TBU Publications
Repository of TBU Publications

Differential migration: Sensitivity analysis and comparison study

DSpace Repository

Show simple item record


dc.title Differential migration: Sensitivity analysis and comparison study en
dc.contributor.author Dlapa, Marek
dc.relation.ispartof 2009 IEEE Congress on Evolutionary Computation, Vols 1-5
dc.identifier.isbn 978-1-4244-2958-5
dc.date.issued 2009
dc.citation.spage 1729
dc.citation.epage 1736
dc.event.title IEEE Congress on Evolutionary Computation
dc.event.location Trondheim
utb.event.state-en Norway
utb.event.state-cs Norsko
dc.event.sdate 2009-05-18
dc.event.edate 2009-05-21
dc.type conferenceObject
dc.language.iso en
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en
dc.identifier.doi 10.1109/CEC.2009.4983150
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4983150
dc.description.abstract The contribution treats properties of a new evolutionary algorithm - Differential Migration, and provides a comparison with other algorithms of this type. Differential Migration is tested with a standard artificial neural network benchmark and standard test functions for performance comparison. Sensitivity analysis is conducted in order to specify the optimal parameters and their influence to the algorithm performance. SOMA (Self-Organizing Migration Algorithm) and Differential Evolution are used as a reference, and the results are compared with Differential Migration. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1001847
utb.identifier.rivid RIV/70883521:28140/09:63507844!RIV10-MSM-28140___
utb.identifier.obdid 43859056
utb.identifier.scopus 2-s2.0-70450032903
utb.identifier.wok 000274803100228
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:05Z
dc.date.available 2011-08-09T07:34:05Z
utb.contributor.internalauthor Dlapa, Marek
utb.fulltext.affiliation Marek Dlapa M. Dlapa is with the Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, 760 05 Zlin, Czech Rep. (phone: +420 57 603 3032; fax: +420 57 603 5279; e-mail: dlapa@fai.utb.cz).
utb.fulltext.dates Manuscript received November 14, 2008.
utb.fulltext.references [1] S.-H. Chen and C.-F. Lu, “Would evolutionary computation help in designs of ANNs in forecasting foreign exchange rates?” In Proceedings of the 1999 Congress on Evolutionary ComputationCEC99, volume 1, Washington, DC, 6.-9. July 1999, IEEE, Piscataway, NJ, pp. 267–274. [2] S.-H. Chen and C.-C. Ni, “Evolutionary artificial neural networks and genetic programming: a comparative study based on financial data,” In George D. Smith and Nigel C. Steele, editors, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, Norwich, UK, 2.-4. April 1997, pp. 397–400. [3] M. Dlapa, R. Prokop, and I. Zelinka, “Evolutionary algorithms in fuzzy logic control,” Proceedings of Process Control, 2001. [4] M. Dlapa and R. Prokop, “Evolutionary μ -Synthesis for Systems with Parametric Uncertainties,” Proceedings of Conference on Control Application, (CCA/CACSD 2002), Glasgow, 2002, pp. 1264-1269. [5] M. Dlapa and R. Prokop, “Evolutionary μ-Synthesis: A Simple Controller for Feedback Loop,” Proceedings of 10th Mediterranean Conference on Control and Automation, Lisbon, 2002. [6] M. Dlapa and R. Prokop, “µ-Synthesis: Simple Controllers for Time Delay Systems,” Proceedings of 11th Mediterranean Conference on Control and Automation, Rhodes, Greece, 2003. [7] R. C. Eberhart and R. W. Dobbins, “Designing neural network explanation facilities using genetic algorithms,” In 1991 IEEE International Joint Conference on Neural Networks (IJCANN91), volume 3, Singapore, 18.-21. November 1991, IEEE, New York, pp. 1758–1363. [8] S. Fujita and H. Nishimura, “An Evolutionary Approach to Associative Memory in Recurrent Neural Networks,” Neural Process., Vol. 1, No. 2, 1994, pp. 9-13. [9] D.B. Fogel, “An Introduction to Simulated Evolutionary Optimization,” IEEE Trans. on Neural Networks., Vol. 5, No. 1, January 1994, pp. 3-14. [10] D.B. Fogel, E.C. Wasson III, E.M. Boughton, V.W. Porto and J.W. Shiveley, “Initial Results of Training Neural Networks to Detect Cancer Using Evolutionary Programming,” Control Cybern., (Poland), Vol. 26, No. 3, 1997, pp. 497-510. [11] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989. [12] Garrison W. Greenwood, “Applications of evolutionary strategies in training partially recurrent neural networks,” In Ošmera, editor, Proceedings of the MENDEL’95, Brno (Czech Republic), 26.-28. September 1995, pp. 53–58. [13] D. M. Himmelblau, Applied Nonlinear Programming. New York, McGraw-Hill, 1972. [14] J. Ilonen, K. Kämäräinen and J. Lampinen, “Differential Evolution Training Algorithm for Feed-forward Neural Networks,” Neural Processing Letters 17., No. 1, 2003, pp. 93-105. [15] Internet: http://www.icsi.berkeley.edu/~storn/code.html [16] Internet: http://www.swarmintelligence.org [17] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proc. IEEE int'l conf. on neural networks Vol. IV, IEEE service center, Piscataway, NJ, 1995, pp. 1942-1948. [18] S. M. Lucas, “The open ended evolution of neural networks,” In Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield (UK), 12.-14. September 1995, IEEE, pp. 388–393. [19] M. Murakawa, S. Yoshizawa, I. Kajitani, Y. Xin, N. Kajihara, M. Iwata and Higuchi, “The GRD chip: Genetic Reconfiguration of DSPs for Neural Networks Processing”, IEEE Transactions on Computers., Vol. 48, No. 6, June 1999, pp. 628-639. [20] N.G. Pavlidis, D. K. Tasoulis, V. P. Plagianakos, G. Nikiforidis and M. N. Vrahatis, ”Spiking Neural Network Training UsingEvolutionary Algorithms,” International Joint Conference on Neural Networks, (IJCNN 2004), Budapest, Hungary 2004. [21] K. V. Price, ”An Introduction to Differential Evolution,” in: David Corne, Marco Dorigo and Fred Glover (editors), New Ideas in Optimization. McGraw-Hill, London (UK), 1999, ISBN 007-709506-5, pp. 79–108. [22] D. Wicker, M. M. Rizki, and L. A. Tamburino, “E-Net: Evolutionary neural network synthesis,” Neurocomputing., Vol. 42, No. 1-4, January 2002, pp. 171–196. [23] S. Rudolph, “On a genetic algorithm for the selection of optimally generalizing neural network topologies,” In I. Parmee and M. J. Denham, editors, Adaptive Computing in Engineering Design and Control ’96 (ACEDC’96), 2nd International Conference of the Integration of Genetic Algorithms and Neural Network Computing and Related Adaptive Techniques with Current Engineering Practice, Plymouth (UK), 26.-28. March 1996. [24] R. Storn and K. Price. “Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces,” Journal of Global Optimization, Vol. 11, 1997, pp. 341–359. [25] R. Storn, “System design by constraints adaptation and Differential Evolution,” IEEE Trans. on Evolutionary Computation, Vol. 3, No. 1, 1999, pp. 22-23. [26] M. C. van Wezel, A. E. Eiben, C. M. H. van Kemenade, J. N. Kok, W. Kosters and I. G. Sprinkhuizen-Kuyper, “Natural solutions to practical problems: an overview of marketing, scheduling and information filtering problems solved by neural and evolutionary techniques,” In Proceedings of the Neural Networks: Best Practice in Europe, Amsterdam, Netherlands, 22. May 1997. World Scientific, Singapore, pp. 202–205. [27] X. Yao and Y. Liu, “Towards Designing Artificial Neural Networks by Evolution,” Applied Mathematics and Computation., Vol. 91, No. 1, April 1998, pp. 83-90. [28] X. Yao, “Evolutionary Artificial Neural Networks,” Int. J. Neural Systems., Vol. 4, 1993, pp. 203-222. [29] I. Zelinka, Chapter 7, “SOMA - Self Organizing Migrating Algorithm,” in: G. Onwubolu – B.V. Babu, New Optimization Techniques in Engineering, Springer-Verlag, in print, probably January 2004.
utb.fulltext.sponsorship This work was supported by the Ministry of Education Youth and Sports of the Czech Republic under Grant MSM7088352102.
Find Full text

Files in this item

Show simple item record