r-1 - shodhganga : a reservoir of indian theses @...

33
R-1 REFERENCES [Abd03] Abdul-Latif, A. and Nesnas K., “Plastic Collapse of Cylinders Under Constrained Conditions,” ASME Journal of Engineering Materials and Technology, vol. 125(2), pp. 215-221, 2003. [Abd06] Abdesslem, L., Soham, M., Mohamed, B., “Multiple sequence alignment by quantum genetic algorithm,” In: Proc. IPDPS, pp. 360–367, 2006. [Abs04] Abs da Cruz, A., Hall Barbosa, C., Pacheco, M., Vellasco, M., “Quantum- inspired evolutionary algorithms and its application to numerical optimization problems,” Lecture Notes on Computer Science, 3316, pp. 212–217, 2004. [Abs05] Abs da Cruz, A., Pacheco, M., Vellasco, M., Barbosa, C., “Cultural operators for a quantum-inspired evolutionary algorithm applied to numerical optimization problems,” Lecture Notes on Computer Science, 3562, pp.1–10, 2005. [Abs06] Abs da Cruz, A., Vellasco, M., Pacheco, M., “Quantum-inspired evolutionary algorithm for numerical optimization,” In: Proc. CEC, pp. 2630–2637, 2006. [Abs07] Abs da Cruz, A., Vellasco, M., Pacheco, M., “Quantum-inspired evolutionary algorithm for numerical optimization,” In: Studies in Computational Intelligence, vol. 75, pp. 19–37, 2007. [Ach06] Acharjee S., and Zabaras N., “The Continuum Sensitivity Method for the Computational Design of Three-Dimensional Deformation Processes,” Computer Methods in Applied Mechanics and Engineering, vol. 195(48-49), pp. 6822-6842, 2006. [Ada04] Adam, G.K., “Hybrid neural controller of a stepper motor for a manipulator arm,” Proceedings of the Fourth International Workshop on Robot Motion and Control, pp. 321–326, 2004. [Ade10] Adeyemo J., F. Otieno., “Differential Evolution Algorithm for Solving Multi- Objective Crop Planning Model,” Agricultural Water Management, Vol. 97, pp. 848–856, 2010. [Ahu00] Ahuja, R. K., Orlin, J. B., and Tiwari, A., “A greedy genetic algorithm for the quadratic assignment problem,” Computers and Operations Research, 27, 917–934, 2000. [Akb05] Akbarzadeh-T, M., “Evolutionary quantum algorithms for structural design,” In: Proc. IEEE SMC, vol. 4, pp. 3077–3082, 2005. [Al07] Al-Othman, A., Al-Fares, F., EL-Nagger, K., “Power system security constrained economic dispatch using real coded quantum inspired evolution algorithm,” International Journal of Electrical Computational Systems, 1(4), pp. 199–206 , 2007. [Ala10] Alawode, K., Jubril, A., “Multiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithm,” World Academy of Science, Engineering and Technology, 63, pp.790-795, 2010. [Ale08] Alexandrov I. V., “Multiscale modeling of SPD processes for grain refinement”, Materials Science Forums, pp. 1070-1075, 2008.

Upload: nguyenlien

Post on 10-Mar-2018

231 views

Category:

Documents


6 download

TRANSCRIPT

Page 1: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-1

REFERENCES

[Abd03] Abdul-Latif, A. and Nesnas K., “Plastic Collapse of Cylinders Under Constrained Conditions,” ASME Journal of Engineering Materials and Technology, vol. 125(2), pp. 215-221, 2003.

[Abd06] Abdesslem, L., Soham, M., Mohamed, B., “Multiple sequence alignment by quantum genetic algorithm,” In: Proc. IPDPS, pp. 360–367, 2006.

[Abs04] Abs da Cruz, A., Hall Barbosa, C., Pacheco, M., Vellasco, M., “Quantum-inspired evolutionary algorithms and its application to numerical optimization problems,” Lecture Notes on Computer Science, 3316, pp. 212–217, 2004.

[Abs05] Abs da Cruz, A., Pacheco, M., Vellasco, M., Barbosa, C., “Cultural operators for a quantum-inspired evolutionary algorithm applied to numerical optimization problems,” Lecture Notes on Computer Science, 3562, pp.1–10, 2005.

[Abs06] Abs da Cruz, A., Vellasco, M., Pacheco, M., “Quantum-inspired evolutionary algorithm for numerical optimization,” In: Proc. CEC, pp. 2630–2637, 2006.

[Abs07] Abs da Cruz, A., Vellasco, M., Pacheco, M., “Quantum-inspired evolutionary algorithm for numerical optimization,” In: Studies in Computational Intelligence, vol. 75, pp. 19–37, 2007.

[Ach06] Acharjee S., and Zabaras N., “The Continuum Sensitivity Method for the Computational Design of Three-Dimensional Deformation Processes,” Computer Methods in Applied Mechanics and Engineering, vol. 195(48-49), pp. 6822-6842, 2006.

[Ada04] Adam, G.K., “Hybrid neural controller of a stepper motor for a manipulator arm,” Proceedings of the Fourth International Workshop on Robot Motion and Control, pp. 321–326, 2004.

[Ade10] Adeyemo J., F. Otieno., “Differential Evolution Algorithm for Solving Multi-Objective Crop Planning Model,” Agricultural Water Management, Vol. 97, pp. 848–856, 2010.

[Ahu00] Ahuja, R. K., Orlin, J. B., and Tiwari, A., “A greedy genetic algorithm for the quadratic assignment problem,” Computers and Operations Research, 27, 917–934, 2000.

[Akb05] Akbarzadeh-T, M., “Evolutionary quantum algorithms for structural design,” In: Proc. IEEE SMC, vol. 4, pp. 3077–3082, 2005.

[Al07] Al-Othman, A., Al-Fares, F., EL-Nagger, K., “Power system security constrained economic dispatch using real coded quantum inspired evolution algorithm,” International Journal of Electrical Computational Systems, 1(4), pp. 199–206 , 2007.

[Ala10] Alawode, K., Jubril, A., “Multiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithm,” World Academy of Science, Engineering and Technology, 63, pp.790-795, 2010.

[Ale08] Alexandrov I. V., “Multiscale modeling of SPD processes for grain refinement”, Materials Science Forums, pp. 1070-1075, 2008.

Page 2: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-2

[Alf04] Alfares, F., Alfares, M., Esat, I., “Quantum-inspired evolution algorithm: experimental analysis,” In: Proc. ACDM, pp. 377–389, 2004.

[Alf06] Alfares, F., Esat, I., “Real-coded quantum inspired evolution algorithm applied to engineering optimization problems,” In: Proc. ISoLA, pp. 169–176, 2006.

[Alv02] D’Alvise, L., Massoni, E. and Walloe, S. J., “Finite element modeling of inertia friction welding process between dissimilar materials,” Journal of Materials Processing Technology, pp.125-126, 287-391, 2002.

[Amm10] Ammar, A.B., Nashwan, D., “Simulation Modeling and Multi Layer Genetic Algorithms to Identify Optimal Crew Allocation in the Precast Industry,” Proceedings of the International Conference on Computing in Civil and Building Engineering, Vol. I, pp. 765-771, 2010.

[Amo98] Amorini, E., Brnadini, P., Fabbio, G. and Tabacchi, G., “Volume and Bio-mass prediction models for Turkey Oak Coppica in Central and Southern Tuscany,”Instituto-Spreminentale-per-la-Selvicoltura, Vol.29, pp. 41-56, 1998.

[Ant02] Anto´nio, C.A.C. and Dourado, N.M., “Metal forming process optimization by inverse evolutionary search,” Journal of Materials Processing Technology, vol. 121, pp. 403-13, 2002.

[Ara08] Araujo, M., Nedjah, N., Mourelle, L., “Quantum-inspired evolutionary state assignment for synchronous finite state machines,” Journal of Universal Computer Science, 14(15), pp. 2532–2548, 2008.

[Ari03] Arif A. M., Sheikh A. K., and Qamar S. Z., “A Study of die failure mechanisms in aluminum extrusion,” Journal of Materials Processing Technology, vol. 134, pp. 318 – 328, 2003.

[Ari11] Arikaran, P., Jayabalan, V., “A Grouping Genetic Algorithm for Solving the Machine Component Grouping Problem,” European Journal of Scientific Research, ISSN 1450-216X, Vol.63, No.3, pp.347-357, 2011.

[Aru04] Aruldoss, A. V. T., and Ebenezer, J., “A Hybrid PSO-SQP for economic dispatch with valve-point effect,” Electric Power Systems Research, 71(1), pp. 51–59, 2004.

[Aru05] Aruldoss, A. V. T., and Ebenezer, J. A., “A modified hybrid EP-SQP approach for dynamic dispatch with valve-point effect,” International Journal of Electrical Power and Energy Systems, 27(8), pp. 594–601, 2005.

[ASM88] ASM Handbook, 9e, “Forming and Forging”, vol. 14, 1988.

[Att02] Attaviriyanupap, K. H., Tanaka, E., and Hasegawa, J., “A hybrid EP and SQP for dynamic economic dispatch with non-smooth incremental fuel cost function,” IEEE Transaction on Power Systems, 17(2), pp. 411–416, 2002.

[Axe84] Axelsson, O. and Baker, V.A., “Finite element solution of boundary value problems - theory and computations”, Academic, Orlando, 1984.

[Bad96] Badrinarayanan, S. and Zabaras, N., “A sensitivity analysis for the optimal design of metal forming processes,” Computer Methods in Applied Mechanics and Engineering, vol. 129 (4), pp. 319-48, 1996.

[Bal01] Balendra, R., “Net-Shape Forming: State of Art,” Journal of Materials Processing Technology, vol. 115, pp. 172-179, 2001.

Page 3: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-3

[Bali04] Balic, J., “Neural-Network-Based Numerical Control for Milling Machine,” Journal of Intelligent and Robotic Systems, 40, pp. 343–358, 2004.

[Bar00] Barnett, K.J., “Research initiatives for the forging industry,” Journal of Materials Processing Technology, vol. 98, pp. 162-164, 2000.

[Bec04] Bechtold, W.A., “Largest crown width prediction models for 53 species in the western united states,” Western J. App. For., Vol. 19(4), pp. 245-251, 2004.

[Ben00] Benvenuto, F., Marani, A., “Neural Networks for Environmental Problems: Data Quality Control and Air Pollution Nowcasting,” Global Nest: The International Journal, Vol. 2, No.3, pp. 281-292, 2000.

[Ber11] Bernd-Arno, B., Tarik H., Anas B., Jens M., “Advanced Friction Modeling for Bulk Metal Forming Processes,” Prod. Eng. Res. Devel., 5, DOI 10.1007/s11740-011-0344-8, pp. 621–627, 2011.

[Ber92] Berenji H. R. and Khedkar P., “Learning And Tuning Fuzzy Logic Controllers Through Reinforcements,” IEEE Transactions on Neural Networks, vol. 3(5), pp.724–740, 1992.

[Bey04] Beygelzimer, Y., Varyukhin, V., Orlov, D., Synkov, S., Spuskanyuk, A., Pashinska, Y., “Nanomaterials by severe plastic deformation,” Weinheim, Germany: Wiley–VCH Verlag., pp. 511-518, 2004.

[Bey09] Beygelzimer, Y., Varyukhin, V., Synkov, S. and Orlov D., “Useful Properties of Twist Extrusion”,Materials Science and Engineering, Vol. A 503, pp. 14-17, 2009.

[Bey99] Beygelzimer, Y., Varyukhin, V.N., Synkov, S.G., Sapronov, A.N., Synkov, V.G., “New Schemes of Large Plastic Deformations Accumulating with using of Twist Extrusion,” Phys. Technol. High Press, 9 (3), pp. 109–111, 1999.

[Bha99b] Bhagwan Das, D. and Patvardhan, C. “Solutions of economic load dispatch using real coded hybrid stochastic search,” Electrical Power and Energy Systems. vol. 21(3), pp.165-170, 1999.

[Bi07] Bi, X., Jin, G., “Image segmentation algorithm based on quantum immune programming,” In: Proc IEEE, ICIT, pp. 403–407, 2007.

[Big98] Biglari, F.R., O’Dowd, N.P., and Fenner, R.T., “Optimum design of forging dies using fuzzy logic in conjunction with the backward deformation method,” International Journal of Machine Tools and Manufacture, vol. 38, pp.981-1000, 1998.

[Bog11] Bogart, Y.M., Manuel, C., Juan, R. C., “Distributed Agencies Methodology for the Modeling of Complex Social System using Neuro-Fuzzy and Distributed Agencies,” Journal of Selected Areas in Soft Computing (JSSE), March Edition, pp. 345-349, 2011.

[Bon05a] Bonte, M.H.A., van den Boogaard, A.H. and Hu´etink, J., “Metamodellingtechniques for the optimization of metal forming processes,” in Proceedings of ESAFORM, pp. 155–158, Cluj-Napoca, Romania, 2005.

[Bon05b] Bonte, M.H.A., van den Boogaard, A.H. and Hu´etink, J., “Solving optimisation problems in metal forming using finite element simulation and meta modelling techniques,” Proceedings of Automatic Process Optimization in Materials Technology (APOMAT), pp. 242–251, Morschach, Switzerland, 2005.

Page 4: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-4

[Bon08] Bonte, M. H. A., Boogaard van den, A. H., Huétink, J., “An Optimisation Strategy for Industrial Metal Forming Processes,” Structural and Multidisciplinary Optimization, 35, DOI 10.1007/s00158-007-0206-3, pp. 571–586, 2008.

[Bor02] Boroomand, B., Parvizian, J. and Pishevar, A.R., “Contact modeling in forging simulation,”Journal of Materials Processing, vol. 125/126, pp. 583-7, 2002.

[Bre62] Bremermann, H. J., “Optimization through Evolution and Recombination,” in Self-Organizing Systems, Washington D.C.: Spartan Books, pp. 93-106, 1962.

[Brid35] Bridgman, P.W., “Effects of High Shearing Stress Combined with High Hydrostatic Pressure,” Physical Review, 48, pp. 825–847, 1935.

[Brid52] Bridgman, P.W., “Studies in large plastic flow and fracture,” New York (NY): McGraw-Hill; 1952.

[Brie07] Brien O’ M.J., Bremen H.F., Furukawa M., Horita Z. and Langdon T.G., “A Finite Element Analysis of the Super plastic Forming of an Aluminum Alloy Processed by ECAP,” Materials Science and Engineering A, 456, pp. 236-242, 2007.

[Bru97] Brusco, M.J., et. al., “A morph-based simulated annealing heuristic for a modified bin-packing problem,” Journal of the Operational Research Society, vol. 48, pp.433-439, 1997.

[Buc94] Buckley, J. J. and Hayashi, Y. “Fuzzy neural networks: A survey”. Fuzzy Sets and Systems, vol. 66, pp. 1–13, 1994.

[Buc95] Buckley, J. J. and Hayashi, Y. “Neural networks for fuzzy systems”. Fuzzy Sets and Systems, vol. 71, pp. 265–276, 1995.

[Bur00] Burke, E. K., and Smith, A. J., “Hybrid evolutionary techniques for the maintenance scheduling problem,” IEEE Transactions on Power Systems,1(1), pp. 122–128, 2000.

[Cai03] Cai, X., D. McKinney, M. Rosegrant, “Sustainability Analysis for Irrigation Water Management in the Aral Sea Region,” Agricultural Systems, Vol. 76, pp.1043–1066, 2003.

[Cha02] Chao, T.S., Taho, Y., Chir, M.K., “A Neural-Network Approach for Semiconductor Wafer Post-Sawing Inspection,” IEEE Transactions on Semiconductor Manufacturing, Vol. 15, No. 2, pp.260-266, 2002.

[Cha11] Chang-yuan, Y., Qiu-qin, L.,Yu, C., “An Efficient Hybrid Evolutionary Optimization Algorithm combining Ant Colony Optimization with Simulated Annealing,” International Journal of Digital Content Technology and its Applications, Vol.5, No. 8, pp. 234-240, 2011.

[Che92] Chenot, J. L., Wood, R.D. and Zienkiewicz, O.C. (Eds), “Numerical methods in industrial forming processes,” International Conference on Numerical Methods in Industrial Forming Processes (NUMIFORM’92), A.A. Balkema, Rotterdam, 1992.

[Che96] Chenot, J. L, Massoni, E., and Fourment, L., “Inverse problems in finite element simulation of metal forming simulation of metal forming processes”, Engineering Computations, vol.13, pp. 190 -225,1996.

[Chen04] Chen, H., Zhang, J., Zhang, C., “Chaos updating rotated gates quantum-inspired genetic algorithm,” In: Proc. ICCCAS, pp. 1108–1112, 2004.

Page 5: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-5

[Chen10] Chenot, J.L., Fourment, L., Ducloux, R., Wey, E., “Finite Element Modelling of Forging and other Metal Forming Processes,” International Journal of Material Forming, Vol. 3, Suppl 1, DOI 10.1007/s12289-010-0781-5, pp. 359-362, 2010.

[Chet09] Chethana, H. M., “Prediction Models in Teak Based Agroforestry Systems in Northern Transitional Zone of Karnataka,” Thesis submitted to the University of Agricultural Sciences, Dharwad, Karnataka, for the Degree of Master of Science (Agriculture), pp. 01-76, 2009.

[Chou00] Choudhry, A. and Wertheimer, T.B.: “Comparison of finite strain plasticity algorithms in MARC,” Technical Report, Marc Analysis Research Corporation, 2000.

[Chr90] Chryssolouris, G. and Guillot, M., “A comparison of statistical and AI approaches to the selection of process parameters in intelligent machining,” ASME Journal of Engineering for Industry, vol. 112, pp.122– 131, 1990.

[Chu01] Chu, H.S., Liu, K.S., Yeh, J.W., Scripta Materialia, 45, pp. 541- 548, 2001.

[Chun03] Chung, S. H., Fourment, L., Chenot, J. L., and Hwang, S. M., “Adjoint state method for shape sensitivity analysis in non-steady forming applications,” International Journal for Numerical Methods in Engineering, vol. 57(10), pp. 1431-1444, 2003.

[Cler02] Clerc, M., and Kennedy, J., “The particle swarm: explosion stability and convergence in a multi-dimensional complex space,” IEEE Transaction on Evolutionary Computation, 6(1), pp. 58–73, 2002.

[Coe06] Coello, C.A.C., “Evolutionary multi-objective optimization: a historical view of the field,” IEEE Computational Intelligence Magazine, pp.28-36, February,2006.

[Cou91] Coupez, T., Soyris, N., and Chenot, J.L., “3-D Finite Element Modeling of the Forging Process with Automatic Remeshing,” Journal of Materials Processing Technology, vol. 27, pp. 119–133, 1991.

[Das97] Das Gupta, Dipankar. and Michalewicz, Zbigniew., “Evolutionary algorithms in engineering applications,” Springer-Verlag, March 1997.

[Dav11] David, S., Ondrej, B., Jakub, C., “Prediction of Grinding Parameters for Plastics by Artificial Neural Networks,” International Journal of Mechanics, Vol. 5, Issue 3, pp. 250-261, 2011.

[Deb98] Deb, K., “Multi-objective genetic algorithms: Problem difficulties and construction of test functions,” Technical Report No. CI-49/98, Department of Computer Science/XI, University of Dortmund, Germany, 1998.

[Dej09] Dejan,T., Miodrag, M., “Metal Cutting Process Parameters Modeling: A Neural Network Approach” Journal of Scientific and Industrial Research, Vol. 68, pp. 530-539, 2009.

[Deme93] Demeri, M.Y. (Ed.), “Computer Applications in Shaping and Forming of Materials,” The Journal of The Minerals, Metals & Materials Society, Warrendale, 1993.

[Den94] Deneuville, P. and Lecot, R., “The Study of Friction in Ironing Process by Physical and Numrical Modelling,” Journal of Materials Processing Technology, vol. 45, pp. 625-630, 1994.

Page 6: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-6

[Deu92] Deutsch, D., and Jozsa, R., “Rapid Solution of Problems by Quantum Computation,” In Proceedings of the Royal Society of London, series A, vol. A439, pp. 553–558,1992.

[Din08] Ding, S., Jin, Z., Yang, Q., “Evolving quantum circuits at the gate level with a hybrid quantum-inspired evolutionary algorithm,” Soft Computing, 12(11), pp. 1059–1072, 2008.

[Do04] Do, T. T., Fourment, L., and Laroussi, M., “Sensitivity Analysis and Optimization Algorithms for 3D Forging Process Design, Materials Processing and Design: Modeling, Simulation and Applications,” International Conference on Numerical Methods in Industrial Forming Processes (NUMIFORM’04), copy 712, pp. 2026-2031, 2004.

[Dob07] Dobrzanski, L.A., Sroka, M., Dobrzanski, J., “Application of neural networks to classification of internal damages in steels working in creep service,”Journal of Achievements in Materials and Manufacturing Engineering,Vol. 20, Issues 1-2, pp. 303-306, 2007.

[Dob10] Dobrzanski, L.A., Król, M., “Neural Network Application for Prediction Mechanical Properties of Mg-Al-Zn Alloys,” Archives of Computational Materials Science and Surface Engineering, Vol. 2, Issue 4, pp.181-188, 2010.

[Doe94] Doege, E. and Nagele, H., “FE simulation of the precision forging process of bevel gears,” Annals of CIRP, vol. 43(1), pp. 241-4, 1994.

[Dol87] Doltsinis, J.S., Luginsland, J., Nolting, S., “Some developments in the numerical simulation of metal forming processes,” in Owen, D.R.J. et al. (Eds), Computational Plasticity: Models, Software and Applications, vol. 2, 875-899, 1987.

[Doz98] Dozier, G., Bowen, J., and Homaifar, A., “Solving constraint satisfaction problems using hybrid evolutionary search,” IEEE Transactions on Evolutionary Computation, 2(1), pp. 23–33, 1998.

[Draa10] Draa, A., Meshoul, S., Talbi, H., Batouche, M., “A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem,” The International Arab Journal of Information Technology, Vol. 7, No. 1, pp. 21-27, 2010.

[Du07] Du, J., Tian, Y., Zuo, M., Zhou, Y., “Using quantum immune clone algorithm in the prediction of tourism emergency events,” In: Proc. ICCAS, pp. 2519–2522, 2007.

[Dua04] Duan, X., Velay, X. and Sheppard, T., “Application of finite element method in the hot extrusion of aluminum alloys,” Materials Science and Engineering,A369, pp.66-75, 2004.

[Dug94] Duggirala, R., Shivpuri, R., Kini, S., Ghosh, S., and Roy, S., “Computer Aided approach for Design and Optimisation of Cold forming Sequences for Automotive Parts,” Journal of Materials Processing and Technology, vol. 46, pp.185-198, 1994.

[Ebe95] Eberhart, R. C., and Kennedy, J., “A new optimizer using particle swarm theory,” In Proceedings of 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, Piscataway, NJ, pp. 39–43, 1995.

Page 7: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-7

[Erb79] Erbel, S., “Mechanical Properties and Structure of Extremely Strain hardened Copper,” Metals Technology, 6, pp. 482–486, 1979.

[Esh97] Eshelman, L. J. “Genetic Algorithms,” in Handbook of Evolutionary Computation, New York: Oxford University Press, B1.2:1-B1.2:11, 1997.

[Esp94] Espiga, F., Jugo, A. and Anza, J.J., “Industrial application of the numerical simulation to the design and optimization of forging processes,” Journal of Materials Processing Technology, vol. 45, pp. 81-6, 1994.

[Est06] Estudillo, A. C. M., Martínez, C. H., Estudillo, F. J. M., and Pedrajas, G. C., “Hybridization of evolutionary algorithms and local search by means of a clustering method,” IEEE Transactions on Systems, Man and Cybernetics, Part B, 36(3), pp. 534–545, 2006.

[Fal98] Falco, I. D. “Nonlinear Systems Identification by means of evolutionary optimized Neural Networks,” In Quagliarella D., Periaux J., Poloni C., Winter G. (eds) (1998) Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, John Wiley & Sons Ltd., England, 1998.

[Fan07] Fan, K., Brabazon, A., O’Sullivan, C., O’Neill, M., “Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm,” In: Proc. GECCO, pp. 1983–1990, 2007.

[Fat05] Fatourechi, M., Bashashati, A., Ward, R. K., and Birch, G., “A hybrid genetic algorithm approach for improving the performance of the LF-ASD brain computer interface,” In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP05), Philadelphia, pp. 345–348, 2005.

[Fau94] Fausett, L., “Fundamentals of Neural Networks,” Prentice Hall, Englewood Cliffs, NJ, 1994.

[Fei98] Feiring B. R., T. Sastri, L. S. M. Sim., “A Stochastic Programming Model for Water Resource Planning,” Mathematical and Computer Modelling, Vol. 27(3), pp. 1–7, 1998.

[Feng06] Feng, X., Wang, Y., Ge, H., Zhou, C., Liang, Y., “Quantum-inspired evolutionary algorithm for travelling salesman problem,” Computational Methods, pp. 1363–1367, 2006.

[Fey82] Feynman, R. P., “Simulating physics with computers.” International Journal of Theoretical Physics, vol. 21, no. 6-7, pp., 467-488, 1982.

[Fey86] Feynman, R. P., “Quantum mechanical computers,” Optics News 11. Also in Foundations of Physics, 16(6):507–531, 1986.

[Flo98] Floreano, D., “Evolutionary Mobile Robotics,”In Quagliarella D., Periaux J., Poloni C., Winter G. (eds) (1998) Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, John Wiley & Sons Ltd., England, 1998.

[Fog66] Fogel, L. J., Owens, A. J., and Walsh, M. J., “Artificial Intelligence through Simulated Evolution,” New York: John Wiley, 1966.

[Fog91] Fogel, D. B., “System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling,” Ginn & Co., Needham, MA, 1991.

Page 8: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-8

[Fog94] Fogel, D. B., “An introduction to simulated evolutionary optimization,” IEEE Transaction on Neural Networks, 5(1), pp. 3–14, 1994.

[Fou96a] Foument, L., and Chenot, J.L., “Optimal design for non-steady state metal forming processes-I. Shape optimisation method,” International Journal for Numerical Methods in Engineering, vol. 39(1), pp. 33-50, 1996.

[Fou96b] Foument, L., and Chenot, J.L., “Optimal design for non-steady state metal forming processes-II. Applications of shape optimisation in forging,” International Journal for Numerical Methods in Engineering, vol. 39(1), pp.51-65, 1996.

[Fou96c] Fourment, L., Balan, T., and Chenot, J.L., “Shape optimisation of preforming tools in forging,” Proceedings of the second International Conference on Inverse Problem in Engineering: Theory and Practice, pp. 9-14, 1996.

[Fou97] Fourment, L., Balan, T., and Chenot, J.L., “Optimum design of the hot forging process: a F.E. inverse model with remeshing for large deformations, computational Plasticity- Fundamental and applications,” D.R.J. Owen et al. eds, 5th International Conference Computational Plasticity (CIMNE’1997), Barcelona, pp.804-809, Pineridge Press, 1997.

[Fou98] Fourment, L., Vieilledent, D. and Chenot, J. L., “Shape optimization of the axisymmetric preform tools in forging using a direct differentiation method,” International Journal of Forming Processes, vol. 1(4), pp. 399-423, 1998.

[Fra57] Fraser, A. S., “Simulation of Genetic Systems by Automatic Digital Computers,” Australian Journal of Biological Sciences, vol. 10, pp. 484-491, 1957.

[Fu09] Fu, X., M. Ding, C. Zhou, Y. Sun., “ Multi-threshold image segmentation with improved quantum-inspired genetic algorithm,” In Proceedings of SPIE,volume 7495, pp. 749518, 2009.

[Fue98] Fuente, E., Velazques, Martinez, A., Torres Rojo, J.H., Ramirez-Haldonado, H., RodiguezFronco, C. ,Trinidad-Santor, A., “ Growth and yield forecasting of Pinus virdis Endl in Pueblos Mancomuoador, Iyflan, Oxaca, Ciencia,”For.al-en-Mevico, 23(84), pp. 3-8, 1998.

[Ful00] Fullér, R., Introduction to Neuro-Fuzzy Systems “Advances in Soft Computing Series,” Springer-Verlag, Berlin/Heildelberg, 289 pages, 2000.

[Fuq05] Fuqian, Y., Saran, A., Okazaki, K., “Finite element simulation of equal channel angular extrusion”, Journal of Material Processing Technology,166, pp. 71-78, 2005.

[Fur03] Furukawa, M., Horita, Z., Langdon, T.G., “Factors influencing microstructural development in Equal-Channel Angular Pressing,” Metals and Materials International, 9, pp. 141-149, 2003.

[Fur98] Furukawa, M., Iwahashi, Y., Horita, Z., Nemoto, M., Langdon, T.G., “Microstructural characteristics of ultra-fined grained aluminum using equal channel angular pressing,” Material Science Engineering, A257:328, 1998.

[Gag11] Gagandeep, K., “An Efficient Hybrid Neuro-fuzzy Control Scheme of Synchronous Generator: A Case Study,” Proceedings of the World Congress on Engineering, ISBN: 978-988-19251-4-5, Vol. II, pp. 127-132, 2011.

Page 9: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-9

[Gan04] Ganesh, K., and Punniyamoorthy, M., “Optimization of continuous-time production planning using hybrid genetic algorithms-simulated annealing,” International Journal of Advanced Manufacturing Technology, 26(1), pp. 148–154, 2004.

[Gan05] Ganesh, V., Singhal, G., “Quantum-inspired evolutionary algorithms and binary particle swarm optimization for training MLP and SRN neural networks,” Journal of Computational Theory and Nanoscience, 2(4), pp. 561–568, 2005.

[Gao06] Gao, H., Xu, G., Wang, Z., “A novel quantum evolutionary algorithm and its application,” In: Proc. WCICA, pp. 3638–3642, 2006.

[Gar05] Gardner, P., Wilm, A., Washietl, S., “A benchmark of multiple sequence alignment programs upon structural RNAs,” Nucleic Acids Res., 33, pp. 2433–2439, 2005.

[Gee10] Geertsa S., D. Raesa, M. Garcia, “Using Aqua Crop to Derive Deficit Irrigation Schedules,” Agricultural Water Management, Vol. 98, pp. 213–216, 2010.

[Gel98] Gelin, J.C. and Ghouati, O., “Design optimisation of metal forming processes,” International Conference on Integrated Design and manufacturingin Mechanical Engineering, Compiègne FRANCE, 27-29 May, 499-506, 1998.

[Gol89] Goldberg, D. E., “Genetic algorithms in search, optimization, and machine learning,” New York: Addison-Wesley, 1989.

[Grig01] Grigorenko, I., Garcia, M., “Ground-state wave functions of two-particle systems determined using quantum genetic algorithms,” Physica A, Stat. Mech. Its Appl. 291(1–4), pp. 439–448, 2001.

[Grig02] Grigorenko, I., Garcia, M., “Calculation of the partition function using quantum genetic algorithms,” Physica A, Stat. Mech. It’s Appl. 313(3–4), pp. 463–470, 2002.

[Gro07] Grosan C., Abraham A., “Hybrid Evolutionary Algorithms: Methologies, Architectures and Reviews”, Studies in Computational Intelligence, pp. 1-17, 2007.

[Gro96] Grover, L. K., “A fast quantum mechanical algorithm for database search,” in Proceedings of the 28th ACM Symposium on Theory of Computing, pp. 212-219, 1996.

[Gro97] Grover, L. K., “Quantum Mechanics Helps in Searching for a Needle in a Haystack,” Physical Review Letters, American Physical Society, vol. 79, no.2, pp. 325-328, 1997.

[Gu09] Gu, J., M. Gu, C. Cao, and X. Gu., “A novel competitive coevolutionary quantum genetic algorithm for stochastic job shop scheduling problem,”Computers and Operations Research, 2009.

[Guo07] Guo, R., Li, B., Zou, Y., Zhuang, Z., “Hybrid quantum probabilistic coding genetic algorithm for large scale hardware-software co-synthesis of embedded systems,” In: Proc. CEC, pp. 3454–3458, 2007.

[Gup00] Gupta A. P., R. Harboe, M. T. Tabucanon, “Fuzzy Multiple-Criteria Decision Making for Crop Area Planning in Narmada River Basin,” Agricultural Systems, Vol. 63(1), pp. 1–18, 2000.

Page 10: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-10

[Gup02] Gupta, N. K., G. S. Sekhon, and P. K. Gupta, “A study of fold formation in axisymmetric axial collapse of round tubes,” International Journal of Impact Engineering, vol. 27, pp.87–117, 2002.

[Gup04a] Gupta, N. K., Venkatesh, “Experimental and numerical studies of dynamic axial compression of thin walled spherical shells,” International Journal of Impact Engineering, vol. 30(9), pp.1225-1240, 2004.

[Gup04b] Gupta, N. K., Sekhon, G. S., and Gupta, P. K., “Study of lateral compression of round metallic tubes,” Thin-Walled Structures, vol.43, pp.895–922, 2004.

[Gup06] Gupta, P. K., and Gupta, N. K., “Computational and experimental studies of crushing of metallic hemispherical shells,” Archive of Applied Mechanics, Springer Berlin / Heidelberg, vol. 76(9-10), pp. 511-524, December, 2006.

[Gyl05] Gylienė, V. and Ostaševičius, V., “Study of hydroforming by implementing necking criterion in FEM code,” MECHANIKA, Nr.4 (54) pp.1392 – 1207, 2005.

[Hak09] Hakan, T., Ozalp, V., “A Hybrid Fuzzy Time Series and ANFIS Approach to Demand Variability in Supply Chain Networks,” Journal of Naval Science and Engineering, Vol.5, No.2, pp.20-34, 2009.

[Hak09a] Hakim, S. S. A., Abdel, M. H., “Finite Element Analysis of Sheet Metal Forming Process,” European Journal of Scientific Research, ISSN 1450-216X,Vol.33 No.1, pp.57-69, 2009.

[Hal51] Hall, E.O., Proc. Phys. Soc., Ser. B, 64, pp. 747- 753, 1951.

[Hal94] Halgamuge, S. K. and Glesner, M. “Neural networks in designing fuzzy systems for real world applications”. Fuzzy Sets and Systems, vol. 65, pp. 1–12, 1994.

[Ham11] Hameshbabu, N., “Use of Genetic algorithm based approaches in scheduling of FMS: A Review,” International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 3, pp.1936-1942, 2011.

[Han00] Han, K., Kim, J., “Genetic quantum algorithm and its application to combinatorial optimization problem,” In: Proc. CEC, vol. 2, pp. 1354–1360, 2000.

[Han01] Han, K., Park, K., Lee, C., Kim, J., “Parallel quantum-inspired genetic algorithm for combinatorial optimization problem,” In: Proc. CEC, vol. 2, pp. 1422–1429, 2001.

[Han02] Han, K. H. and Kim, J.H., "Quantum-inspired evolutionary algorithm for a class of combinatorial optimization," IEEE Transactions On Evolutionary Computation, vol. 6(6), pp. 580- 593, 2002.

[Han03a] Han, K., Kim, J., “On setting the parameters of QEA for practical applications: Some guidelines based on empirical evidence,” Lect. Not. Comput. Sci., 2723, pp. 427–428, 2003.

[Han03b] Han, K., Kim, J., “On setting the parameters of quantum-inspired evolutionary algorithm for practical application,” In: Proc. CEC, pp. 178–184, 2003.

Page 11: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-11

[Han04] Han, K. H. and Kim, J.H., "Quantum-inspired evolutionary algorithm with a new termination criterion, Hє gate, and two-phase scheme," IEEE Transactions on Evolutionary Computation, vol. 8(2), pp. 156-169, 2004.

[Han06] Han, K., Kim, J., “On the analysis of the quantum-inspired evolutionary algorithm with a single individual,” In: Proc. CEC, pp. 2622–2629, 2006.

[Hans00a] Hans Raj, K., Sharma, R. S., Srivastava, S. K., Patvardhan, C., “Modelling of Manufacturing processes with ANN for Intelligent Manufacturing,” International Journal of Machine tools and Manufacture, vol. 40(6), pp.851 –868, 2000.

[Hans00b] Hans Raj, K., Sharma, R. S., Srivastava, S. K., “Modeling of Hot Extrusion with Artificial Neural Network”, Journal of Institution of Engineers (India), vol. 81, pp. 49 – 54, 2000.

[Hans00c] Hans Raj, K., Sharma, R. S., Srivastava, S. K., Patvardhan, C., “Optimization of hot extrusion using single objective neuro stochastic search technique,” Proceedings of IEEE International Conference on Industrial Technology 2000,vol.2, pp. 666 – 671, 2000.

[Hans05a] Hans Raj, K., Sharma R. S., Mishra, G. S., Dua, A. and Patvardhan, C., “An Evolutionary Computational Technique for Constrained Optimization in Engineering Design”, Journal of Institution of Engineers(India), vol. 86, pp. 121-128, 2005.

[Hans05b] Hans Raj, K., Sharma R. S., “A Neuro-Hybrid, Stochastic Search Technique with Applications in Agile Manufacturing”, International Journal of Agile Manufacturing, vol. 8(2), 2005.

[Hans92] Hans Raj, K., Fourment, L., Coupez, T., and Chenot, J.L., “Simulation of Industrial forging of axisymmetrical parts,” International Journal of Engineering Computations, vol. 9, pp.575-586, 1992.

[Hans96] Hans Raj, K., Chenot, J. L., Fourment, L., “Finite element modelling of hot metal forming” Indian Journal of Engineering and Material Science, vol. 3, pp. 234-238, 1996.

[Hans98] Hans Raj, K., Mangla P, Bhardwaj P, Patvardhan C. ‘Cutting Force Estimation with Artificial Neural Network Models.’ IE (I) Journal –PR, 79, pp. 21-25, May 1998.

[Hans99] Hans Raj, K., Sharma, R. S., Srivastava, S.K., Patvardhan, C., “Neural Network Modeling and Simulation of Hot Upsetting”, Indian Journal of Engineering and Material Sciences (CSIR), vol. 6, pp. 11-118, 1999.

[Hat10] Hataitep, W., “Modeling of Thermodynamic Properties based on Neuro Fuzzy System for Steam Power Plant,” Proceedings of the World Congress on Engineering and Computer Science, Vol. II, pp. 1045-1048, 2010.

[Hei05] Hein, P., “A global approach to the finite element simulation of hot stamping,” Advanced Materials Research, vol. 6-8, pp.763-770, 2005.

[Her96] Herrera, F., and Lozano, M., “Adaptation of genetic algorithm parameters based on Fuzzy logic controllers,” Genetic Algorithms and Soft Computing,Herrera F, Verdegay JL (Eds.), pp. 95–125. 1996.

Page 12: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-12

[Hey99] Hey, T. “Quantum computing: an introduction,” Computing & Control Engineering Journal, Piscataway, NJ: IEEE Press, vol. 10, no. 3, pp. 105-112, Jun. 1999.

[Hib70] Hibbit, H.D., Marcal, P.V. and Rice, J.R., “Finite Element Formulation for Problems of Large Strain and Large Displacements,” International Journal of Solids and Structures, vol. 6, pp. 1069–1086, 1970.

[Hig97] Higuchi, T., Iwata, M., and Liu, W., (Eds.), “Proceedings of 1st International Conference on Evolvable Systems: From Biology to Hardware (ICES96),”Springer-Verlag, Lecture Notes in Computer Science, 1997.

[Hint99] Hinterding, R., “Representation, constraint satisfaction and the knapsack problem,” In: Proc. CEC, pp. 1286– 1292, 1999.

[Hof05] Hofmann, D.C., Vecchio, K.S., Material Science Engineering, A402, pp. 234-240, 2005.

[Hol10] Hol, J., Cid Alfaro, M.V., De Rooij, M.B., Meinders, T., “Multiscale Friction Modeling for Sheet Metal Forming,” 4th International Conference on Tribology in Manufacturing Processes - ICTMP 2010,Paris, France, pp. 01-10, 2010.

[Hol75] Holland, J. H., “Adaptation in Natural and Artificial Systems,” Ann Arbor: University of Michigan Press, 1975.

[Hor00] Horita, Z., Furukawa, M., Nemoto, M., Langdon, T.G. “The use of severe plastic deformation,” Material Science & Technology, 16, pp. 1239-1245, 2000.

[Hoss10] Hossain M., Hossain, K., Hashem, M. M. A., “A generalized hybrid real-coded quantum evolutionary algorithm based on particle swarm theory with arithmetic crossover,” International journal of computer science & information Technology (IJCSIT), Vol.2, No.4, pp. 172- 187, 2010.

[Hua07] Huang, Y., Tang, C., Wang, S., “Quantum-inspired swarm evolution algorithm,” In: Proc. CISW, pp. 208–211, 2007.

[Hua08] Huangy J. Y., Liao X. Z., Zhu Y. T., “Grain Boundary Structure of Nanocrystalline Cu Processed by RCS” Philosophical Magazine, Vol. 83, No. 12, pp.1407–1419, 2008.

[Huo06] Huo, H., Stojkovic, V., “Two-phase quantum based evolutionary algorithm for multiple sequence alignments,” In: Proc. ICCIAS, pp. 374–379, 2006.

[Huo07] Huo, H., Stojkovic, V., “Two-phase quantum based evolutionary algorithm for multiple sequence alignment,” In: Lecture Notes in Artificial Intelligence, vol. 4456, pp. 11–21, 2007.

[Hyo02] Hyoung S. K. Min H. S., Sun I. H., “Finite element analysis of equal channel angular pressing of strain rate sensitive metals”, Journal of Materials Processing Technology, pp. 497-503, 2002.

[Ima08] Imabeppu, T., Nakayama, S., Ono, S., “A study on a quantum-inspired evolutionary algorithm based on pair swap,” Artificial Life Robot, 12(1), pp. 148–152, 2008.

[Iva09] Ivan S., Anna K., Lucia I., Vaclav S., “Grain and subgrain boundaries in ultrafine-grained materials”, Materials Characterization, pp. 1163-1167, 2009.

Page 13: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-13

[Jan93] Jang, J.S.R., “ANFIS: Adaptive-Network-Based Fuzzy Inference Systems,”IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp.665–685, 1993.

[Jang03] Jang, J.S., Han, K.H., Kim, J.H., “Quantum-inspired evolutionary algorithm-based face verification,” In: Lecture Notes in Computer Science, vol. 2724, pp. 2147–2156, 2003.

[Jang04a] Jang, J.S., Han, K.H., Kim, J.H., “Evolutionary algorithm-based face verification,” Pattern Recognition Lett., 25(16), pp.1857–1865, 2004.

[Jang04b] Jang, J.S., Han, K.H., Kim, J.H., “Face detection using quantum-inspired evolutionary algorithm,” In: Proc. CEC, pp. 2100–2106, 2004.

[Jang09] Jang, S.H., Jung, Y.W., Kim, W., Shin, J.R., Park, J.B., “A thermal unit commitment approach based on a bounded quantum evolutionary algorithm,” Transactions Korean Institute of Electrical Engineering, 58(6), pp. 1057–1064, 2009.

[Jas10] Jassar, S. Liao, Z., Zhao, L., “Data Quality in Hybrid Neuro-Fuzzy based Soft-Sensor Models: An Experimental Study,” IAENG International Journal of Computer Science, 37:1, IJCS_37_1_08, pp.234-239, 2010.

[Jeo05] Jeong, S. J., Lim, S. J., and Kim, K. S., “Hybrid approach to production scheduling using genetic algorithm and simulation,” International Journal of Advanced Manufacturing Technology, 28(1), pp. 129–136, 2005.

[Jeo09] Jeong, Y.W., Park, J.B., Shin, J.R., Lee, K.Y., “A thermal unit commitment approach using an improved quantum evolutionary algorithm,” Electrical Power Components Systems, 37(7), pp. 770–786, 2009.

[Jia08] Jiao, L., Li, Y., Gong, M., Zhang, X., “Quantum-inspired immune colonel algorithm for global optimization,” IEEE Trans. Syst. Man Cybern., Part B, Cybern., 38(5), pp. 1234–1253, 2008.

[Joh09] John, V., John, P., “Reactive power and voltage control based on general quantum genetic algorithms,” Expert Syst. Appl., 36(3), pp. 6118–6126, 2009.

[Joh82] Johnson, W., Sowerby, R., and Venter, R.D., "Plane-Strain Slip-Line Fieldsfor Metal Deformation Processes: A Source Book and Bibliography,”Pergamon Press, New York, pp. 64-158, 1982.

[Jun03] Jung, S. and Wen John, T., “Nonlinear model predictive control for the swing up of a rotary inverted pendulum,” Rensselaer Polytechnic Institute, Troy, NY, 12180, USA, April 2003.

[Kai09] Kaibyshev R. O., Mazurina I. A., Gromov D. A., “Mechanisms of grain refinement in aluminium alloys in the process of severe plastic deformation”, Metal Science and Heat Treatment, vol. 48, pp. 14-19, 2009.

[Kal11] Kalbande, D.R., Priyanka, S., “An Advanced Technology Selection Model using Neuro Fuzzy Algorithm for Electronic Toll Collection System,”International Journal of Advanced Computer Science and Applications, Vol. 2, No. 4, pp. 97-104, 2011.

[Kam03] Kamachi, M., Furukawa, M., Horita, Z., Langdon, T.G., “An Experimental Investigation of the Shearing Characteristics in Equal-Channel Angular Pressing,” Materials Science and Engineering A, 347, pp. 223-230, 2003.

Page 14: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-14

[Kan10] Kannan N., T. Senthivel, A. Rayar, M. Frank, “Investigating Water Availability for Introducing an Additional Crop Yield in Dry Season on Hill Land at Rubirizi, Rwanda,” Agricultural Water Management, Vol. 97, pp. 623–634, 2010.

[Kat09] Katerina S., Michal B. Tibor K., “Verification of hall-petch equation of nanocrystalline copper”, Metal, pp. 19-24, 2009.

[Ken95] Kennedy, J., and Eberhart, R. C., “Particle swarm optimization,” In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948, 1995.

[Ken97] Kennedy, J., “The particle swarm: social adaptation of knowledge,” In: Proceedings of IEEE International Conference on Evolutionary Computation,Indianapolis, IN, 1997, pp. 303–308, 1997.

[Kha97] Khalid, H. and Obadait, M.S., “Simulation of new neural network-based cache replacement scheme,” Simulation Journal, vol. 68(4), pp.209 – 218, 1997.

[Khor05] Khorsand, A., “Quantum gate optimization in a meta-level genetic quantum algorithm,” In: Proc. IEEE SMC, pp. 3055–3062, 2005.

[Khor06] Khorsand, A., “Genetic quantum algorithm for voltage and pattern design of piezoelectric actuator,” In: Proc. CEC, pp. 2593–2600, 2006.

[Kim03] Kim, K.H., Hwang, J.Y., Han, K.H., Kim, J.H., Park, K.H., “A quantum-inspired evolutionary computing algorithm for disk allocation method,” IEICE Trans. Inf. Syst., E86, D (3), pp. 645–649, 2003.

[Kim05] Kim, D. H., and Cho, J. H., “Robust tuning of PID controller using bacterial-foraging based optimization,” JACIII , 9(6), pp. 669–676, 2005.

[Kim06] Kim, Y., Kim, J.H., Han, K.H., “Quantum-inspired multiobjective evolutionary algorithm for multiobjective 0/1 knapsack problems,” In: Proc. CEC, pp. 2601–2606, 2006.

[Kir83] Kirkpatrick, S., Gelatt, C.D., and Vecchi, M.P., “Optimisation by Simulated Annealing,” Science, vol. 220(4598), pp.671-680, May 1983.

[Kob84] Kobayashi, S., “The role of the finite-element method in metal forming technology,” Advance Techniques. Plastics, vol. 2, pp. 1035-40, 1984.

[Kob85a] Kobayashi, S., “Metal forming and the finite element method - past and future,” Proceedings of 25th International Machine Tool Design Research Conference, Birmingham, UK, pp. 17-32, 1985.

[Kob85b] Kobayashi, S., “Recent developments on the application of the finite element method to metal forming problems,” Innovative Material Processing, Plenum Press, pp. 187-208, 1985.

[Kob89b] Kobayashi, S., Oh, S. I., Altan, T., “Metal forming and the finite element method,” New York, Oxford University Press, 1989.

[Koz03] Koza, J. R., Keane, M. A., Streeter, M. J., Mydlowec, W., Yu, J., and Lanza, G., “Genetic programming IV: Routine human-competitive machine intelligence,” Kluwer, Dordecht, 2003.

Page 15: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-15

[Koz04] Kozlov E. V., Zhdanov A. N., Popova N. A., Pekarskaya E. E., Koneva N. A., “Subgrain structure and stress fields in UFG materials: problem of Hall-Petch relation”, Materials Science and Engineering, pp. 789-794, 2004.

[Koz05] Koza, J.R., Al-Sakran, S.H., Jones, L.W., “Cross-domain features of runs of genetic programming used to evolve designs for analog circuits, optical lens systems, controllers, antennas, mechanical systems, and quantum computing circuits,” In: Proc NASA/DoD EH, pp. 205–212, 2005.

[Koz92] Koza, J. R., “Genetic Programming: On the Programming of Computers By Means of Natural Selection,” MIT Press, Cambridge, MA, 1992.

[Koz94] Koza, J. R., “Genetic Programming II: Automatic Discovery of Reusable Programs,” MIT Press, Cambridge, MA, 1994.

[Koz95] Koza, J. R., “Survey of Genetic Algorithms and Genetic Programming,” in Proceedings of the 1995 WESCON conference, Piscataway, NJ: IEEE Press, pp. 589-594, 1995.

[Koz99] Koza, J. R., Bennett, F. H., Andre. D., and Keane, M. A., “Genetic programming III: Darwinian invention and problem solving,” Morgan Kaufmann, Los Altos, CA, 1999.

[Kri10] Krishnaiah, A., Kumaran, K., Chakkingal, U., “Finite Element Analysis of Multi-Pass Equal Channel Angular Extrusion/Pressing Process,” Materials Science Forum Vols. 654-656, pp. 1574-1577, 2010.

[Kru94] Kruse, R., Gebhardt, J., and Klawonn, F., “Foundations of Fuzzy Systems”, Wiley Chichester, 1994.

[Kun93] Kung, S.Y., “Digital Neural Networks”, Englewood Cliffs, NJ: Prentice Hall, 1993.

[Kus89] Kusiak, J. and Thompson, E.G., “Optimization techniques for extrusion die shape design,” in Mattiasson, K., Samuelsson, A., Wood, R.D. and Zienkiewicz, O.C. (Eds), International Conference on Numerical Methods in Industrial Forming Processes (NUMIFORM’89), A.A. Balkema, Rotterdam and Boston, MA, pp. 569-74, 1989.

[Kus94] Kusiak, J., Pirtryzk, M. and Chenot, J. L., “Die Shape Design and Evaluation of Microstructure Control in the Closed-die Axisymmetric Forging by Using FORGE2 Program,” The Iron and Steel Institute of Japan - ISIJ International,vol. 34, No. 9, pp. 755-760, ISSN 0915-1559, 1994.

[Kva08] Kvackaj T., Kocisko R., Besterci M., T. Donic, I. Pokorny, T. Kuskulic, M. Molnarova, Kovacova A., “Influence of SPD by ECAP on Cu properties”, Materials Science Forum, pp. 310-314, 2008.

[Lai07] Lai, W.L., Dipti, S., “A Hybrid Evolutionary Algorithm for Dynamic Route Planning,” IEEE Congress on Evolutionary Computation (CEC 2007), pp.4743-4749, 2007.

[Lam07] Lamien, I.N., Tigabu, M., Guinko, S. and Oden, P.C., “Variations in Dendrometric and fruiting characters of Vitellaria Paradoxa populations and multivariate models for estimation of fruit yield,” Agro-For. Sys., Vol. 69(1), pp. 1-11, 2007.

Page 16: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-16

[Lan06] Langdon T. G. and Valiev R. Z., “Principles of equal-channel angular pressing as a processing tool for grain refinement”, Progress in Material Science, pp. 881-981, 2006.

[Lan07] Langdon T.G., “The principles of grain refinement in equal channel angular pressing”, Material Science and Engineering, A462, pp. 32-42, 2007.

[Lang07] Langdon, T.G., “The Principles of Grain Refinement in Equal-Channel Angular Pressing,” Materials Science and Engineering, A462, pp. 3-11, 2007.

[Lau09] Lau, T.W., Chung, C.Y., Wong, K.P., Chung, T.S., Ho, S.L., “Quantum-inspired evolutionary algorithm approach for unit commitment,” IEEE Transactions on Power Systems, 24(3), pp. 1503–1512, 2009.

[Lav08] Lavandar, S., Nigam, M. J., “Adaptive Neuro-Fuzzy Inference System based control of six DOF robot manipulator,” Journal of Engineering Science and Technology, Review 1, pp. 106-111, 2008.

[Law11] Lawrynowicz, A., “A Survey of Evolutionary Algorithms for Production and Logistics Optimization,” Research in Logistics and Production, ISSN 2083-4950 (Online), 2011.

[Lee03] Lee, M. A., and Takagi, H., “Dynamic control of genetic algorithms using fuzzy logic techniques,” In Forrest S (Ed.), Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufmmann, San Mateo, pp 76–83, 2003.

[Lek04] Lekkas, D.F., Onof, C., Lee, M. J., Baltas, E.A., “Application of Artificial Neural Networks for Flood Forecasting,” Global Nest: The International Journal, Vol. 6, No.3, pp. 205-211, 2004.

[Li04] Li, Y., Zhang, Y.N., Zhao, R.C., Jiao, L.C., “An edge detector based on parallel quantum-inspired evolutionary algorithm,” In: Proc. ICMLC, pp. 4062–4066, 2004.

[Li04a] Li, Y., Jiao, L., Liu, F., “Self-adaptive chaos quantum colonel evolutionary programming,” In: Proc. ICSP, vol. 2, pp. 1550–1553, 2004.

[Li04c] Li, Y., Zhang, Y.N., Zhao, R.C., Jiao, L.C., “The immune quantum-inspired evolutionary algorithm,” In: Proc. IEEE, ICSMC, pp. 3301–3305, 2004.

[Li05] Li, Y., Jiao, L., “Quantum-inspired immune colonel algorithm,” In: Lecture Notes in Computer Science, vol. 3627, pp. 304–317, 2005.

[Li05a] Li, N., Du, P., Zhao, H.J., “Independent component analysis based on improved quantum genetic algorithm: Application in hyperspectral images,” In: Proc. IGARSS, pp. 4323–4326, 2005.

[Li05b] Li, Y., Zhang, Y., Cheng, Y., Jiang, X., Zhao, R., “A novel immune quantum-inspired genetic algorithm,” In: Lecture Notes in Computer Science, vol. 3612, pp. 215–218, 2005.

[Li06] Li, Y., Jiao, L., Gou, S., “Quantum-inspired immune colonel algorithm for multiuser detection in DS-CDMA systems,” In: Lecture Notes in Computer Science, vol. 4247, pp. 80–87, 2006.

[Li06] Li, Y., Liu, F., “A novel immune colonel algorithm,” In: Lecture Notes in Computer Science, vol. 4222, pp. 31–40, 2006.

Page 17: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-17

[Li07] Li, Y., Jiao, L., “Quantum-inspired immune colonel multiobjective optimization algorithm,” In: Lecture Notes in Artificial Intelligence, vol. 4426, pp. 672–679, 2007.

[Li07] Li, B.B., Wang, L., “A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling,” IEEE Trans. Syst. Man Cybern., Part B, Cybern.,37(3), pp. 576–591, 2007.

[Li08] Li, P., Li, S., “Quantum-inspired evolutionary algorithm for continuous space optimization based on Bloch coordinates of qubits,” Neurocomputing, 72(1–3), pp. 581–591, 2008.

[Li09] Li, Z., Rudolph, G., Li, K., “Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms,” Applied Computational Mathematics, 57(11–12), pp. 1843–1854, 2009.

[Li97] Li F, Morgan R, Williams, D., “Hybrid genetic approaches to ramping rate constrained dynamic economic dispatch,” Electric Power Systems Research, 43(11), pp. 97–103, 1997.

[Lia98] Liao, T. W., & Chen, L. J., “Manufacturing Process Modelling and Optimisation Based on Multi-Layer Perceptron Network,” Journal of Manufacturing Science and Engineering, vol. 120, pp.109–119, February 1998.

[Lin04] Lin, Y. C., Hwang, K. S., and Wang, F. S., “A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems,” Computers and Mathematics with Applications, 47(8–9), pp. 1295–1307, 2004.

[Lin96] Lin, C. T. and Lee, C. C., “Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems,” Prentice Hall, New York, 1996.

[LinCT91] Lin, C. T. and Lee, C. S. G., “Neural-network based fuzzy logic control and decision system,” IEEE Transaction on Computers, vol. 40(12), pp.1320–1336, 1991.

[Lio97] Liou, J. H. & Jang, D. Y., “Forging parameter optimisation considering stress distributions in products through FEM analysis and robust design methodology,” International Journal of Machine Tools and Manufacture, vol. 37, no. 6, pp.775-782, 1997.

[Liu05] Liu, H., Zhang, D., Yan, J.Q., Li, Z.S., “Fast and robust portrait segmentation using QEA and histogram peak distribution methods,” In: Lecture Notes in Computer Science, vol. 3645, pp. 920–928, 2005.

[Liu06] Liu, F., Li, S.Q., Liang, M., Hu, L.Z., “Wideband signal DOA estimation based on modified quantum genetic algorithm,” IEICE Transactions on Fundamental Electronics Communication and Computer Science, E89A(3), pp. 648–653, 2006.

[Lo00] Lo, C.C and Chang, W.H., “A multi objective hybrid genetic algorithm for the capacitated multipoint network design problem,” IEEE Transactions on Systems, Man and Cybernetics - Part B, 30(3), pp. 461–470, 2000.

[Liu08] Liu, H., Zhang, G., Liu, C., Fang, C., “A novel memetic algorithm based on real-observation quantum inspired evolutionary algorithms,” In: Proc. ISKE,pp. 486–490, 2008.

Page 18: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-18

[Lor98] Lorenzo, D. I., Micari, F., “An inverse approach for the design of optimal preform shape in cold forming,” Proceedings of the 1st ESAFORM Conference on Metal forming, Sophia Antipolis, France, 1998.

[Lu08] Lu, T.C., Juang, J.C., Yu, G.R., “On-line outliers detection by neural network with quantum evolutionary algorithm,” In: Proc ICICIC, pp. 254–257, 2008.

[Lug09] Lugo N., J. M. Cabrera, N. Llorca, C. J. Luis, R. Luri, J.Leon, Puertas I., “Grain refinement of pure copper by ECAP”, Materials Science Forum, pp. 393-401, 2009.

[Luj08] Lujic, R., Saric, T., “Application of Genetic Algorithm to the Technological Operations Scheduling Problem,” Metalurgija, Vol. 47, Issue 2, pp.103-107, 2008.

[Luo08] Luo, Z., Wang, P., Li, Y., Zhang, W., Tang, W., Xiang, M., “Quantum-inspired evolutionary tuning of SVM parameters,” Progr. Nat. Sci., 18(4), pp. 475–480, 2008.

[Lv07] Lv, Y.J., Liu, N.X., “Application of quantum genetic algorithm on finding minimal reduction,” In: Proc. GrC, pp. 728–733, 2007.

[Maci07] Maciejewski, J., Kopec, H., Petryk, H., “Finite Element Analysis of Strain Non-Uniformity in Two Processes of Severe Plastic Deformation”, Engineering Transactions 55, 3, pp. 197–216, 2007.

[Mad07] Madej, L., Hodgson, P. D. and Pietrzyk, M., “Comparison of the strain Distribution from Multi scale and conventional Approaches to modeling extrusion,” Solid State Phenomenon, Transaction Technology Publications, Switzerland, vol. 129, 2007, pp.25-30, 2007.

[Maj07] Majchrzak, E., Mendakiewicz, J., Paruch, M., “Application of Evolutionary Algorithms in Identification of Solidification Parameters,” Journal of Achievements in Materials and Manufacturing Engineering, Vol. 23 Issue 2, pp.67-70, 2007.

[Mal04] Malossini, A., Blanzieri, E., Calarco, T., “QGA: a quantum genetic algorithm,” Technical Report No. DIT04-105, Informatica e lecommunicazioni, University of Trento, 2004.

[Mal08] Malossini, A., Blanzieri, E., Calarco, T., “Quantum genetic optimization,” IEEE Trans. Evol. Comput., 12(2), pp. 231–241, 2008.

[Man97] Mainuddin, M., A. D. Gupta, P. R. Onta., “Optimal Crop Planning Model for an Existing Ground water Irrigation Project in Thailand,” Agricultural Water Management, Vol. 33, pp. 43–62, 1997.

[Mart98] Martinez, A., Benavente, R., “The AR face database,” http://rvl1.ecn.purdue.edu/~aleix/aleixfaceDB.html,1998.

[Med08] Medeiros, N., Lins, J.F.C., Moreira, L.P., Gouvea, J.P., “The role of the friction during the equal channel angular pressing of an IF-steel billet”, Materials Science and Engineering A, 489, pp. 363–372, 2008.

[Men05] Menon, P. P., Bates, D. G. and Postlethwaite, I., “Hybrid evolutionary optimization methods for the clearance of nonlinear flight control laws,” Proceedings of the 44th IEEE Conference on Decision and Control, Seville, Spain, pp. 4053–4058, 2005.

Page 19: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-19

[Mes05a] Meshoul, S., Layeb, A., Batouche, M., “A quantum evolutionary algorithm for effective multiple sequence alignment,” In: Lecture Notes in Artificial Intelligence, vol. 3808, pp. 260–271, 2005.

[Mes05b] Meshoul, S., Mahdi, K., Batouche, M., “A quantum inspired evolutionary framework for multi-objective optimization,” In: Lecture Notes in Artificial Intelligence, vol. 3808, pp. 190–201, 2005.

[Mil04] Milenin, A.A., Berski, Banaszek, S. G. and Dyja, H., “Theoretical analysis and optimisation of parameters in extrusion process of explosive cladded bimetallic rods,” Journal of Materials Processing Technology, vol. 157-158, pp.208-212, 20 December 2004.

[Mill98] Miller, F. J., Thomson, P. and Fogarty, T., “Designing Electronic circuits using Evolutionary Algorithms,” In Quagliarella D., Periaux J., Poloni C., Winter G. (eds) (1998) Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, John Wiley & Sons Ltd., England, 1998.

[Min04] Ming, Z., Yan, Z., Ni, Y., Li, G., “An ARIMA approach to forecasting electricity Price with accuracy improvement by predicted errors,” Power Engineering Society General Meeting, 1: 233–238, 2004.

[Min11] Ming, K. C., Kuang, H. P., Chang, H. H., Kuo, H. C., “The Application of Artificial Neural Network Model on the Establishment of Configuration Identification,” International Journal of Electronic Business Management, Vol. 9, No.3, pp. 267-278, 2011.

[Mish05] Mishra, R.S., Ma, Z.Y., Material Science Engineering, R50, pp. 120-125, 2005.

[Moh07] Mohammadzaheri, M., Mirsepahi,A., Asef-afshar, O., Koohi H., “Neuro-Fuzzy Modeling of Superheating System of a Steam Power Plant,” AppliedMathematical Sciences, Vol. 1, No. 42, pp. 2091-2099, 2007.

[Mon86] Monostori, L., “Learning Procedures in Machine Tool Monitoring,” Computers in Industry, vol. 7, pp. 53-64, 1986.

[Mon92] Monostori, L., “Artificial Neural Networks in Intelligent Manufacturing, Robotics and Computer Integrated Manufacturing,” Pergamon Press, vol. 9(6), pp. 421– 437, 1992.

[Moor95] Moore, M., Narayanan, A., “Quantum-inspired computing,” Technical Report, Department of Computer Science, University Exeter, Exeter, UK, 1995.

[Mor77] More, J. J., “The Levenberg-Marquardt Algorithm: Implementation and theory, Numerical Analysis”, G. A. Watson (Ed), Lecture Notes in Mathematics, Springer Verlag, 630, pp.105 – 116, 1977.

[Mun97] Munetomo, M., Takai, Y. & Sato, Y., “An adaptive network routing algorithm employing genetic operators Proc,” 7th International Conference on Genetic Algorithms (ICGA97), Michigan State University, East Lansing, Michigan, USA, pp.643-649,1997.

[Mur10] Murali, R.V., Puri, A.B., Prabhakaran, G., “Artificial Neural Networks based Predictive Model for Worker Assignment into Virtual Cells,” International Journal of Engineering, Science and Technology, Vol. 2, No.1, pp. 163-174, 2010.

Page 20: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-20

[Mus08] Mustafa,T., Halil,D., Ugur,C., “Artificial Neural Network (ANN) Approach to Prediction of Diffusion Bonding Behavior (Shear Strength) of Ni-Ti Alloys Manufactured by powder metallurgy method,” Mathematical and Computational Applications, Vol. 13, No. 3, pp. 183-191, 2008.

[Myu96] Myung, H., and Kim, J., “H Hybrid evolutionary programming for heavily constrained problems,” Biosystems, 38(1), pp. 29–43, 1996.

[Naa07] Naadimuthu, G., Liu, D.M., Lee. E.S., “Application of an Adaptive Neural Fuzzy Inference System to Thermal Comfort and Group Technology Problems,” Computers and Mathematics with Applications, 54, pp. 1395–1402, 2007.

[Nag09] Nagasekhar A. V., Yoon,S. Tick-Hon C. Y., Kim H. S., “An experimental verification of the finite element modelling of equal channel angular pressing”, Computational Materials Science, pp. 347-351, 2009.

[Nag97] Nagaraju, C. H., and Venkateswara, P. Rao., “Estimation of Cutting Forces using Artificial Neural Network,” Proceedings of the 17th All India Manufacturing Technology Design and Research Conference, Warangal, Allied, pp. 595-599,1997.

[Nah09] Nahed E., Farouk A., Mohamed A. H. Mohamed I. A., “Effect of Cu content and number of passes on evolution of microstructure and mechanical properties of ECAPed Al/Cu alloys”, Materials Science and Engineering, A517, pp. 46-50, 2009.

[Nara96] Narayanan, A., Moore, M., “Quantum-inspired genetic algorithms,” In: Proc. CEC, pp. 61–66, 1996.

[Nau96a] Nauck, D. and Kruse, K., “Neuro-fuzzy systems research and applications,” outside of Japan. (in japanese). In Umano M., Hayashi I., and Furuhashi T., editors, Fuzzy-Neural Networks (in Japanese), Soft Computing Series, 108–134. Asakura Publ., Tokyo, 1996.

[Nau96b] Nauck, D. and Kruse, K., “Designing neuro-fuzzy systems through backpropagation,” In Witold Pedrycz, editor, Fuzzy Modelling: Paradigms and Practice, Kluwer, Boston pp. 203–228, 1996.

[Nau97] Nauck, D., Klawonn, F. and Kruse, R., “Foundations of Neuro-Fuzzy Systems,” Wiley, New York, 1997.

[Nem98] Nemoto, M., Horita, Z., Furukawa, M., Langdon, T.G., “The use of severe plastic deformation tool for microstructural control,” Metals and Materials, 4, pp. 1181-1190, 1998.

[New04] Newaz. M.S. and Millat-e-Mustafa, M., “Growth and Yield prediction models for Acacia mangium grown in the plantations of the central region ofBangladesh,” New For., Vol. 27(1), pp. 81-88, 2004.

[Nie00] Nielsen, M. A. and Chuang, I. L., Quantum Computation and Quantum Information. Cambridge University Press, 2000.

[Niu09] Niu, Q., Zhou, T., Ma, S., “A quantum-inspired immune algorithm for hybrid flow shop with makespan criterion,” Journal of Universal Computer Science,15(4), pp. 765–785, 2009.

Page 21: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-21

[Not98] Notredame, C., Holm, L., Higgins, D., “An objective functions for multiple sequence alignments,” Bioinformatics, 14, pp. 407–422, 1998.

[Oba93] Obaidat, M.S., and Abu-Saymeh, D., “Methodologies for characterizing ultrasonic transducers using neural networks and pattern recognition techniques,” IEEE Transaction Industry Electron, vol. 40, pp. 235–242, 1993.

[Oba98] Obaidat, M. S., “Editorial Artificial Neural Networks to Systems, Man, and Cybernetics: Characteristics, Structures, and Applications,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 28(4), pp.489–495, 1998.

[Orl09] Orlov,D., Beygelzimer,Y., Synkov S.,VaryukhinV., Tsuji N., Horita, Z., “Microstructure Evolution in Pure Al Processed with Twist Extrusion”, Materials Transactions, Vol. 50, No.1, pp. 96-100, 2009.

[Osk90] Oskada, K. and Yang, G. B., “Neural network for process planning of cold forging,” Annals of CIRP, 40(1), pp. 480-489, 1990.

[Osk91] Oskada, K. and Yang, G., “Application of neural network to an expert system for cold forging,” International Journal on Machine Tools and Manufacture, vol. 31(4), pp.577-587, 1991.

[Owe95] Owen, D.R.J. and Onate, E. (Eds.), Computational Plasticity, Fundamentals and Applications, Proceedings of the 4th International Conference on Computational Plasticity, Pineridge Press, Swansea, U.K., 1995.

[Ozc02] Ozcalik, H.R. and Kucuktufekci, A., “An efficient neural controller for a DC servo motor by using ANN and PLR identifiers,” IEEE International Conference on Artificial Intelligence Systems, pp. 224–227, 2002.

[Pan07] Pan, G.F., Xia, K.W., Dong, Y., Shi, J., “An improved LS-SVM based on quantum PSO algorithm and its application,” In: Proc. ICNC, pp. 606–610, 2007.

[Pas95] Pasquinelli, G., “Simulation of metal-forming processes by the finite element method,” International Journal of Plasticity, vol. 11 No. 5, pp. 623-51, 1995.

[Pass02] Passino, K., M., “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems Magazine, 22(3), pp. 52–67, 2002.

[Pat99] Patrick, U., “Optimal extrusion die design to achieve flow balance,” International Journal on Machine Tools and Manufacture, vol. 39, pp.1047-1064, 1999.

[Pen04] Peng, Z and Sheppard, T, “Individual influence of forming parameters on surface recrystallization during aluminium extrusion,” Journal of Modeling and Simulation in Materials Science and Engineering, vol.12, pp.43-57, 2004.

[Pet53] Petch, N.J., Journal of Iron and Steel Institute, pp. 25-28,1953

[Pin94] Pinol, A.A., 1994, “Yield Prediction models for Teak (Tectona grandis Linn.),” Sylvatrop, Vol. 4, pp. 65-80, 1994.

[Plat07] Platelt, M.D., Schliebs, S., Kasabov, N., “A versatile quantum-inspired evolutionary algorithm,” In: Proc.CEC, pp. 423–430, 2007.

[Poh07] Pohlak, M., Majak, J., Küttner, R., “Incremental Sheet Forming Process Modeling - limitation analysis” Journal of Achievements in Materials and Manufacturing Engineering, volume 22, Issue 2, pp. 67-70, 2007.

Page 22: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-22

[Por97] Porto, V.W. “Evolutionary Programming,” in Handbook of Evolutionary Computation, New York: Oxford University Press, B1.4:1-B1.4:10, 1997.

[Por98] Porter, B. “Evolutionary synthesis of control policies for manufacturing systems,” Eds. Quagliarella D., Periaux J., Poloni C., Winter G. , Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, John Wiley & Sons Ltd., England, 1998.

[Pot06] Pötz, W., Fabian, J., “Quantum Coherence: from Quarks to Solids,” Springer, Berlin, 2006.

[Pri94] Prior, A.M., “Applications of implicit and explicit finite element techniques to metal forming,” Journal of Materials Processing Technology, vol. 45, pp. 649-56, 1994.

[Qin07] Qin, C., Zheng, J., Lai, J., “A multi agent quantum evolutionary algorithm for global numerical optimization,” In: LNBI, vol. 4689, pp. 380–389, 2007.

[Rah09] Rahul, S.S., Vikas, U., Hans Raj, K., “Neuro fuzzy Modeling of Hot Extrusion,” Indian Journal of Engineering and Material Sciences, Vol.16, pp.86-92, 2009.

[Raha00] Rahaman, M.M. and Ahmad, L.V., “Growth and yield prediction model of jamoon (Gmelina arbonea) in Chittagong hill tracks Bangladesh,” J. Tropical For. Sci., Vol. 12 (2), pp. 276-285, 2000.

[Rai07] Raizada, A., Rao, M.S.R.M., Nambiar, K.T.N. and Padmaiah, H., “Bio-mass production and prediction models for Acacia nilotica in salt affected vertisols in Karnataka,” Indian For., Vol. 133(2), pp. 239-246, 2007.

[Raju06] Rajua K.S., D.N.Kumarb, L.Duckstein, “Artificial Neural Networks and Multicriterion Analysis for Sustainable Irrigation Planning,” Computers & Operations Research, Vol. 33, pp. 1138–1153, 2006.

[Ran89] Rangwala, S. and Dornfeld, D.A., “Learning and optimisation of machining operations using computing abilities of neural network,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 19(2), pp.299 – 314, 1989.

[Ras09] Rasit, A., “An Adaptive Neuro-Fuzzy Inference System Approach for Prediction of Power Factor in Wind Turbines,” Journal of Electrical & Electronics Engineering, Vol. 9, No.1, pp. 905-912, 2009.

[Rav08] Ravi K. R., Saravanan M., Pillai R. M., Mandal A., Murthy B. S., Chakraborty M., Pai B. C., “Equal channel angular pressing of Al-5 wt% TiB2 in situ composite”, Journal of Alloys and Compounds, vol. 459, pp. 239-243, 2008.

[Rec73] Rechenberg, I., “Evolution Strategies,” Stuttgart, Germany: Frommann-Holzbog, 1973.

[Rep05a] Repalle, J., and Grandhi, R. V., “Design of Forging Process Variables under uncertainties,” Journal of Materials Engineering and Performance, vol. 14(1), pp. 123-131, Feb 2005.

[Rep05b] Repalle, J., Grandhi, R. V., “Reliability-Based Preform Shape Design in Forging,” Communications in Numerical Methods in Engineering, vol. 21(11), pp. 607-617, 2005.

Page 23: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-23

[Rich99] Richert, M., Liu, Q., Hansen, N., Material Science Engineering, A260, pp. 275- 282, 1999.

[Rie00] Rieffel, E., and Polak, W., “An Introduction to Quantum Computing for Nonphysicists,” ACM Computing Surveys, 2000.

[Riz06] Rizvi, R.H., Diwakar and Khare, “Prediction model for timber weight of populus deltoids planted on farmlands in Haryana,” Indian J. Agro. For., Vol. 8(1), pp. 77-85, 2006.

[Rob09] Roberto, B., Figueiredo Paulo R., Cetlin Terence G., Langdon, T.G., “The evolution of damage in perfect-plastic and strain hardening materials processed by equal-channel angular pressing”, Materials Science and Engineering A, doi:10.1016/j.msea.2009.04.007, 2009.

[Rod95] Rodic, T. and Owen, D.R.J., “Finite element technique as a decision support system in the design of bulk metal forming technologies,” in Owen, D.R.J. (Ed.), 4th International Conference on Computational Plasticity, Pineridge Press, pp. 2341-2348, 1995.

[Ros02] Rosochowski, L. Olejnik, Numerical and physical modelling of plastic deformation in 2-turn equal channel angular extrusion. Journal of Material Processing Technology, 125-126, pp. 309-316, 2002.

[Ros04] Rosochowski, L. Olejnik, R. “FEM analysis of two-turn equal channel angular extrusion of cylindrical billets”, Proceedings of the 7th International Conference on Material Forming, Trondheim, Norway, Norwegian University of Science and Technology, pp. 207-210, 2004.

[Ros07] Rosochowski, L. Olejnik, “Finite element simulation of severe plastic deformation processes”, Proc. I Mech E Part L: J. Materials: Design and Applications, 221/4, pp. 187-196, 2007.

[Roy97] Roy, S., Ghosh S., and Shivpuri, R., “A new approach to optimal design of multi-stage metal forming processes with micro genetic algorithms,”International Journal of Machine Tools and Manufacture, vol. 37, no 1, pp.29-44, 1997.

[Rud97] Rudolph, G. “Evolution Strategies,” in Handbook of Evolutionary Computation, New York: Oxford University Press, B1.3:1-B1.3:6, 1997.

[Rut89] Rutenbar, R.A., “Simulated Annealing Algorithms: An Overview,” IEEE Circuits and Devices Magazine, pp.19-26, January 1989.

[Ryla00] Rylander, B., Soule, T., Foster, J., Alves-Foss, J., “Quantum genetic algorithms,” In: Proc. GECCO, pp. 373-377, 2000.

[Sah07] Sahni, V. “Quantum Computing,” Tata McGraw- Hill Publishing Company Limited, New Delhi, 2007.

[Sahi05] Sahin, M., Atav, U., Tomak, M., “Quantum genetic algorithm method in self-consistent electronic structure calculations of a quantum dot with many electrons,” International Journal of Modern Physics, C 16(9), pp. 1379–1393, 2005.

Page 24: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-24

[Sai98] Saito, Y., Tsuji, N., Utsunomiya, H., Sakai, T., Hong, R.G., “Ultra-fine Grained Bulk Aluminum Produced by Accumulative Roll-bonding (ARB) Process,”Scripta Materialia, 39 (9), pp. 1221–1227, 1998.

[Sai99] Saito, Y., Utsunomiya, H., Tsuji, N., Sakai, T., Acta Materialia, 47, pp. 579-585, 1999.

[Sail08] Sailesh Babu, G.S., Bhagwan Das, D., Patvardhan, C., “Real-parameter quantum evolutionary algorithm for economic load dispatch,” IET Gener. Transm. Distrib. 2(1), pp. 22–31, 2008.

[Sak09] Sakshi, A., Garg, M.L., “New Hybrid Evolutionary Algorithm for Solving the Bounded Diameter Minimum Spanning Tree Problem,” Advances in Computational Research, ISSN: 0975–3273, Vol.1, Issue 2, pp.39-42, 2009.

[Sant98] Santo, M. T., Lama, J., “Numerical optimisation of the forging processes using the response surface approach,”1st ESAFORM Conference on Metal forming, Sophia Antipolis, France, 1998.

[Sar02] Sarker R. A., M. A. Quaddus, “Modelling a Nationwide Crop Planning Problem Using a Multiple Criteria Decision Making Tool,” Computers and Industrial Engineering, Vol. 42, pp. 541–553, 2002.

[Sar07] Saravanan M., Pillai R. M., Pai B. C., Brahmakumar M., Ravi K. R., “Development of ultrafine grain aluminium-graphite metal matrix composite by equal channel angular pressing”, Composites Science and Technology, vol. 67, pp. 1275-1279, 2007.

[Sar09] Sarker R. and T. Ray., “An Improved Evolutionary Algorithm for Solving Multi-Objective Crop Planning Models,” Computers and Electronics in Agriculture, Vol.68, pp.191–199, 2009.

[Sar97] Sarker R., S. Talukdar, A. Haque, “Determination of Optimum Crop Mix for Crop Cultivation in Bangladesh, Applied Mathematical Modelling, Vol. 21(10), pp. 621–632, 1997.

[Sat92] Sathyanarayanan, G., Lin, I.J. and Chen, M. K., “Neural Network Modelling and Multi-Objective Optimisation of Creep Feed Grinding of Super alloys,” International Journal of Production Research, vol. 30(10), pp. 2421 – 2438, 1992.

[Sch04] Schlottmann, F. and Seese, D., “A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios,” Computational Statistics and Data Analysis, 47(2), pp. 373–399, 2004.

[Sch09] Schulze,V., Bertram, A., Böhlke, T., Krawietz, A., “Texture-Based Modeling of Sheet Metal Forming and Springback,” TECHNISCHE MECHANIK, Band 29, Heft 2, pp. 135 – 159, 2009.

[Sch95] Schwefel, H.P., “Evolution and Optimum Seeking,” New York: Wiley Inter-Science, 1995.

[Seg04] Segal, V.M., Dobatkin, S.V., Valiev, R.Z., “Equal-channel angular pressing of metallic materials: Achievements and trends,” Thematic issue, Part 1, Russian Metall, vol. no. 1, pp. 1–102, 2004.

[Seg77] Segal, V.M., USSR Patent No. 575892, 1977.

Page 25: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-25

[Seg81] Segal, V.M., Reznikov, V.I., Drobyshevskiy, A.E., Kopylov, V.I., “Plastic working of metals by simple shear,” Russian Metall, 1:99, 1981.

[Seg99] Segal, V. M., “Equal channel angular extrusion: from micromechanics to structure formation”, Material Science and Engineering, A271, pp.322-333, 1999.

[Sen01] Senkov, O.N., Froes, F. H., Stolyarov, V. V., Valiev, R. Z., Liu, J., “Grain Refinement and Superplastic Flow in an Aluminum Alloy Processed by High Pressure Torsion,” Scripta Materialia, 39, pp. 1511-1516, 2001.

[Seu08] Seung, C.Y., Hyoung, S.K., “Finite element analysis of the effect of the inner corner angle in equal channel angular pressing”, Materials Science and Engineering A, 490, pp. 438–444, 2008.

[Sha08] Sharma, R.S., Hans Raj, K., “Finite Element Modelling and Simulation of Hot Upsetting Process to Minimize Central Bulge in Manufacturing,” XXXII National Systems Conference, NSC 2008, pp.485-489, 2008.

[She01] Sherby, O.D., Wadsworth J., Journal of Material Processing Technology, 117, pp. 347-352, 2001.

[She08] Sheu, J.B., “A hybrid Neuro-fuzzy Analytical Approach to Mode Choice of Global Logistics Management,” European Journal of Operational Research, 189, pp. 971–986, 2008.

[Shi05] Shi, X. H., Liang, Y. C., Lee, H. P., Lu, C., and Wang, L. M., “An improved GA and a novel PSO-GA-based hybrid algorithm,” Information Processing Letters, 93(5), pp. 255–261, 2005.

[Shik07] Shikha, S., Bhatti, T. S., Kothari, D. P., “Wind Power Estimation using Artificial Neural Network,” Journal of Energy Engineering , Vol. 133, No.1, pp.1021-1024, 2007.

[Shin02] Shin, D.H., Park, J.J., Kim, Y.S., Park. K.T., Material Science Engineering, A328, pp. 98- 103, 2002.

[Shor94] Shor, P. W., “Algorithms for quantum computation: Discrete log and factoring,” In Proceedings of the 35th Annual Symposium on Foundations of Computer Science (Nov. 1994), pp. 124–134, 1994.

[Shu07] Shu, W.N., He, B.J., “A quantum genetic simulated annealing algorithm for task scheduling,” In: Lecture Notes in Computer Science, vol. 4683, pp. 169–176, 2007.

[Shu11] Shu B. X., Cai N. J., Guo C. R., Guo Q. Z., “Finite Element Simulation of Die Design for Equal Channel Angular Extrusion Process of Pure Aluminum and its Experimental Investigation,” Advanced Materials Research (Volumes 146 –147), pp. 1737-1740, 2011.

[Shub11] Shubo, X., Cainian, J., Guocheng, R., Peng, L., “Finite Element Simulation of Die Design for Warm Equal Channel Angular Extrusion Process of AZ31 Alloy and its Experimental Investigation,” Advanced Materials Research(Volumes 667 – 669), pp. 75-79, 2011.

[Sin08] Singh, R., Setia, R., Das, G., “Neuro - Fuzzy Modeling of Rotary FurnaceParameters for the Agile Production of Quality Castings,” Proceedings of XXXII National Systems Conference (NSC), pp. 364-368, 2008.

Page 26: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-26

[Sip97] Sipper, M. et al., “A Phylogenetic, Ontogenetic, and Epigenetic View of Bio-Inspired Hardware Systems,” IEEE Transactions on Evolutionary Computation, vol. 1(1), 1997.

[Sit04] Sitdikov, O., Sakai, T., Goloborodko, A., Miura, H., Kaibyshev, R.. Mater Trans, 45, pp. 2232-2239, 2004.

[Sof06] Sofge, D.A., “Toward a framework for quantum evolutionary computation,” In: Proc. CIS, pp. 789–794, 2006.

[Som05] Somasundaram, P., Lakshmiramanan, R., and Kuppusamy, K., “Hybrid algorithm based on EP and LP for security constrained economic dispatch problem,” Electric Power Systems Research, 76(1–3), pp. 77–85, 2005.

[Son06a] Son, I. H., Jin, Y.G., Im, Y.T., “Finite element investigations of friction condition in equal channel angular extrusion”, Journal of Achievements in Materials and Manufacturing Engineering, vol.17, pp 285-288, 2006.

[Son06b] Son, I.H., Lee, J.H., Im, Y.T., “Finite element investigation of equal channel angular extrusion with back pressure”, Journal of Materials Processing Technology, 171, pp. 480-487, 2006.

[Spec98] Spector, L., Barnum, H., Bernstein, H., “Genetic programming for quantum computers,” In: Proc. GP, pp. 365–373, 1998.

[Spec99] Spector, L., Barnum, H., Bernstein, H., Swamy, J.N., “Finding a better-than-classical quantum and/or algorithm using genetic programming,” In: Proc. CEC, pp. 2239–2246, 1999.

[Sri04] Srinivasan, S., Ranganathan, S., “India’s legendary wootz steel: An advanced material of the ancient world,” National Institute of Advanced Studies and Indian Institute of Science, Bangalore, India, 2004.

[Sri04a] Srivastava, S.K., Srivastava, Kamal, Sharma, Rahul Swarup, Hans Raj, K., and Dwivedi, S.N., “Multi-Objective Process Optimization of Hot Closed Die Forging for Intelligent Manufacturing,” International Journal of Agile manufacturing, vol. 4(1), pp. 61-70, 2004.

[Sri94] Srinivas, M., and Patnaik, L. M., “Genetic Algorithms: A Survey,” Piscataway, NJ: IEEE Press, vol. 27, no. 6, pp. 17-26, 1994.

[Su10] Su, H., Yang, Y., Zhao, L., “Classification rule discovery with DE QDE algorithm,” Expert Syst. Appl., 37(2), pp. 1216–1222, 2010.

[Sug98] Sugeno M. and G. T. Kang, “Structure Identification of Fuzzy Model,” Fuzzy Sets and Systems, 28, pp. 15-33, 1998.

[Sun09] Sunghak L., Yang G., Byoungchul H., Chul W. L., Dong H. S., “Dynamic deformation and fracture behavior ultra-fine grained pure copper fabricated by equal channel angular pressing”, Material Science and Engineering, A 504, pp. 163-168, 2009.

[Suz79] Suzuki, Y., “The case of warm and cold forging,” American Machinist, February, 1979.

[Tah08] Taher, N., Bahman, B.F., “An Efficient Hybrid Evolutionary Algorithm for Cluster Analysis” World Applied Sciences Journal, ISSN 1818-4952, 4 (2), pp. 300-307, 2008.

Page 27: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-27

[Tah10] Taher, N., Reza, K., “A Hybrid Evolutionary Algorithm for Distribution Feeder Reconfiguration,” Indian Academy of Sciences, Vol. 35, Part 2, pp. 139–162, 2010.

[Tak85] Takagi, T. and M. Sugeno, “Fuzzy Identification of Systems and its Application to Modeling and Control,” IEEE Trans. on Systems, Man & Cybernetics, 15, pp. 116-132, 1985.

[Tal04] Talbi, H. Draa, A. and Batouche, M., "A new quantum-inspired genetic algorithm for solving the travelling salesman problem," 14th International Conference on Computer Theory and Applications, Alexandria, Egypt, 2004.

[Tan03] Tan, K. C., Yu, Q., Heng, C. M., and Lee, T. H., “Evolutionary computing for knowledge discovery in medical diagnosis,” Artificial Intelligence in Medicine, 27(2), pp. 129–154, 2003.

[Tan06] Tansel, I.N., Ozelik, B., Bas, W.Y., Chen, P., Rincon, D., Yang, S.Y., Yenilmeg, A., “Selection of optimal cutting conditions by using GONNS.” International Journal of Machine Tools & Manufacture, 46, pp. 26-35, 2006.

[Tans00] Tansel, I.N., Arkan, T.T., Bao, W.Y., Mahendrakar, N., Shisler, B., Smith, D., “Tool Wear Estimation in Micro-machining. Part II: Neural-Network-based Periodic Inspector for Non- Metals” International Journal of Machine Tools & Manufacture, 40, pp. 609–620, 2000.

[Tao06] Tao Suo Yulong Li, Yazhou Guo, Yuanyong Liu, “The simulation of deformation distribution during ECAP using 3D finite element method”, Materials Science and Engineering A, 432, pp. 269–274, 2006.

[Tek84] Tekkaya, A.E. and Roll, K., “Analysis of metal forming processes by different finite element methods,” Numerical Methods Non-Linear Problems, Pineridge Press, vol. 2, pp. 450-61, 1984.

[Tho99] Thompson, J.D., Plewniak, F., Poch, O., “A benchmark alignment database for the evaluation of multiple alignment programs,” Bioinformatics, 15, pp. 87–88, 1999.

[Tie02] Tiernan, P. and Hillery, M. T., “Experimental and numerical analysis of the deformation in mild steel wire during dieless drawing,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Materials: Design and Applications, vol. 216(3), pp.167-178, 2002.

[Tse05] Tseng, L.Y. and Liang, S.C., “A hybrid metaheuristic for the quadratic assignment problem,” Computational Optimization and Applications, 34(1), pp. 85–113, 2005.

[Tug05] Tugrul,O., Yigit, K., “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks,” International Journal of Machine Tools & Manufacture, 45, pp. 467–479, 2005.

[Udr06] Udrescu, M., Prodan, L., Vladutiu, M., “Implementing quantum genetic algorithms: a solution based on Grover’s algorithm,” In: Proc. CF, pp. 14–16, 2006.

[Uga02] Ugalde Arias, L.A.Montagnin, F. and Reicha, C.R., “Preliminary models for the estimation of bio-mass of ten species native to the Atlantic Zone of Costa Rica,” Memoria-del-taller- Seminario – Especies Frestales-nativas-Henedia, -costarica, pp 73-75, 2002.

Page 28: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-28

[Val04] Valiev, R.Z., Nature Mater, vol 3, pp. 511-515, 2004.

[Val91] Valiev, R. Z., Krasilnikov, N. A., Tsenev, N. K., “Plastic Deformation of Alloys with Submicron-grained Structure,”.Material Science & Engineering A, 137,pp. 35-42, 1991.

[Val97] Valiev, R. Z., “Structure and Mechanical Properties of Ultrafine-grained Metals,” Material Science & Engineering A, 59, pp. 234-235, 1997.

[Var06] Varyutkhin, V.N., Beygelzimer, Y.Y., Synkov, S., Orlov, D., Materials Science Forum, pp.503–504, 2006.

[Var93] Vargas, L. S., Quintana, V. H., and Vannelli, A., “A tutorial description of an interior point method and its application to security-constrained economic dispatch,” IEEE Transaction on Power Systems, 8(3), pp. 1315–1324, 1993.

[Vaz00] Vazquez, M., and Whitley, D., “A hybrid genetic algorithm for the quadratic assignment problem,” In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), Morgan Kaufmann, San Mateo, CA, pp. 135–142, 2000.

[Vel99] Veldhuizen, D. A. V., “Multi-objective Evolutionary Algorithms: Classifications, Analyses, and New Innovations,” Ph. D. thesis, Graduate School of Engineering of the Air Force Institute of Technology, Air University,June 1999.

[Vla08] Vlachoglannis, J.G., “Quantum-inspired evolutionary algorithm for real and reactive power dispatch,” IEEE Transactions on Power Systems, 23(4), pp. 1627–1636, 2008.

[Vla08] Vladimir V. S., Theodore Zhu Y., Igor V. A., Valiev R.Z., “Influence of ECAP routes on microstructure and properties of pure Ti”, Material Science and Engineering, A299, pp. 59-63, 2008.

[Wag01] Wagoner, R. H. and Chenot, J. L., “Metal forming analysis,” Cambridge University Press, Cambridge, 2001.

[Wal11] Walczyk, W., Milenin, A., Pietrzyk, M., “Computer Aided Design of New Forging Technology for Crank Shafts,” Steel Research Journal, No.3, DOI: 10.1002/srin.201000121, pp. 187-194, 2011.

[Wan05] Wang, L., “A hybrid genetic algorithm-neural network strategy for simulation optimization,” Applied Mathematics and Computation, 170(2), pp. 1329–1343, 2005.

[Wan05a] Wang, L., Tang, F., Wu, H., “Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation,” Appl. Math. Comput., 171(2), pp. 1141–1156, 2005.

[Wan05b] Wang, L., Wu, H., Tang, F., Zheng, D.Z., “A hybrid quantum-inspired genetic algorithm for flow shop scheduling,” In: Lecture Notes in Computer Science,vol. 3645, pp. 636–644, 2005.

[Wan05c] Wang, L., Wu, H., Zheng, D.Z., “A quantum-inspired genetic algorithm for scheduling problems, In: Lecture Notes in Computer Science, vol. 3612, pp. 417–423, 2005.

Page 29: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-29

[Wan05d] Wang, Y., Feng, X.Y., Huang, Y.X., Zhou, W.G., Liang, Y.C., Zhou, C.G., “A novel quantum swarm evolutionary algorithm for solving 0-1 knapsack problem,” In: Lecture Notes in Computer Science, vol. 3611, pp. 698–704, 2005.

[Wan06] Wang, J.T., Material Science Forum, 363, pp. 503–504, 2006.

[Wan07a] Wang, L., Niu, Q., Fei, M.R., “A novel quantum ant colony optimization algorithm,” In: Lecture Notes in Computer Science, vol. 4688, pp. 277–286, 2007.

[Wan07b] Wang, X.H., Ying, Y., Xiao, J.M., “Application of quantum genetic algorithm in logistics distribution planning,” In: Proc. CCC, pp. 759–762, 2007.

[Wan07c] Wang, Y., Feng, X.Y., Huang, Y.X., Pu, D.B., Zhou, W.G., Liang, Y.C., Zhou, C.G., “A novel quantum swarm evolutionary algorithm and its applications,” Neurocomputing, 70(4–6), pp. 633–640, 2007.

[Wan10] Wang, L., Li, L.P., “An effective hybrid quantum-inspired evolutionary algorithm for parameter estimation of chaotic systems,” Expert Syst. Appl.37(2), pp. 1279–1285, 2010.

[Wang06] Wang, L., Zhang, L., and Zheng, D. Z., “An effective hybrid genetic algorithm for flow shop scheduling with limited buffers,” Computers and Operations Research, 33(10), pp. 2960–2971, 2006.

[War01] Ward, M. J., Miller, B. C., Davey, K., “Efficient strategies for the simulation of wheel forming,” Journal of Materials Processing Technology, vol. 118, pp. 389-396, 2001.

[War98] Ward, M. J., Miller, B. C., Davey, K., “Simulation of multi stage rail wheel and type forming processes,” Journal of Materials Processing Technology, vol. 80-81, pp. 206-212, 1998.

[Wei08] Wei, W., Li, B., Zou, Y., Zhang, W., Zhuang, Z., “A multi-objective HW-SW co-synthesis algorithm based on quantum-inspired evolutionary algorithm,” International Journal of Computational Intelligence, 7(2), pp. 129–148, 2008.

[Wei09] Wei W., Wei Z., Kun X., Yi Z., Gang Z., Jing H., “Finite Element analysis of deformation behavior in continuous ECAP process”, Material Science and Engineering, A 516, pp. 111-118, 2009.

[Wei11] Weimin, Z., Wang, S., Balint, D., Lin, J., “Crystal Plasticity Finite Element Process Modeling for Hydro-forming Micro-tubular Component,” Chinese Journal of Mechanical Engineering, Vol. 24, pp. 01-06, 2011.

[Wid90] Widrow, B., and Lehr, M. A., “30 years of adaptive neural networks: Perceptron, madaline, and back propagation,” Proceedings of the IEEE, vol. 78, pp.1415–1442, 1990.

[Wu09] Wu, Q., Jiao, L., Li, Y., Deng, X., “A novel quantum-inspired immune colonel algorithm with the evolutionary game approach,” Progr. Nat. Sci., 19(10), pp. 1341–1347, 2009.

[Xia06] Xiao, W.X., Zang, X., Yan, X.P., “QGA based bandwidth adaptation scheme for wireless/mobile networks,” In: Proc. ITST, pp. 1323–1326, 2006.

Page 30: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-30

[Xia08] Xiaoyu,W., Wen,W., Yong,H., Nhan,N., Kalmanje, K., “Design of Neural Network-based Estimator for Tool Wear Modeling in Hard Turning,” Journal of Intelligent Manufacturing, 19, pp.383–396, DOI 10.1007/s10845-008-0090-8, 2008.

[Xin09a] Xing, H., Ji, Y., Bai, L., Liu, X., Qu, Z., Wang, X., “An adaptive-evolution-based quantum-inspired evolutionary algorithm for QOS multicasting in IP/DWDM networks,” Computer Communications, 32(6), pp. 1086 – 1094, 2009.

[Xin09b] Xing, H., Liu, X., Jin, X., Bai, L., Ji, Y., “A multi-granularity evolution based quantum genetic algorithm for QOS multicast routing problem in WDM networks,” Computer Communications, 32(2), pp. 386–393, 2009.

[Xin10] Xin, W., Shigeru, F., “Multi Update Mode Quantum Evolutionary Algorithm for a Combinatorial Problem,” The 2nd International Conference on Computer and Automation Engineering, (ICCAE), pp. 281-288, 2010.

[Xu05] Xu, J.J., Chen, H.J., Cheng, Y.H., Luo, R., “Blind signal separation based on quantum genetic algorithm,” Journal of Communication Computation, 2(9), pp. 62–66, 2005.

[Yan03] Yang, S.Y., Tansel, I.N., Kropas-Hughes, C.V., “Selection of Optimal Material and Operating Conditions in Composite Manufacturing. Part I: Computational Tool,” International Journal of Machine Tools & Manufacture, 43, pp.169–173, 2003.

[Yan03a] Yang, J.A., Li, Z.Q., Zhuang, Z.Q., “Multi-universe parallel quantum genetic algorithm and its application to blind source separation,” In: Proc. ICNNS, pp. 393–398, 2003.

[Yan03b] Yang, J.A., Peng, H., Zhuang, Z.Q., “Research of nonlinear blind source separation algorithm based on quantum evolutionary neural network,” In: Proc. ICMLC, pp. 835–840, 2003.

[Yan04a] Yang, S.Y., Wang, M., Jiao, L.C., “A genetic algorithm based on quantum chromosome,” In: Proc. ICSP, pp. 1622–1625, 2004.

[Yan04b] Yang, S.Y., Wang, M., Jiao, L.C., “A novel quantum evolutionary algorithm and its application,” In: Proc CEC, pp. 820–826, 2004.

[Yan05] Yang, J.A., Zhao, B., Ye, Z.F., “Research of blind deconvolution algorithm based on high-order statistics and quantum inspired GA,” In: Lecture Notes in Computer Science, vol. 3611, pp. 461–467, 2005.

[Yan07] Yang, Q., Ding, S.C., “Methodology and case study of hybrid quantum-inspired evolutionary algorithm for numerical optimization,” In: Proc. ICNC,pp. 634–638, 2007.

[Yao99a] Yao, X., Liu, Y., Lin, G., “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, 3(2), pp. 82–102, 1999.

[Yao99] Yao, Y., Li, X., Yuan, Z., “Tool Wear Detection with Fuzzy Classification and Wavelet Fuzzy Neural Network,” International Journal of Machine Tools & Manufacture, 39, pp. 1525–1538, 1999.

[Yon01] Yongcai, X., Masami, I., and Katsuhisa, F., “Time Optimal Swing – Up Control of Single Pendulum. Journal of Dynamic Systems,” Measurement and Control on Transactions of the ASME, 123, 2001.

Page 31: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-31

[You06a] You, X., Liu, Y., Shuai, D., “On parallel immune quantum evolutionary algorithm based on learning mechanism and its convergence,” In: Lecture Notes in Computer Science, vol. 4221, pp. 903–912, 2006.

[You06b] You, X., Shuai, D., Liu, S., “Research and implementation of quantum evolution algorithm based on immune theory,” In: Proc. WCICA, pp. 3410–3414, 2006.

[You06c] You, X.M., Liu, S., Shuai, D.X., “On improved parallel immune quantum evolutionary algorithm based on learning mechanism,” In: Proc. ISDA, pp. 908–913, 2006.

[You07] You, X., Sheng, L., Dianxun, S., “Studying the performance of quantum evolutionary algorithm based on immune theory,” In: Lecture Notes in Computer Science, vol. 4490, pp. 1068–1075, 2007.

[Yu06] Yu, Y., Tian, Y.F., Yin, Z.F., “Hybrid quantum evolutionary algorithms based on particle swarm theory,” In: Proc. IEA, pp. 309–315, 2006.

[Yuk10] Yuksel, O., Irfan, G., “Adaptive Neuro-fuzzy Inference System to Improve thePower Quality of Variable-speed Wind Power Generation System,” Turkish Journal of Electrical Engineering & Computer Science, Vol. 18, No.4, pp. 625-645, 2010.

[Yun00] Yuntian T., Terry C. L., “Observations and issues on mechanisms of grain refinement during ECAP process”, Material Science and Engineering, A291, pp. 46-53, 2000.

[Yun05] Yunong, Z., Shuzhi, S. G., “Design and Analysis of a General Recurrent Neural Network Model for Time-Varying Matrix Inversion,” IEEE Transactions on Neural Networks, Vol. 16, No. 6, pp. 1477-1490, 2005.

[Zha03a] Zhang, G.X., Jin, W.D., Hu, L.H., “A novel parallel quantum genetic algorithm,” In: Proc. PDCAT, pp. 693– 697, 2003.

[Zha03b] Zhang, G.X., Jin, W.D., Hu, L.Z., “Quantum evolutionary algorithm for multi-objective optimization problems,” In: Proc. ISIC, pp. 703–708, 2003.

[Zha03c] Zhang, G.X., Jin, W.D., Li, N., “An improved quantum genetic algorithm and its application,” In: Lecture Notes in Artificial Intelligence, vol. 2639, pp. 449–452, 2003.

[Zha04] Zhao, X., Jing, T.F., Gao, Y.W., Zhou, J.F., Wang, W., Mater Lett, 58, pp. 2335-2441, 2004.

[Zha04a] Zhang, G.X., Hu, L.Z., Jin, W.D., “Quantum computing based machine learning method and its application in radar emitter signal recognition,” In: Lecture Notes in Artificial Intelligence, vol. 3131, pp. 92–103, 2004.

[Zha04b] Zhang, G.X., Hu, L.Z., Jin, W.D., “Resemblance co-efficient and a quantum genetic algorithm for feature selection,” In: Lecture Notes in Artificial Intelligence, vol. 3245, pp. 155–168, 2004.

[Zha06] Zhao, Z., Peng, X., Peng, Y., Yu, E., “An effective constraint handling method in quantum-inspired evolutionary algorithm for knapsack problems,” WSEAS Transaction on Computers, 5(6), pp. 1194–1199, 2006.

Page 32: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-32

[Zha06] Zhang, G.X., Li, N., Jin, W.D., Hu, L.Z., “Novel quantum genetic algorithm and its applications,” Front. Electr. Electron. Eng. China 1(1), pp. 31–36, 2006.

[Zha07b] Zhang, R., Gao, H., “Real-coded quantum evolutionary algorithm for complex functions with high dimension,” In: Proc. ICMA, pp. 2974–2979, 2007.

[Zha07c] Zhang, G.X., Rong, H.N., “Real-observation quantum-inspired evolutionary algorithm for a class of numerical optimization problems,” In: Lecture Notes in Computer Science, vol. 4490, pp. 989–996, 2007.

[Zha09] Zhao, S., Xu, G., Tao, T., Liang, L., “Real-coded chaotic quantum-inspired genetic algorithm for training of fuzzy neural networks,” Computational Mathematics Applied, 57(11–12), pp. 2009–2015, 2009.

[Zha09] Zhang Z. F., Qu S., Ann X. H., Yang H. J., Huang C. X., G. Yang, Xang Q. S., Wang Z. G., “Microstructural evolution and mechanical properties of Cu-Al alloys subjected to equal channel angular pressing”, Acta Materialia, vol. 57, pp. 1586-1601, 2009.

[Zhan06] Zhang, G.X., Rong, H.N., “Improved quantum-inspired genetic algorithm based time-frequency analysis of radar emitter signals,” In: Lecture Notes in Artificial Intelligence, vol. 4481, pp. 484–491, 2006.

[Zhan07] Zhang, G.X., Rong, H.N., “Real-observation quantum-inspired evolutionary algorithm for a class of numerical optimization problems,” In: Lecture Notes in Computer Science, vol. 4490, pp. 989–996, 2007.

[Zhan07] Zhang, R., Gao, H., “Improved quantum evolutionary algorithm for combinatorial optimization problem,” In: Proc. ICMLC, pp. 3501–3505, 2007.

[Zhan07b] Zhang, G.X., Rong, H.N., “Quantum-inspired genetic algorithm based time-frequency atom decomposition,” In: Lecture Notes in Computer Science, vol. 4490, pp. 243–250, 2007.

[Zhan11] Zhang, G., “Quantum-inspired evolutionary algorithms: a survey and empirical study,” Journal of Heuristics, vol. 17, Issue 3, pp. 303-351, 2011.

[Zhan95] Zhang, H. C. and Haung, S. H., “Neural-Expert Hybrid approach for Intelligent Manufacturing: A survey,” Computers in Industry, vol. 26, pp.107–126, 1995.

[Zhe01] Zhen, W., “Neural Network Detection of Grinding Burn from Acoustic Emission,” International Journal of Machine Tools & Manufacture, 41, pp. 283–309,2001.

[Zhen10] Zheng T., M. Yamashiro, “Solving flow shop scheduling problems by quantum differential evolutionary algorithm,” The International Journal of Advanced Manufacturing Technology, pp. 1–20, 2010.

[Zhi03] Zhilyaev, A.P., Nurislamova, G.V., Kim, B.K., Baro, M.D., Szpunar, J.A., Langdon, T.G., Acta Materilia, 51, pp. 753-758, 2003.

[Zhi08] Zhilyaev A. P., Swaminathan S., Glimazov A. A., McNelly T. R., Langdon T. G., “An evaluation of microstructure and microhardness in copper subjected to ultra-high strains”, Journal of Material Science, pp. 7451-7456, 2008.

Page 33: R-1 - Shodhganga : a reservoir of Indian theses @ INFLIBNETshodhganga.inflibnet.ac.in/bitstream/10603/14189/18/18... · R-1 REFERENCES [Abd03] ... for a quantum-inspired evolutionary

R-33

[Zho06b] Zhou, W.G., Zhou, C.G., Liu, G.X., Lv, H.Y., Liang, Y.C., “An improved quantum-inspired evolutionary algorithm for clustering gene expression data,” Computational Methods, pp. 1351–1356, 2006.

[Zhou05] Zhou, S., Sun, Z., “A new approach belonging to EDAS: Quantum-inspired genetic algorithm with only one chromosome,” In: Lecture Notes in Computer Science, vol. 3612, pp. 141–150, 2005.

[Zhou06a] Zhou, W.G., Zhou, C.G., Huang, Y.X., Wang, Y., “Analysis of gene expression data: application of quantum inspired evolutionary algorithm to minimum sum-of-squares clustering,” In: Proc. FSLCT, SPIE, vol. 6105, pp. 383–391, 2006.

[Zhu01] Zhu, Y.T., Jiang, H., Huang, J., Lowe, T.C., Metall Mater Trans, 32A, pp. 1559-1565, 2001.

[Zhu98] Zhu, L., Wainwright, R.L. & Schoenefeld, D.A., “A genetic algorithm for the point to multipoint routing problem with varying number of requests,” IEEE International Conference on Evolutionary Computation (ICEC98), Anchorage, Alaska, USA, pp.171-176, 1998.

[Zie74] Zienkiewicz, O.C. and Godbole, P. N., “Flow of plastic and visco-plastic solids with special reference to extrusion and forming processes,”International Journal for Numerical Methods in Engineering, vol. 8, pp. 3, 1974.

[Zmu03] Zmuda, M. A., Rizki, M. M., and Tamburino, L. A., “Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems,” Applied Soft Computing, 2(4), pp. 269–282, 2003.

[Zur91] Zurda, J.M., “Introduction to Artificial Neural Networks,” St. Paul, MN: West,1991.

[Zuy00] Zuyan, L., Gang, L., Wang, Z.R., “Finite element simulation of a new deformation type occurring in changing channel extrusion”, Journal of Material Processing Technology, 102, pp. 30-32, 2000.

***********THE END***********