Different approaches for constant estimation in analytic programming

Komínková Oplatková, Zuzana; Viktorin, Adam; Šenkeřík, Roman; Urbánek, Tomáš

dc.title	Different approaches for constant estimation in analytic programming	en
dc.contributor.author	Komínková Oplatková, Zuzana
dc.contributor.author	Viktorin, Adam
dc.contributor.author	Šenkeřík, Roman
dc.contributor.author	Urbánek, Tomáš
dc.relation.ispartof	Proceedings - 31st European Conference on Modelling and Simulation, ECMS 2017
dc.identifier.isbn	978-0-9932440-4-9
dc.date.issued	2017
dc.citation.spage	326
dc.citation.epage	332
dc.event.title	31st European Conference on Modelling and Simulation, ECMS 2017
dc.event.location	Budapest
utb.event.state-en	Hungary
utb.event.state-cs	Maďarsko
dc.event.sdate	2017-05-23
dc.event.edate	2017-05-26
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	European Council for Modelling and Simulation
dc.identifier.doi	10.7148/2017-0326
dc.relation.uri	http://www.scs-europe.net/dlib/2017/2017-0326.htm
dc.relation.uri	http://www.scs-europe.net/dlib/2017/ecms2017acceptedpapers/0326-is_ECMS2017_0131.pdf
dc.subject	Analytic programming	en
dc.subject	Differential evolution	en
dc.subject	approximation	en
dc.subject	sextic	en
dc.subject	quintic	en
dc.description.abstract	This research deals with different approaches for constant estimation in analytic programming (AP). AP is a tool for symbolic regression tasks which enables to synthesise an analytical solution based on the required behaviour of the system. Some tasks do not need any constant estimation-AP is used in its basic version without any constant estimation handling. Compared to this, cases like data approximation need constants (coefficients) which are essential for the process of precise solution synthesis. This paper offers another strategy to already known and used by the AP from the very beginning and approaches published recently in 2016. This paper compares these procedures and the discussion also includes nonlinear fitting and metaevolutionary approach. As the main evolutionary algorithm, a differential algorithm (de/rand/1/bin) for the main process of AP is used. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors).	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1007248
utb.identifier.obdid	43877325
utb.identifier.scopus	2-s2.0-85021823730
utb.identifier.wok	000404420000049
utb.source	d-scopus
dc.date.accessioned	2017-09-03T21:40:05Z
dc.date.available	2017-09-03T21:40:05Z
dc.description.sponsorship	Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303, MSMT-7778/2014]; European Regional Development Fund under the project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Grant Agency of the Czech Republic - GACR [P103/15/06700S]; Internal Grant Agency of Tomas Bata University in Zlin [IGA/CebiaTech/2017/004]
utb.ou	CEBIA-Tech
utb.contributor.internalauthor	Komínková Oplatková, Zuzana
utb.contributor.internalauthor	Viktorin, Adam
utb.contributor.internalauthor	Šenkeřík, Roman
utb.contributor.internalauthor	Urbánek, Tomáš
utb.fulltext.affiliation	Zuzana Kominkova Oplatkova, Adam Viktorin, Roman Senkerik, Tomas Urbanek Tomas Bata University in Zlin, Faculty of Applied Informatics Nam T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {oplatkova, aviktorin, senkerik, turbanek}@fai.utb.cz
utb.fulltext.dates	-
utb.fulltext.references	Back T., Fogel D. B., Michalewicz Z., Handbook of evolutionary algorithms, Oxford University Press, 1997, ISBN 0750303921 Deugo D., Ferguson D.: Evolution to the xtreme: Evolving evolutionary strategies using a meta-level approach, Proceedings of the 2004 IEEE congress on evolutionary computation, IEEE Press, Portland, Oregon, pp. 31–38, 2004 Dioşan, L., Oltean, M.: Evolutionary design of evolutionary algorithms. Genetic Programming and Evolvable Machines, Vol. 10, Issue 3, p. 263-306, 2009 Edmonds, B.: Meta-genetic programming: Co-evolving the operators of variation, Elektrik, Vol. 9, Issue 1, pp. 13-29, 2001 Eiben A.E., Michalewicz Z., Schoenauer M., Smith J.E.: Parameter control in evolutionary algorithms, pp. 19–46, Springer, 2007 Jones D.F., Mirrazavi S.K., Tamiz M.: Multi-objective metaheuristics: An overview of the current state-of-the-art, European Journal of Operational Research, Volume 137, Issue 1, 16 February 2002, Pages 1-9, ISSN 0377-2217. Kominkova Oplatkova Z., Senkerik R. (2013): Evolutionary Synthesis of Complex Structures - Pseudo Neural Networks for the Task of Iris Dataset Classification. In Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Heidelberg : Springer-Verlag Berlin, 2013, p. 211-220. ISSN 2194-5357. ISBN 978-3-319-00541-6. Kominkova Oplatkova Z., Senkerik R. (2016): Control Law and Pseudo Neural Networks Synthesized by Evolutionary Symbolic Regression Technique, in Al-Begain K., Bargiela A.: Seminal Contributions to Modelling and Simulation - Part of the series Simulation Foundations, Methods and Applications, pp 91-113, doi: 10.1007/978-3-319-33786-9_9, ISBN: 978-3-319-33785-2. Kordík P., Koutník J., Drchal J., Kovářík O., Čepek M., Šnorek M.: Meta-learning approach to neural network optimization, Neural Networks, Vol. 23, Issue 4, p. 568-582, 2010, ISSN 0893-6080. Koza J. R. et al., Genetic Programming III; Darwinian Invention and problem Solving, Morgan Kaufmann Publisher, 1999, ISBN 1-55860-543-6 Koza J. R., Genetic Programming, MIT Press, 1998, ISBN 0-262-11189-6 Lampinen J., Zelinka I., 1999, “New Ideas in Optimization – Mechanical Engineering Design Optimization by Differential Evolution”, Volume 1, London: McGraw-hill, 1999, 20 p., ISBN 007-709506-5. O’Neill M., Ryan C., Grammatical Evolution. Evolutionary Automatic Programming in an Arbitrary Language, Kluwer Academic Publishers, 2003, ISBN 1402074441 Oplatkova Z.: Metaevolution: Synthesis of Optimization Algorithms by means of Symbolic Regression and Evolutionary Algorithms, Lambert Academic Publishing Saarbrücken, 2009, ISBN: 978-3-8383-1808-0 Price K., Storn R. M., Lampinen J. A., 2005, “Differential Evolution : A Practical Approach to Global Optimization”, (Natural Computing Series), Springer; 1 edition. Price, K. and Storn, R. (2001), Differential evolution homepage, [Online]: http://www.icsi.berkeley.edu/~storn/code.html, [Accessed 29/02/2012]. Urbanek T., Prokopova Z., Silhavy R., Kuncar A.: New Approach of Constant Resolving of Analytical Programming. In 30th European Conference on Modelling and Simulation, 2016, p. 231-236. ISBN 978-0-9932440-2-5. Viktorin A., Pluhacek M., Kominkova Oplatkova Z., Senkerik R.: Analytical Programming with Extended Individuals. In 30th European Conference on Modelling and Simulation, 2016, p. 237-244. ISBN 978-0-9932440-2-5. Volna, E., Kotyrba, M., & Jarusek, R. (2013). Multi-classifier based on Elliott wave’s recognition. Computers & Mathematics with Applications, 66(2), 213-225. Zelinka et al.: Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures, in Kita E.: Evolutionary Algorithms, InTech 2011, ISBN: 978-953-307-171-8 Zelinka I., Varacha P., Oplatkova Z., Evolutionary Synthesis of Neural Network, Mendel 2006 – 12th International Conference on Softcomputing, Brno, Czech Republic, 31 May – 2 June 2006, pages 25 – 31, ISBN 80-214-3195-4 Zelinka I.,Oplatkova Z, Nolle L., 2005. Boolean Symmetry Function Synthesis by Means of Arbitrary Evolutionary Algorithms-Comparative Study, International Journal of Simulation Systems, Science and Technology, Volume 6, Number 9, August 2005, pages 44 - 56, ISSN: 1473-8031.
utb.fulltext.sponsorship	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, further it was supported by Grant Agency of the Czech Republic—GACR P103/15/06700S and by Internal Grant Agency of Tomas Bata University in Zlin under the project No. IGA/CebiaTech/2017/004.
utb.scopus.affiliation	Tomas Bata University in Zlin, Faculty of Applied Informatics, Nam T.G. Masaryka 5555, Zlin, Czech Republic
utb.fulltext.projects	LO1303
utb.fulltext.projects	MSMT-7778/2014
utb.fulltext.projects	CZ.1.05/2.1.00/03.0089
utb.fulltext.projects	P103/15/06700S
utb.fulltext.projects	IGA/CebiaTech/2017/004