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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 9783319590592
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 -
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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
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