Synthetic objective function to improve the performance of DE - Initial study

Viktorin, Adam; Pluháček, Michal; Šenkeřík, Roman

dc.title	Synthetic objective function to improve the performance of DE - Initial study	en
dc.contributor.author	Viktorin, Adam
dc.contributor.author	Pluháček, Michal
dc.contributor.author	Šenkeřík, Roman
dc.relation.ispartof	AIP Conference Proceedings
dc.identifier.issn	0094-243X Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn	978-0-7354-1538-6
dc.date.issued	2017
utb.relation.volume	1863
dc.event.title	International Conference of Numerical Analysis and Applied Mathematics 2016, ICNAAM 2016
dc.event.location	Rhodes
utb.event.state-en	Greece
utb.event.state-cs	Řecko
dc.event.sdate	2016-09-19
dc.event.edate	2016-09-25
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	American Institute of Physics (AIP)
dc.identifier.doi	10.1063/1.4992255
dc.relation.uri	http://aip.scitation.org/doi/abs/10.1063/1.4992255
dc.description.abstract	In this initial study, the idea of synthesizing objective function during the evolution process is tested for the improvement of optimization performance of Differential Evolution (DE) algorithm. Since many of the real world problems require computationally expensive simulations there is a demand for specialized optimization algorithms to solve them in as few objective function evaluations as possible. This paper proposes a new approach which combines DE with Analytical Programming (AP), a symbolic regression tool used for the synthesis of objective function in order to adapt the control parameter settings during evolution. © 2017 Author(s).	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1007297
utb.identifier.obdid	43877047
utb.identifier.scopus	2-s2.0-85026632403
utb.identifier.wok	000410159800105
utb.source	d-scopus
dc.date.accessioned	2017-09-03T21:40:09Z
dc.date.available	2017-09-03T21:40:09Z
dc.description.sponsorship	ERDF, European Regional Development Fund
dc.description.sponsorship	Grant Agency of the Czech Republic - GACR [P103/15/06700S]; 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]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2016/007]
utb.contributor.internalauthor	Viktorin, Adam
utb.contributor.internalauthor	Pluháček, Michal
utb.contributor.internalauthor	Šenkeřík, Roman
utb.fulltext.affiliation	Adam Viktorin 1, a) , Michal Pluhacek 1, b) and Roman Senkerik 1, c) 1 Tomas Bata University in Zlin, Faculty of Applied Informatics T. G. Masaryka 5555, 760 01 Zlin, CZECH REPUBLIC a) Corresponding author: aviktorin@fai.utb.cz b) pluhacek@fai.utb.cz c) senkerik@fai.utb.cz
utb.fulltext.dates	-
utb.fulltext.references	1. Storn, R., & Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces (Vol. 3). Berkeley: ICSI. 2. Brest, J., Greiner, S., Bošković, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. Evolutionary Computation, IEEE Transactions on, 10(6), 646-657. 3. Omran, M. G., Salman, A., & Engelbrecht, A. P. (2005). Self-adaptive differential evolution. In Computational intelligence and security (pp. 192-199). Springer Berlin Heidelberg. 4. Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization.Evolutionary Computation, IEEE Transactions on, 13(2), 398-417. 5. Zhang, J., & Sanderson, A. C. (2009). JADE: adaptive differential evolution with optional external archive. Evolutionary Computation, IEEE Transactions on, 13(5), 945-958. 6. Tanabe, R., & Fukunaga, A. (2013, June). Success-history based parameter adaptation for differential evolution. In Evolutionary Computation (CEC), 2013 IEEE Congress on (pp. 71-78). IEEE. 7. Zelinka, I. (2001). Analytic programming by means of new evolutionary algorithms, Proceedings of 1 st International Conference on New Trends in Physics’01, Brno, Czech Republic, pp. 210-214. 8. Zelinka, I., & Oplatkova, Z. (2003). Analytic programming – comparative study, Proceedings of Second International Conference on Computational Intelligence, Robotics, and Autonomous Systems, Singapore. 9. Koza, J. R. (1990). Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Stanford University, Department of Computer Science. 10. Schmidt, M., & Lipson, H. (2009). Distilling free-form natural laws from experimental data. science, 324(5923), 81-85. The software is currently available from: http://nutonian.com 11. Viktorin, A., Pluhacek, M., & Senkerik, R. (2016). Lozi Map Generated Initial Population in Analytical Programming. In Artificial Intelligence Perspectives in Intelligent Systems (pp. 297-306). Springer International Publishing.
utb.fulltext.sponsorship	This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2016/007.
utb.scopus.affiliation	Tomas Bata University in Zlin, Faculty of Applied Informatics, T. G. Masaryka 5555, Zlin, Czech Republic