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Comparison of three novelty approaches to constants (Ks) handling in analytic programming powered by SHADE
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| dc.title | Comparison of three novelty approaches to constants (Ks) handling in analytic programming powered by SHADE | en |
| dc.contributor.author | Komínková Oplatková, Zuzana | |
| dc.contributor.author | Viktorin, Adam | |
| dc.contributor.author | Šenkeřík, Roman | |
| dc.relation.ispartof | Advances in Intelligent Systems and Computing | |
| dc.identifier.issn | 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR | |
| dc.identifier.isbn | 978-3-319-97887-1 | |
| dc.date.issued | 2019 | |
| utb.relation.volume | 837 | |
| dc.citation.spage | 134 | |
| dc.citation.epage | 145 | |
| dc.event.title | 23rd International Conference on Soft Computing, MENDEL 2017 | |
| dc.event.location | Brno | |
| utb.event.state-en | Czech Republic | |
| utb.event.state-cs | Česká republika | |
| dc.event.sdate | 2017-06-20 | |
| dc.event.edate | 2017-06-22 | |
| dc.type | conferenceObject | |
| dc.language.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.identifier.doi | 10.1007/978-3-319-97888-8_12 | |
| dc.relation.uri | https://link.springer.com/chapter/10.1007/978-3-319-97888-8_12 | |
| dc.subject | analytic programming | en |
| dc.subject | constant handling | en |
| dc.subject | SHADE | en |
| dc.description.abstract | This research deals with the comparison of three novelty approaches for constant estimation in analytic programming (AP) powered by Success-history based Differential evolution (SHADE). AP is a tool for symbolic regression tasks which enables to synthesise an analytical solution based on the required behaviour of the system. 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 in the version SHADE for the main process of AP is used. The proposed comparison is performed out on quintic, sextic, Sine 3 and Sine 4 benchmark problems. © Springer Nature Switzerland AG 2019. | en |
| utb.faculty | Faculty of Applied Informatics | |
| dc.identifier.uri | http://hdl.handle.net/10563/1008171 | |
| utb.identifier.obdid | 43880127 | |
| utb.identifier.scopus | 2-s2.0-85051832939 | |
| utb.source | d-scopus | |
| dc.date.accessioned | 2018-08-30T13:31:25Z | |
| dc.date.available | 2018-08-30T13:31:25Z | |
| utb.ou | CEBIA-Tech | |
| utb.contributor.internalauthor | Komínková Oplatková, Zuzana | |
| utb.contributor.internalauthor | Viktorin, Adam | |
| utb.contributor.internalauthor | Šenkeřík, Roman | |
| utb.fulltext.affiliation | Zuzana Kominkova Oplatkova (✉) , Adam Viktorin, and Roman Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam. T. G. Masaryka 5555, 76001 Zlin, Czech Republic {oplatkova,aviktorin,senkerik}@fai.utb.cz | |
| utb.fulltext.dates | - | |
| utb.fulltext.references | 1. Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Algorithms. Oxford University Press, Oxford (1997). ISBN 0750303921 2. Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith J.E.: Parameter control in evolutionary algorithms, pp. 19–46. Springer (2007) 3. Oplatkova, Z.K., Senkerik, R.: 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, pp. 211–220. Springer, Heidelberg (2013). ISSN 2194-5357, 978-3-319-00541-6 4. Oplatkova, Z.K., Senkerik, R.: 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 (2016). https://doi.org/10.1007/978-3-319-33786-9_9. ISBN 978-3-319-33785-2 5. Oplatkova, Z.K., Viktorin, A., Senkerik, R., Urbanek, T.: Different approaches for constant estimation in analytic programming. In: 31st European Conference on Modelling and Simulation, pp. 326–332 (2017). ISBN 978-0-9932440-4-9. ISSN 2522-2422 6. Koza, J.R., et al.: Genetic Programming III: Darwinian Invention and problem Solving. Morgan Kaufmann Publisher, Burlington (1999). ISBN 1-55860-543-6 7. Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1998). ISBN 0-262-11189-6 8. Lampinen, J., Zelinka, I.: New ideas in optimization – mechanical engineering design optimization by differential evolution, vol. 1. McGraw-hill, London (1999). 20 p, ISBN 007-709506-5 9. O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003). ISBN 1402074441 10. Oplatkova, Z.: Metaevolution: Synthesis of Optimization Algorithms by means of Symbolic Regression and Evolutionary Algorithms. Lambert Academic Publishing, Saarbrücken (2009) 11. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series, 1st edn. Springer, Heidelberg (2005) 12. Price, K., Storn, R.: Differential evolution homepage (2001). http://www.icsi.berkeley.edu/~storn/code.html. Accessed 29 Feb 2012 13. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 71–78. IEEE (2013) 14. Urbanek, T., Prokopova, Z., Silhavy, R., Kuncar, A.: New approach of constant resolving of analytical programming. In: 30th European Conference on Modelling and Simulation, pp. 231–236 (2016). ISBN 978-0-9932440-2-5 15. Viktorin, A., Pluhacek, M., Oplatkova, Z.K., Senkerik, R.: Analytical programming with extended individuals. In: 30th European Conference on Modelling and Simulation, pp. 237–244 (2016). ISBN 978-0-9932440-2-5 16. Volna, E., Kotyrba, M., Jarusek, R.: Multi-classifier based on Elliott wave’s recognition. Comput. Math. Appl. 66(2), 213–225 (2013) 17. 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 18. Zelinka, I., Varacha, P., Oplatkova, Z.: Evolutionary synthesis of neural network. In: Mendel 2006 – 12th International Conference on Softcomputing, Brno, Czech Republic, 31 May – 2 June 2006, pp. 25– 31 (2006). ISBN 80-214-3195-4 19. 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 | |
| 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 | Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam. T. G. Masaryka 5555, Zlin, Czech Republic | |
| utb.fulltext.projects | LO1303 (MSMT-7778/2014) | |
| utb.fulltext.projects | CZ.1.05/2.1.00/03.0089 | |
| utb.fulltext.projects | GACR P103/15/06700S | |
| utb.fulltext.projects | IGA/CebiaTech/2017/004 |
