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

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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 9780735415386
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
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