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Detecting potential design weaknesses in shade through network feature analysis
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| dc.title | Detecting potential design weaknesses in shade through network feature analysis | en |
| dc.contributor.author | Viktorin, Adam | |
| dc.contributor.author | Pluháček, Michal | |
| dc.contributor.author | Šenkeřík, Roman | |
| 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 | 978-3-319-59650-1 | |
| dc.identifier.isbn | 978-3-319-59649-5 | |
| dc.date.issued | 2017 | |
| utb.relation.volume | 10334 LNCS | |
| dc.citation.spage | 662 | |
| dc.citation.epage | 673 | |
| dc.event.title | 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017 | |
| dc.event.location | Logroño (La Rioja) | |
| utb.event.state-en | Spain | |
| utb.event.state-cs | Španělsko | |
| dc.event.sdate | 2017-06-21 | |
| dc.event.edate | 2017-06-23 | |
| dc.type | conferenceObject | |
| dc.language.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.identifier.doi | 10.1007/978-3-319-59650-1_56 | |
| dc.relation.uri | https://link.springer.com/chapter/10.1007/978-3-319-59650-1_56 | |
| dc.subject | Centrality | en |
| dc.subject | Complex network | en |
| dc.subject | Differential evolution | en |
| dc.subject | SHADE | en |
| dc.description.abstract | This preliminary study presents a hybridization of two research fields – evolutionary algorithms and complex networks. A network is created by the dynamic of an evolutionary algorithm, namely Success-History based Adaptive Differential Evolution (SHADE). Network feature, node degree centrality, is used afterward to detect potential design weaknesses of SHADE algorithm. This approach is experimentally tested on the CEC2015 benchmark set of test functions and future directions in the research are proposed. © Springer International Publishing AG 2017. | en |
| utb.faculty | Faculty of Applied Informatics | |
| dc.identifier.uri | http://hdl.handle.net/10563/1007261 | |
| utb.identifier.obdid | 43877050 | |
| utb.identifier.scopus | 2-s2.0-85021714373 | |
| utb.identifier.wok | 000432880600056 | |
| utb.source | d-scopus | |
| dc.date.accessioned | 2017-09-03T21:40:06Z | |
| dc.date.available | 2017-09-03T21:40:06Z | |
| dc.description.sponsorship | GACR P103/15/06700S, GACR;GAČR, Grantová Agentura České Republiky | |
| 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 Project [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/2017/004] | |
| utb.contributor.internalauthor | Viktorin, Adam | |
| utb.contributor.internalauthor | Pluháček, Michal | |
| utb.contributor.internalauthor | Šenkeřík, Roman | |
| utb.contributor.internalauthor | Kadavý, Tomáš | |
| utb.fulltext.affiliation | Adam Viktorin, Michal Pluhacek, Roman Senkerik, and Tomas Kadavy (&) Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {aviktorin,pluhacek,senkerik,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. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997) 6. Omran, M.G.H., Salman, A., Engelbrecht, Andries P.: Self-adaptive differential evolution. In: Hao, Y., et al. (eds.) CIS 2005. LNCS, vol. 3801, pp. 192–199. Springer, Heidelberg (2005). doi:10.1007/11596448_28 7. 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) 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. Systems Man Cybern. Part B (Cybern.) 42(2), 482–500 (2012) 9. 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) 10. Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009) 11. 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 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. Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood-based mutation operator. IEEE Trans. Evol. Comput. 13(3), 526–553 (2009) 14. Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Trans. Evol. Comput. 15(1), 32–54 (2011) 15. Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011) 16. Brest, J., Korošec, P., Šilc, J., Zamuda, A., Bošković, B., Maučec, M.S.: Differential evolution and differential ant-stigmergy on dynamic optimisation problems. Int. J. Syst. Sci. 44(4), 663–679 (2013) 17. Brest, J., Maučec, M.S., Bošković, B.: iL-SHADE: improved L-SHADE algorithm for single objective real-parameter optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 1188–1195, IEEE, July 2016 18. Viktorin, A., Pluhacek, M., Senkerik, R.: Success-history based adaptive differential evolution algorithm with multi-chaotic framework for parent selection performance on CEC2014 benchmark set. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4797–4803, IEEE, July, 2016 19. Poláková, R., Tvrdík, J., Bujok, P.: L-SHADE with competing strategies applied to CEC2015 learning-based test suite. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4790–4796, IEEE, July 2016 20. Awad, N.H., Ali, M.Z., Suganthan, P.N., Reynolds, R.G.: An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2958–2965, IEEE, July 2016 21. Viktorin, A., Hrabec, D., Pluhacek, M.: Multi-chaotic differential evolution for vehicle routing problem with profits. In: Proceedings-30th European Conference on Modelling and Simulation, ECMS 2016. European Council for Modelling and Simulation (ECMS) (2016) 22. Szenkovits, A., Gaskó, N., Jakab, H.: Optimizing test input generation for reactive systems with an adaptive differential evolution. In: 2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 214–218, IEEE, September 2016 23. Zamuda, A., Sosa, J.D.H., Adler, L.: Constrained differential evolution optimization for underwater glider path planning in sub-mesoscale eddy sampling. Appl. Soft Comput. 42, 93–118 (2016) 24. Ekici, B., Chatzikonstantinou, I., Sariyildiz, S., Tasgetiren, M.F., Pan, Q.K.: A multi-objective self-adaptive differential evolution algorithm for conceptual high-rise building design. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 2272– 2279, IEEE, July 2016 25. Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015) 26. Zamuda, A., Brest, J.: Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm Evol. Comput. 25, 72–99 (2015) 27. Tanabe, R., Fukunaga, A.: How far are we from an optimal, adaptive DE? In: Handl, J., Hart, E., Lewis, Peter R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 145–155. Springer, Cham (2016). doi:10.1007/978-3-319-45823-6_14 28. Viktorin, A., Pluhacek, M., Senkerik, R.: Network based linear population size reduction in SHADE. In: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 86–93, IEEE, September 2016 | |
| 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.scopus.affiliation | Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, Zlin, Czech Republic |
