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Archive analysis in SHADE

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dc.title Archive analysis in SHADE en
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
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 688
dc.citation.epage 699
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_62
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-59060-8_62
dc.subject Archive en
dc.subject Differential evolution en
dc.subject SHADE en
dc.description.abstract The aim of this research paper is to analyze the current optional archive in Success-History based Adaptive Differential Evolution (SHADE) which is used during mutation. The usefulness of the archive is analyzed on CEC 2015 benchmark set of test functions where the impact of successful archive use on final test function value is studied. This paper also proposes a new version of optional archive named Enhanced Archive (EA), which is also tested on CEC 2015 benchmark set and the results are compared with the canonical version. Two research questions are discussed: Whether SHADE with EA has better performance than canonical SHADE and whether it makes a better use of the archive. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007182
utb.identifier.obdid 43877053
utb.identifier.scopus 2-s2.0-85020923437
utb.identifier.wok 000426206100062
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]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2017/004]
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.affiliation Adam Viktorin ( * ) , Roman Senkerik, Michal Pluhacek, and Tomas Kadavy Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {aviktorin,senkerik,pluhacek,kadavy}@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references 1. Storn, R., Price, K.: Differential Evolution-a Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, vol. 3. ICSI, Berkeley (1995) 2. Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010) 3. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011) 4. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution-an updated survey. Swarm Evol. Comput. 27, 1–30 (2016) 5. Brest, J., Greiner, S., Bošković, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical Benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006) 6. Omran, M.G.H., Salman, A., Engelbrecht, A.P.: Self-adaptive differential evolution. In: Hao, Y., Liu, J., Wang, Y., Cheung, Y., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS, vol. 3801, pp. 192–199. Springer, Heidelberg (2005). doi:10.1007/11596448 28 7. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009) 8. Islam, S.M., Das, S., Ghosh, S., Roy, S., Suganthan, P.N.: An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(2), 482–500 (2012) 9. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009) 10. Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 71–78. IEEE, June 2013 11. Liang, J.J., Qu, B.Y., Suganthan, P.N., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on realparameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical report, 201212 (2013) 12. Tanabe, R., Fukunaga, A.S.: Improving the search performance of SHADE using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1658–1665. IEEE, July 2014 13. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore (2013) 14. Liang, J.J., Qu, B.Y., Suganthan, P.N., Chen, Q.: Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical report 201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical report, Nanyang Technological University, Singapore (2014)
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/2017/004.
utb.wos.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, Zlin, Czech Republic
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