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Differential evolution driven analytic programming for prediction
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| dc.title | Differential evolution driven analytic programming for prediction | en |
| dc.contributor.author | Šenkeřík, Roman | |
| dc.contributor.author | Viktorin, Adam | |
| dc.contributor.author | Pluháček, Michal | |
| dc.contributor.author | Kadavý, Tomáš | |
| dc.contributor.author | Zelinka, Ivan | |
| dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
| dc.identifier.issn | 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR | |
| dc.identifier.isbn | 978-3-319-59059-2 | |
| dc.date.issued | 2017 | |
| utb.relation.volume | 10246 LNAI | |
| dc.citation.spage | 676 | |
| dc.citation.epage | 687 | |
| dc.event.title | 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 | |
| dc.event.location | Zakopane | |
| utb.event.state-en | Poland | |
| utb.event.state-cs | Polsko | |
| dc.event.sdate | 2017-06-11 | |
| dc.event.edate | 2017-06-15 | |
| dc.type | conferenceObject | |
| dc.language.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.identifier.doi | 10.1007/978-3-319-59060-8_61 | |
| dc.relation.uri | https://link.springer.com/chapter/10.1007/978-3-319-59060-8_61 | |
| dc.subject | Analytic programming | en |
| dc.subject | Differential evolution | en |
| dc.subject | SHADE | en |
| dc.subject | Time series prediction | en |
| dc.description.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 connection between AP and different strategies of DE. AP can be considered as powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). Simple experiment has been carried out here with the time series consisting of 300 data-points 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. © Springer International Publishing AG 2017. | en |
| utb.faculty | Faculty of Applied Informatics | |
| dc.identifier.uri | http://hdl.handle.net/10563/1007185 | |
| utb.identifier.obdid | 43877229 | |
| utb.identifier.scopus | 2-s2.0-85020884828 | |
| utb.identifier.wok | 000426206100061 | |
| utb.source | d-scopus | |
| dc.date.accessioned | 2017-09-03T21:39:57Z | |
| dc.date.available | 2017-09-03T21:39:57Z | |
| dc.description.sponsorship | Grant Agency of the Czech Republic - GACR [P103/15/06700S]; Ministry of Education, Youth and Sports of the Czech Republic within National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; VSB-Technical University of Ostrava [SGS 2017/134]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2017/004] | |
| utb.contributor.internalauthor | Šenkeřík, Roman | |
| utb.contributor.internalauthor | Viktorin, Adam | |
| utb.contributor.internalauthor | Pluháček, Michal | |
| utb.contributor.internalauthor | Kadavý, Tomáš | |
| utb.fulltext.affiliation | Roman Senkerik 1( B ) , Adam Viktorin 1 , Michal Pluhacek 1 , Tomas Kadavy 1 , and Ivan Zelinka 2 1 Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {senkerik,aviktorin,pluhacek,kadavy}@fai.utb.cz 2 Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic ivan.zelinka@vsb.cz | |
| utb.fulltext.dates | - | |
| utb.fulltext.references | 1. Zelinka, I., et al.: Analytical programming - a novel approach for evolutionary synthesis of symbolic structures. In: Kita, E. (ed.) Evolutionary Algorithms, InTech 2011, ISBN 978-953-307-171-8 2. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces’. J. Global Optim. 11, 341–359 (1997) 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) 4. Santini, M., Tettamanzi, A.: Genetic programming for financial time series prediction. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tettamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 361–370. Springer, Heidelberg (2001). doi:10.1007/3-540-45355-5 29 5. Palit, A.K., Popovic, D.: Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer Science & Business Media, London (2006) 6. Koza, J.R.: Genetic Programming. MIT Press, New York (1998) 7. Zelinka, I., Oplatkova, 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). ISSN 1473-8031 8. Oplatkova, 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) 9. Zelinka, I., Chen, G., Celikovsky, S.: Chaos synthesis by means of evolutionary algorithms. Int. J. Bifurcat. Chaos 18(4), 911–942 (2008) 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. Modell. 57(1), 57–67 (2013) 11. Price, K.V., Storn, R.M., Lampinen, J.A., Evolution, D.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005) 12. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010) 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) 14. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution - an updated survey. Swarm Evol. Comput. 27, 1–30 (2016) 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) 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) 17. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009) 18. Das, S., Abraham, A., Chakraborty, U., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009) 19. Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Trans. Evol. Comput. 15(1), 32–54 (2011) 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) 21. Brest, J., Korosec, P., Silc, J., Zamuda, A., Boskovic, B., Maucec, M.S.: Differential evolution and differential ant-stigmergy on dynamic opti- misation problems. Int. J. Syst. Sci. 44(4), 663–679 (2013) 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) | |
| utb.fulltext.sponsorship | This work was supported by Grant Agency of the Czech Republic - GACR P103/15/06700S, further by This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project no. LO1303 (MSMT-7778/2014) 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 project no. IGA/CebiaTech/2017/004. | |
| utb.wos.affiliation | Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, Czech Republic; Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17 listopadu 15, Poruba, Ostrava, Czech Republic |
