CR10 AC Martínez-Estudillo (176_CR17) 2005; 36 A Zellner (176_CR31) 1974; 2 C Chatfield (176_CR4) 2016 S Salcedo-Sanz (176_CR26) 2014; 87 GM Weiss (176_CR29) 2004; 6 TW Liao (176_CR16) 2005; 38 J Demšar (176_CR7) 2006; 7 FL Chung (176_CR6) 2004; 8 AM Durán-Rosal (176_CR9) 2018; 442 S Salcedo-Sanz (176_CR24) 2014; 2014 AM Durán-Rosal (176_CR11) 2018; 63 176_CR5 A Nikolaou (176_CR19) 2015; 44 K Chakrabarti (176_CR3) 2002; 27 S Salcedo-Sanz (176_CR22) 2017; 393 M Salotti (176_CR28) 2001; 22 S Salcedo-Sanz (176_CR27) 2014; 25 C Yan (176_CR30) 2019; 184 176_CR23 176_CR13 176_CR14 176_CR15 M Friedman (176_CR12) 1940; 11 G Moody (176_CR18) 2001; 20 E Bermejo (176_CR1) 2018; 42 S Salcedo-Sanz (176_CR21) 2017; 6 S Salcedo-Sanz (176_CR25) 2014; 24
References_xml	– volume: 6 start-page: 1 year: 2017 end-page: 15 ident: CR21 article-title: A review on the coral reefs optimization algorithm: new development lines and current applications publication-title: Prog. Artif. Intell. doi: 10.1007/s13748-016-0104-2 – volume: 2 start-page: 17 issue: 1 year: 1974 end-page: 54 ident: CR31 article-title: Time series analysis and simultaneous equation econometric models publication-title: J. Econom. doi: 10.1016/0304-4076(74)90028-1 – volume: 38 start-page: 1857 issue: 11 year: 2005 end-page: 1874 ident: CR16 article-title: Clustering of time series data—a survey publication-title: Pattern Recognit doi: 10.1016/j.patcog.2005.01.025 – ident: CR14 – volume: 22 start-page: 215 issue: 2 year: 2001 end-page: 221 ident: CR28 article-title: An efficient algorithm for the optimal polygonal approximation of digitized curves publication-title: Pattern Recognit. Lett. doi: 10.1016/S0167-8655(00)00088-X – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: CR7 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: J. Mach. Learn. Res. – volume: 326–327 start-page: 3 year: 2019 end-page: 14 ident: CR20 article-title: On the use of evolutionary time series analysis for segmenting paleoclimate data publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.11.101 – ident: CR10 – volume: 11 start-page: 86 issue: 1 year: 1940 end-page: 92 ident: CR12 article-title: A comparison of alternative tests of significance for the problem of m rankings publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177731944 – volume: 157 start-page: 14 year: 2018 end-page: 26 ident: CR2 article-title: Active vibration control design using the coral reefs optimization with substrate layer algorithm publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2017.12.002 – volume: 27 start-page: 188 issue: 2 year: 2002 end-page: 228 ident: CR3 article-title: Locally adaptive dimensionality reduction for indexing large time series databases publication-title: ACM Trans. Database Syst. (TODS) doi: 10.1145/568518.568520 – ident: CR23 – volume: 25 start-page: 1057 issue: 11 year: 2014 end-page: 1069 ident: CR27 article-title: A coral-reef optimization algorithm for the optimal service distribution problem in mobile radio access networks publication-title: Trans. Emerg. Telecommun. Technol. doi: 10.1002/ett.2759 – volume: 63 start-page: 139 year: 2018 end-page: 153 ident: CR11 article-title: A statistically-driven coral reef optimization algorithm for optimal size reduction of time series publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.11.037 – volume: 20 start-page: 45 issue: 3 year: 2001 end-page: 50 ident: CR18 article-title: The impact of the MIT-BIH arrhythmia database publication-title: IEEE Eng. Med. Biol. Mag. doi: 10.1109/51.932724 – volume: 44 start-page: 1919 issue: 7–8 year: 2015 end-page: 1933 ident: CR19 article-title: Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm publication-title: Clim. Dyn. doi: 10.1007/s00382-014-2405-0 – volume: 87 start-page: 10 year: 2014 end-page: 18 ident: CR26 article-title: Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization—extreme learning machine approach publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2014.06.041 – volume: 2014 start-page: 1 year: 2014 end-page: 15 ident: CR24 article-title: The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems publication-title: Sci. World J. – ident: CR15 – ident: CR13 – volume: 36 start-page: 534 issue: 3 year: 2005 end-page: 545 ident: CR17 article-title: Hybridization of evolutionary algorithms and local search by means of a clustering method publication-title: IEEE Trans. Syst. Man Cybern Part B (Cybernetics) doi: 10.1109/TSMCB.2005.860138 – volume: 24 start-page: 239 year: 2014 end-page: 248 ident: CR25 article-title: A coral reefs optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.07.007 – volume: 6 start-page: 7 issue: 1 year: 2004 end-page: 19 ident: CR29 article-title: Mining with rarity: a unifying framework publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1007730.1007734 – year: 2016 ident: CR4 publication-title: The Analysis of Time Series: An Introduction – volume: 442 start-page: 186 year: 2018 end-page: 201 ident: CR9 article-title: Simultaneous optimisation of clustering quality and approximation error for time series segmentation publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.02.041 – volume: 42 start-page: 138 year: 2018 end-page: 159 ident: CR1 article-title: Coral reef optimization with substrate layers for medical image registration publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2018.03.003 – ident: CR5 – volume: 393 start-page: 62 year: 2017 end-page: 75 ident: CR22 article-title: Structures vibration control via tuned mass dampers using a co-evolution coral reefs optimization algorithm publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2017.01.019 – volume: 8 start-page: 471 issue: 5 year: 2004 end-page: 489 ident: CR6 article-title: An evolutionary approach to pattern-based time series segmentation publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.832863 – volume: 184 start-page: 102 year: 2019 end-page: 111 ident: CR30 article-title: Hybrid binary coral reefs optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical datasets publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2018.11.010 – volume: 117 start-page: 292 year: 2016 end-page: 301 ident: CR8 article-title: Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2016.03.053 – volume: 11 start-page: 86 issue: 1 year: 1940 ident: 176_CR12 publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177731944 – volume: 7 start-page: 1 year: 2006 ident: 176_CR7 publication-title: J. Mach. Learn. Res. – volume: 6 start-page: 1 year: 2017 ident: 176_CR21 publication-title: Prog. Artif. Intell. doi: 10.1007/s13748-016-0104-2 – volume: 87 start-page: 10 year: 2014 ident: 176_CR26 publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2014.06.041 – volume: 2 start-page: 17 issue: 1 year: 1974 ident: 176_CR31 publication-title: J. Econom. doi: 10.1016/0304-4076(74)90028-1 – volume: 393 start-page: 62 year: 2017 ident: 176_CR22 publication-title: J. Sound Vib. doi: 10.1016/j.jsv.2017.01.019 – volume: 36 start-page: 534 issue: 3 year: 2005 ident: 176_CR17 publication-title: IEEE Trans. Syst. Man Cybern Part B (Cybernetics) doi: 10.1109/TSMCB.2005.860138 – ident: 176_CR15 doi: 10.1142/9789812565402_0001 – volume: 117 start-page: 292 year: 2016 ident: 176_CR8 publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2016.03.053 – ident: 176_CR5 – ident: 176_CR10 doi: 10.1007/978-3-030-00374-6_20 – volume: 27 start-page: 188 issue: 2 year: 2002 ident: 176_CR3 publication-title: ACM Trans. Database Syst. (TODS) doi: 10.1145/568518.568520 – volume: 442 start-page: 186 year: 2018 ident: 176_CR9 publication-title: Inf. Sci. doi: 10.1016/j.ins.2018.02.041 – volume: 20 start-page: 45 issue: 3 year: 2001 ident: 176_CR18 publication-title: IEEE Eng. Med. Biol. Mag. doi: 10.1109/51.932724 – volume: 38 start-page: 1857 issue: 11 year: 2005 ident: 176_CR16 publication-title: Pattern Recognit doi: 10.1016/j.patcog.2005.01.025 – ident: 176_CR23 doi: 10.1155/2014/739768 – volume: 63 start-page: 139 year: 2018 ident: 176_CR11 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2017.11.037 – volume: 22 start-page: 215 issue: 2 year: 2001 ident: 176_CR28 publication-title: Pattern Recognit. Lett. doi: 10.1016/S0167-8655(00)00088-X – volume: 8 start-page: 471 issue: 5 year: 2004 ident: 176_CR6 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2004.832863 – ident: 176_CR13 doi: 10.1007/3-540-44794-6_10 – volume: 157 start-page: 14 year: 2018 ident: 176_CR2 publication-title: Eng. Struct. doi: 10.1016/j.engstruct.2017.12.002 – volume: 25 start-page: 1057 issue: 11 year: 2014 ident: 176_CR27 publication-title: Trans. Emerg. Telecommun. Technol. doi: 10.1002/ett.2759 – volume: 42 start-page: 138 year: 2018 ident: 176_CR1 publication-title: Swarm Evol. Comput. doi: 10.1016/j.swevo.2018.03.003 – volume: 6 start-page: 7 issue: 1 year: 2004 ident: 176_CR29 publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1007730.1007734 – volume: 2014 start-page: 1 year: 2014 ident: 176_CR24 publication-title: Sci. World J. – volume: 24 start-page: 239 year: 2014 ident: 176_CR25 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.07.007 – volume: 44 start-page: 1919 issue: 7–8 year: 2015 ident: 176_CR19 publication-title: Clim. Dyn. doi: 10.1007/s00382-014-2405-0 – ident: 176_CR14 – volume-title: The Analysis of Time Series: An Introduction year: 2016 ident: 176_CR4 – volume: 326–327 start-page: 3 year: 2019 ident: 176_CR20 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.11.101 – volume: 184 start-page: 102 year: 2019 ident: 176_CR30 publication-title: Chemom. Intell. Lab. 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SubjectTerms	Algorithms Approximation Artificial Intelligence Cardiology Computational Intelligence Computer Imaging Computer Science Control Coral reefs Data Mining and Knowledge Discovery Mathematical analysis Mechatronics Natural Language Processing (NLP) Oceanography Optimization Pattern Recognition and Graphics Regular Paper Robotics Time series Vision
Title	Dynamical memetization in coral reef optimization algorithms for optimal time series approximation
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