Super-fit control adaptation in memetic differential evolution frameworks

• Birkho G, Lane SM (1953) A survey of modern algebra. Macmillan, New York

  Google Scholar 

• Brest BBMMJ, Greiner S, Žumer V. (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evolut Comput 10(6): 646–657

  Article  Google Scholar 

• Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for on-line and off-line control design of pmsm drives. IEEE Trans Syst Man Cybern B (special issue on Memetic Algorithms) 37(1): 28–41

  Article  Google Scholar 

• Cascella MSGL, Salvatore N, Salvatore L (2005) On-line simplex-genetic algorithm for self-commissioning of electric drives. In: Proceedings 11th EPE, pp 277–283

• Chiou J-P, Wang F-S (1998) A hybrid method of differential evolution with application to optimal control problems of a bioprocess system. In: The 1998 IEEE international conference on evolutionary computation proceedings, pp 627–632

• Chiou J-P, Wang F-S (1999) Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to a fed-batch fermentation process. Comp Chem Eng 23: 1277– 1291

  Article  Google Scholar 

• Chiou J-P, Chang C-F, Su C-T (2004) Ant direction hybrid differential evolution for solving large capacitor placement problems. IEEE Trans Power Syst 19: 1794–1800

  Article  Google Scholar 

• Daugman J (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am 2(7): 1160–1169

  Article  Google Scholar 

• del Toro Garcia X, Neri F, Cascella GL, Salvatore N (2006) A surrogate assisted hooke-jeeves algorithm to optimize the control system of a pmsm drive. In: Proceedings of IEEE international symposium on industrial electronics, vol 1, pp 347–352

• Dunn D, Higgins W (1995) Optimal Gabor filters for texture segmentation. IEEE Trans Image Process 4: 947–964

  Article  Google Scholar 

• Dury W (2001) Control techniques drives & controls handbook. Institution Electrical Engineers

• Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, pp 39–43

• Eiben AE, Smith JE (2003) Introduction to evolutionary computation. Springer, Berlin

  Google Scholar 

• Gabor D (1946) Theory of communication. J IEE (London) 93: 429– 457

  Google Scholar 

• Hart WE, Krasnogor N, Smith JE (2004) Memetic evolutionary algorithms. In: Hart WE, Krasnogor N, Smith JE(eds) Recent advances in memetic algorithms. Springer, Berlin, pp 3–27

  Google Scholar 

• Hendtlass T (2001) A combined swarm differential evolution algorithm for optimization problems. In: Lecture Notes in Computer Science, vol 2070. Springer, Heidelberg, pp 11–18

• Iivarinen J, Pakkanen J, Rauhamaa J (2004) A som-based system for web surface inspection. In: Machine vision applications in industrial inspection XII, vol 5303, pp 178–187, SPIE

• Jain LC, de Silva CW (1998) Intelligent adaptive control: industrial applications. CRC, Boca Raton

  Google Scholar 

• Kaelo P, Ali MM (2007) Differential evolution algorithms using hybrid mutation. Comput Optim Appl 37: 231–246

  Article  MATH  MathSciNet  Google Scholar 

• Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, pp 1942–1948

• Krishnan R (2001) Electronic motor drives: modeling, analysis and control. Prentice-Hall, New Jersey

  Google Scholar 

• Kirkpatrick S, Gelatt CDJ, Vecchi MP (1983) Optimization by simulated annealing. Science 220: 671–680

  Article  MathSciNet  Google Scholar 

• Khorrami HMF, Krishnamurthy P (2003) Modeling and adaptive nonlinear control of electric motors. Springer, Heidelberg

  Google Scholar 

• Krasnogor N (2004) Toward robust memetic algorithms. In: Hart WE, Krasnogor N, Smith JE(eds) Recent advances in memetic algorithms. Springer, Berlin, pp 185–207

  Google Scholar 

• Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the nelder-mead simplex method in low dimensions. SIAM J Optim 9: 112–147

  Article  MATH  MathSciNet  Google Scholar 

• Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Oŝmera P (ed) Proceedings of 6th international mendel conference on soft computing, pp 76–83

• Leonhard W (2001) Control of electrical drives, 2nd edn. Springer, Heidelberg

  Google Scholar 

• Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evolut Comput 10(3): 281–295

  Article  Google Scholar 

• Lin Y-C, Wang F-S, Hwang K-S (1999) A hybrid method of evolutionary algorithms for mixed-integer nonlinear optimization problems. In: Proceedings of the IEEE congress on evolutionary computation, vol 3, July

• Lin Y-C, Hwang K-S, Wang F-S (2001) Co-evolutionary hybrid differential evolution for mixed-integer optimization problems. Eng Optim 33(6): 663–682

  Article  Google Scholar 

• Lin Y-C, Hwang K-S, Wang F-S (2002) Hybrid differential evolution with multiplier updating method for nonlinear constrained optimization. In: Proceedings of the IEEE congress on evolutionary computation, May

• Liu J, Lampinen J (2002a) On setting the control parameter of the differential evolution algorithm. In: Proceedings of the 8th international Mendel conference on soft computing, pp 11–18

• Liu J, Lampinen J (2002b) Adaptive parameter control of differential evolution. In: Proceedings of the 8th international Mendel conference on soft computing, pp 19–26

• Liu JLJ (2002) A fuzzy adaptive differential evolution algorithm. In: Proceedings of the 17th IEEE region 10 international conference on computer, communications, control and power engineering, vol I, p 606–611

• Liu JLJ (2005) A fuzzy adaptive differential evolution algorithm. Soft computing—a fusion of foundations, methodologies and applications, vol 9. Springer, Heidelberg, pp 448–462

  Google Scholar 

• Lopez Cruz IL, Van Willigenburg L, Van Straten G (2001) Parameter control strategy in differential evolution algorithm for optimal control. In: Hamza M (ed) Proceedings of the IASTED international conference artificial intelligence and soft computing (ASC 2001), May 21–24. ACTA Press, Calgary, pp 211–216

• Kumar A, Pang G (2002) Defect detection in textured materials using gabor filters. IEEE Trans Ind Appl 38(2): 425–440

  Article  Google Scholar 

• Manjunath WYMBS, Haley GM (2000) Handbook of image & video processing, Chap. Multiband techinques for texture classification and segmentation. Academic Press, New York, pp 367–381

• Mydur R (2000) Application of evolutionary algorithms and neural networks to electromagnetic inverse problems. Master’s Thesis, Texas A and M University, Texas

• Neri F, Toivanen J, Cascella GL, Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinform (Special Issue on Computational Intelligence Approaches in Computational Biology and Bioinformatics) 4(2): 264–278

  Google Scholar 

• Nelder A, Mead R (1965) A simplex method for function optimization. Comput J 7: 308–313

  MATH  Google Scholar 

• Neumann D, de Araujo HX: Hybrid differential evolution method for the mixed h2/h robust control problem under pole assignment. In: Proceedings of the 44th IEEE conference on decision and control, 2005 and 2005 European control conference, pp 1319–1324

• Ng H (2004) Automatic Thresholding for Defect Detection. In: Proceedings of the third international conference on image and graphics (ICIG’04), vol 00, pp 532–535

• Ong YS, Keane AJ (2004) Meta-lamarkian learning in memetic algorithms. IEEE Trans Evolut Comput 8(2): 99–110

  Article  Google Scholar 

• Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1): 141–152

  Article  Google Scholar 

• Panagopoulos H, Åström KJ, Hägglund T. (2002) Design of PID controllers based on constrained optimisation. IEE Proc Control Theory Appl 149: 32–40

  Article  Google Scholar 

• Parker S, Chan J (2002) Dirt counting in pulp: an approach using image analysis methods. In: Signal and Image Processing (SIP 2002)

• Price KV, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Heidelberg

  MATH  Google Scholar 

• Rogalsky T, Derksen RW (2000) Hybridization of differential evolution for aerodynamic design. In: Proceedings of the 8th annual conference of the computational fluid dynamics society of Canada, pp 729–736

• Rechemberg I (1973) Evolutionstrategie: Optimierung Technisher Systeme nach prinzipien des Biologishen Evolution. Fromman-Hozlboog Verlag, Stuggart

  Google Scholar 

• Rosenbrock HH (1960) An automatic method for findong the greatest or least value of a function. Comp J 3(3): 175–184

  Article  MathSciNet  Google Scholar 

• Schwefel H (1981) Numerical optimization of computer models. Wiley, Chichester

  MATH  Google Scholar 

• Slotine J-J, Li W (1990) Applied nonlinear control. Prentice Hall, New Jersey

  Google Scholar 

• Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. TR-95-012, ICSI

• Su C-T, Lee C-S (2003) Network reconfiguration of distribution systems using improved mixed-integer hybrid differential evolution. IEEE Trans Power Delivery 18: 1022–1027

  Article  Google Scholar 

• Sun Z, Bebis G, Miller R (2005) On-road vehicle detection using evolutionary gabor filter optimization. IEEE Trans Intell Transportation Syst 6: 125–137

  Article  Google Scholar 

• Szklarski KJL, Horodecki A (1990) Electric drive systems dynamics. Elsevier, Amsterdam

  Google Scholar 

• Szu H, Hartley R (1987) Fast simulated annealing. Phys Lett A 122: 157–162

  Article  Google Scholar 

• Tirronen V, Neri F, Kärkkäinen T, Majava K, Rossi T (2007) A memetic differential evolution in filter design for defect detection in paper production. In: Applications of evolutionary computing. Lectures Notes in Computer Science, vol 4448. Springer, Berlin, pp 320–329

• Tsa D, Wu S (2000) Automated surface inspection using Gabor filters. Int J Adv Manufact Technol 16(7): 474–482

  Google Scholar 

• Tvrdík J (2006) Differential evolution: competitive setting of control parameters. In: Proceedings of the international multiconference on computer science and information technology, pp 207–213

• Wang F-S, Jang H-J (2000) Parameter estimation of a bioreaction model by hybrid differential evolution. Proc the IEEE Congress Evolut Comput 1: 410–417

  Google Scholar 

• Weldon TP, Higgins WE (1996) Design of multiple gabor filters for texture segmentation (Atlanta, GA), pp 2243–2246

• Zaharie D (2002) Critical values for control parameters of differential evolution algorithm. In: Matuŝek R, Oŝmera P (eds) Proceedings of 8th international Mendel conference on soft computing, pp 62–67

• Zanchetta FCMMP, Sumner M, Mininno E (1995) On-line and off-line control design in power electronics and drives using genetic algorithms. In: Proceedings 11th IEEE CAIA, pp 277–283

• Zielinskiand K, Weitkemper P, Laur R, Kammeyer K-D (2006) Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proceedings of the IEEE congress on evolutionary computation, pp 1857–1864

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