Accelerating IEC and EC Searches with Elite Obtained by Dimensionality Reduction in Regression Spaces
Inhaltsverzeichnis
Reference
Yan Pei and Hideyuki Takagi: Accelerating IEC and EC Searches with Elite Obtained by Dimensionality Reduction in Regression Spaces. Journal of Evolutionary Intelligence, Springer-Verlag Berlin Heidelberg, vol.6, no.1, pp. 27-40 (2013).
DOI
http://dx.doi.org/10.1007/s12065-013-0088-9
Abstract
We propose a method for accelerating interactive evolutionary computation (IEC) and evolutionary computation (EC) searches using elite obtained in one-dimensional spaces and use benchmark functions to evaluate the proposed method. The method projects individuals onto n one-dimensional spaces corresponding to each of the n searching parameter axes, approximates each landscape using interpolation or an approximation method, finds the best coordinate from the approximated shape, obtains the elite by combining the best n found coordinates, and uses the elite for the next generation of the IEC or EC. The advantage of this method is that the elite may be easily obtained thanks to their projection onto each one-dimensional space and there is a higher possibility that the elite individual locates near the global optimum. We compare the proposal with methods for obtaining the landscape in the original search space, and show that our proposed method can significantly save computational time. Experimental evaluations of the technique with differential evolution using a simulated IEC user (Gaussian mixture model with different dimensions) and 34 benchmark functions show that the proposed method substantially accelerates IEC and EC searches.
Extended Abstract
Bibtex
Used References
Brest J, Greiner S, Boskovic B, Mernik M, Umer V (2008) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6):107–125
Carretero JA, Nahon MA (2005) Solving minimum distance problems with convex or concave bodies using combinatorial global optimization algorithms. IEEE Trans Syst Man Cybern Part B Cybern 35(6):1144–1155 http://dx.doi.org/10.1109/TSMCB.2005.850172
De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive system, Ph.D Dissertation. University of Michigan, USA
Gong W, Cai Z, Ling CX, Li H (2011) Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans Syst Man Cybern 41(2):397–413 http://dx.doi.org/10.1109/TSMCB.2010.2056367
Hakimi-Asiabara M, Ghodsypoura SH, Kerachianb R (2009) Multi-objective genetic local search algorithm using Kohonen’s neural map. Comput Ind Eng 56(4):1566–1576 http://dx.doi.org/10.1016/j.cie.2008.10.010
Liu LM, Wang NP, Li FC (2009) Study on convergence of self-adaptive and multi-population composite genetic algorithm. In: International Conference on machine learning and cybernetics, vol 5. Baoding, China, pp 2680–2685, 12–15 Jul 2009
Lahiri A, Chakravorti S (2004) Electrode-spacer contour optimization by ANN aided genetic algorithm. IEEE Trans Dielectr Electr Insul 11(6):964–975 http://dx.doi.org/10.1109/TDEI.2004.1387819
Mantawy AH, Abdel-Magid YL, Selim SZ (1999) Integrating genetic algorithms, Tabu Search, and simulated annealing for the unit commitment problem. IEEE Trans Power Syst 14(3):829–836 http://dx.doi.org/10.1109/59.780892
Nakano Y, Takagi H (2009) Influence of quantization noise in fitness on the performance of interactive PSO. In: IEEE congress on evolutionary computation (CEC2009). Trondheim, Norway, pp 2146–2422
Pei Y, Takagi H (2011) Accelerating evolutionary computation with elite obtained in projected one-dimensional spaces. In: Proceedings of the 5th international conference on genetic and evolutionary computing (ICGEC2011). Kimmen Taiwan, Xiamen China, pp 89–92, 29 Aug, 1 Sept 2011
Pei Y, Takagi H (2011) A novel traveling salesman problem solution by accelerated evolutionary computation with approximated cost matrix in an industrial application. In: Proceedings of the 3rd international conference on soft computing and pattern recognition (SoCPaR2011). Dalian, China, pp 39–44, 14–16 Oct 2011
Pei Y, Takagi H (2011) A survey on accelerating evolutionary computation approaches. In: Proceedings of the 3rd international conference on soft computing and pattern recognition (SoCPaR2011). Dalian, China, pp 201–206, 14–16 Oct 2011
Pei Y, Takagi H (2011) Comparative evaluations of evolutionary computation with elite obtained in reduced dimensional spaces. In: Proceedings of the 3rd international conference on intelligent networking and collaborative systems (INCoS2011). Fukuoka, Japan, pp 35–40, 30 Nov, 2 Dec 2011
Pei Y, Takagi H (2013) Triple and quadruple comparison-based interactive differential evolution and differential evolution. In: Proceedings of the foundations of genetic algorithms XII (FOGA) 2013 workshop. Adelaide, Australia, pp 173–182, 16–20 Jan 2013)
Pei Y, Takagi H (2012) Fourier analysis of the fitness landscape for evolutionary search acceleration. In: Proceedings of the 2012 IEEE congress on evolutionary computation (IEEE CEC 2012). Brisbane, Australia, pp 2934–2940, June 2012)
Pei Y, Takagi H (2012) Comparative study on fitness landscape approximation with Fourier transform. In: Proceedings of the sixth international conference on genetic and evolutionary computation (ICGEC2012). Kitakyushu, Japan, pp 400–403, 25–28 Aug 2012
Price K, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptition for global numberical optimization. IEEE Trans Evol Comput 13(2):398–417 http://dx.doi.org/10.1109/TEVC.2008.927706
Suganthan P, Hansen N, Liang J, Deb K, Chen Y, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization, Technical Report. Nanyang Technological University, Singapore, http://www.ntu.edu.sg/home/EPNSugan
Qin AK, Huang VL, Suganthan PN (2009) JADE: adaptive differential evolution with external archive. IEEE Trans Evol Comput 13(5):945–958 http://dx.doi.org/10.1109/TEVC.2009.2014613
Takagi H, Pallez D (2009) Paired comparison-based interactive differential evolution. In: The first world congress on nature and biologically inspired computing (NaBIC2009). Coimbatore, India, pp 375–480, 9–11 Dec 2009
Takagi H, Ingu T, Ohnishi K (2003) Accelerating a GA convergence by fitting a single-peak function. J Jpn Soc Fuzzy Theory Intell Inform 15(2):219–229 (in Japanese)
Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc IEEE 89(9):1275–1296 http://dx.doi.org/10.1109/5.949485
Wang Y, Cai ZX, Guo GQ, Zhou YR (2007) Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Trans Syst Man Cybern Part B Cybern 37(3):560–575 http://dx.doi.org/10.1109/TSMCB.2006.886164
Whitley D, Mathias K, Rana S, Dzubera J (1995) Building better test functions. In: Proceedings of the 6th international conference on genetic algorithms (ICGA95). Pittsburgh, PA, pp 239–246
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102 http://dx.doi.org/10.1109/4235.771163
Zhou ZZ, Ong YS, Nair pB, Keane AJ, Lum KY (2007) Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Trans Syst Man Cybern Part C Appl Rev 37(1):66–76 http://dx.doi.org/10.1109/TSMCC.2005.855506
Links
Full Text
[extern file]
