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References
Ackley DH (1987) A connectionist machine for genetic hillclimbing. Kluwer, Boston, MA, USA
Ali MM, Törn A (1998) Evolution based global optimization techniques and the controlled random search algorithm: Proposed modifications and numerical studies. Submitted to the Journal of Global Optimization, 1998, Kluwer Academic Publishers, The Netherlands
Ali MM, Törn A (2000) Optimization of carbon and silicon clusters geometry for Tersoff potential using differential evolution. In: Floudas CA, Pardalos PM (eds) Optimization in computational and molecular biology. Kluwer Academic Publishers pp 1–15
Aluffi-Pentini F, Parisi V, Zirilli F (1985) Global optimization and stochastic differential equations. Journal of Optimization and Theory and Applications 47(1):1–16
Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization. In: Porto VW, Saravanan N, Waagen D, Eiben AE (eds) Evolutionary programming VII. Springer, Berlin pp 601–610
Babu BV, Sastry KKN (1999) Estimation of heat transfer parameters in a tricklebed reactor using differential evolution and orthogonal collocation. Computers and Chemical Engineering 23:327–339
Bersini H, Dorigo M, Langerman S, Seront G, Gambardella L (1996) Results of the first international contest on evolutionary optimization (1st ICEO). In: Proceedings of the 1996 international conference on evolutionary computation, Nagoya, Japan, May 20–22. IEEE Press
Cao YJ, Wu QH (1997) Mechanical design optimization by mixed-variable evolutionary programming. In: Proceedings of the 1997 conference on evolutionary computation. IEEE Press pp 443–446
Chellapilla K (1998) Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation 2:91–96
Chen JL, Tsao YC (1993) Optimal design of machine elements using genetic algorithms. Journal of the Chinese Society of Mechanical Engineers 14(2):193–199
Corana A, Marchesi M, Martini C, Ridella S (1987) Minimizing multimodal functions for continuous variables with the “simulated annealing algorithm”. ACM Transactions on Mathematical Software, March 1987, pp 272–280
Crutchley DA, Zwolinski M (2003) Globally convergent algorithms for DC operating point analysis for nonlinear circuits. IEEE Transactions on Evolutionary Computation 7(1):2–10
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6:182–197
Fischer MM, Reismann M, Hlavackova-Schindler K (1999) Parameter estimation in neural spatial interaction modelling by a derivative free global optimization method. In: Proceedings of IV international conference on geocomputation, Mary Washington College, Fredericksburg, VA, USA, July 25–28, 1999 Available via Internet: http://www.geovista.psu.edu/sites/geocomp99/Gc99/007/gc_007.htm
Fu J-F, Fenton RG, Cleghorn WL (1991) A mixed integer-discrete-continuous programming method and its application to engineering design optimization. Engineering Optimization 17(4):263–280
Goodman R, Zeng Z (1994) A learning algorithm for multi-layer perceptrons with hard-limiting threshold units. In: Proceedings of the IEEE Neural Networks for Signal Processing, pp 219–228
Gorwin EM, Logar AM, Oldham WJB (1994) An iterative method for training multilayer networks with threshold functions. IEEE Transactions on Neural Networks 5:507–508
Griewangk AO (1981) Generalized descent for global optimization. JOTA 34:11–39
Han K-H, Kim J-H (2004) Quantum-inspired evolutionary algorithms with a new termination criterion, Hɛ gate, and two-phase scheme. IEEE transactions on Evolutionary Computation 8(2):156–169
Hu YF, Mcguire KC, Cokljat D, Blake RJ (1997) Parallel controlled random search algorithms for shape optimization. In: Emerson DR, Ecer A, Periaux J, Satofuka N (eds) Parallel computational fluid dynamics. North-Holland, pp 345–352
Ingber L (1993) Simulated annealing: Practice versus theory. Journal of Mathematical and Computer Modeling 18(11):29–57
Joines JA, Houck CR (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems. In: Proceedings of the first IEEE conference on evolutionary computation, June 27–29. IEEE Press vol 2, pp 579–584
Joshi R, Sanderson AC (1999) Minimal representation multisensor fusion using differential evolution. IEEE Transactions on systems, man and cybernetics — part A: Systems and Humans 29(1):63–76
Katsuura H (1991) Continuous nowhere differential functions — an application of contraction mappings. The American Mathematical Monthly 5(98)
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, 4. IEEE Press, Piscataway, NJ, USA pp 1942–1948
Krink T, Filipie B, Fogel GB (2004) Noisy optimization problems — a particular challenge for differential evolution? In: Proceedings of the 2004 Congress on evolutionary computation vol 1, pp 332–339
Kozeil S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings and constrained parameter optimization. Evolutionary Computation 7(1):19–44
Kukkonen S, Lampinen J (2004) An extension of generalized differential evolution for multi-objective optimization with constraints. In: Proceedings of PPSN 2004, the 8th International conference on parallel problem solving from nature, September 18–22 2004, Birmingham, UK, pp 752–761. Springer, ISBN: 3-540-23092-0
Lampinen J (2002). A constraint handling approach for the differential evolution algorithm. In: Proceedings of the 2002 IEEE world congress on computational intelligence — WCCI 2002, 2002 Congress on evolutionary computation — CEC 2002, Honolulu, Hawaii, May 12—17, 2002. IEEE Press, 6 pages. ISBN 0-7803-7281-6
Lampinen J, Storn R (2004) Differential evolution. In: Onwubolu GC, Babu BV (eds) New optimization techniques in engineering. Studies in fuzziness and soft computing, vol 141, Chapter 6. Springer, pp 123–166. ISBN 3-540-20167-X
Lampinen J, Zelinka I (1999) Mechanical engineering design optimization by differential evolution. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, Maidenhead, UK pp 127–146
Lee C-Y, Yao X (2004) Evolutionary programming using mutations based on the Levy probability distribution. IEEE Transactions on Evolutionary Computation 8(1):1–13
Li HL, Chow CT (1994) A global approach for nonlinear mixed discrete programming in design optimization. Engineering Optimization 22:109–122
Leung Y-W, Wang Y (2001) An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5(1):41–53
Lin SS, Zhang C, Wang H-P (1995) On mixed-discrete nonlinear optimization problems: A comparative study. Engineering Optimization 23(4):287–300
Loh HT, Papalambros PY (1991) A sequential linearization approach for solving mixed-discrete nonlinear design optimization problems. Transactions of the ASME, Journal of Mechanical Design 113(3):325–334
Loh HT, Paplambros PY (1991a) Computational implementation and tests of a sequential linearization algorithm for mixed-discrete nonlinear design optimization. Transactions of the ASME, Journal of Mechanical Design 113(3):335–345
Margoulas GD, Vrahatis MN, Grapsa TN, Androulackis GS (1997) A training method for discrete multilayer neural networks. In: Ellacot SW, Mason JC, Anderson IJ (eds) Mathematics of neural networks: Models, algorithms and applications, chapter 41. Kluwer Academic Publishers
Michalewicz Z (1995) Genetic algorithms, numerical optimization and constraints. In: Proceedings of the sixth international conference on genetic algorithms, Pittsburgh, July 15–19 pp 151–158
Michalewicz Z, Schoenauer M (1996) Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4(1):1–32
Moscato PA (1989) On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report, ??Caltech concurrent computation program report 826, Caltech, Pasadena, California
Mühlenbein H, Scomisch D, Born J (1991) The parallel genetic algorithm as function optimizer. Parallel Computing 17:619–632
Mühlenbein H, Schlierkamp-Vosen D (1993) Predictive models for the breeder genetic algorithm, I. Continuous parameter optimization. Evolutionary Computation 1(1):25–49
Paterlini S, Krink T (2004) Differential evolution and particle swarm optimization in partitional culstering. In: Proceedings of the 2004 Congress on Evolutionary Computation (CEC 2004), IEEE Press, Piscataway, NJ, USA
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C. Cambridge University Press
Price KV (1997) Differential evolution vs. the contest functions of the 2nd ICEO. In: Proceedings of the 1997 IEEE international conference on evolutionary computation, April 13–16, Indianapolis, IN, USA. IEEE Press, pp 153–157
Price WL (1977) Global optimization by controlled random search. Computer Journal 20:367–370
Plagianakos VP, Magoulas GD, Nousis NK, Vrahatis MN (2001) Training multilayer networks with discrete activation functions. In: Proceedings of the INNS-IEEE international joint conference on neural networks, July 14–19, 2001, Washington DC, USA
Rogalsky T, Derksen RW, Kocabiyik S (1999) Differential evolution in aerodynamic optimization. In: Proceedings of the 46th annual conference of the Canadian aeronautics and space institute, May 2–5, 1999, pp 29–36. Available via Internet: http://home.cc.umanitoba.ca/~umrogal1/publications.html
Růžek B, Kvasnička M (2001) Differential evolution algorithm in the earthquake hypocenter location. Pure and Applied Geophysics 158:667–693
Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions: A survey of some theoretical and practical aspects of genetic algorithms. Biosystems 39(3):263–278
Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design optimization. Transactions of the ASME, Journal of Mechanical Design 112(2):223–229
Schwefel H-P (1995) Evolution and optimum seeking. Wiley
Stanhope SA, Daida JM (1997) An individually variable mutation rate strategy for genetic algorithms. In: Angeline PJ, Reynolds RJ, McDonnell JR, Eberhart R (eds) Evolutionary programming VI; Lecture notes in computer science 1213. Springer, pp 235–245
Storn R, Price KV (1997) Differential evolution — A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11:341–359
Thierauf G, Cai J (1997) Evolution strategies — parallelization and application in engineering optimization. In: Topping BHV (ed) Parallel and distributed processing for computational mechanics. Saxe-Coburg Publications, Edinburgh
Thomas P, Vernon D (1997) Image registration by differential evolution. In: Proceedings of the first Irish machine vision and image processing conference IMVIP-97, Magee College, University of Ulster, pp 221–225. PostScript file available from http://www.cs.may.ie/~pthomas/
Thomsen R (2003) Flexible ligand docking using evolutionary algorithms: Investigating the effects of variation operators and local search hybrids. Biosystems 72(1–2):57–73
Tom DJ (1990) Training binary node feed forward neural networks by backpropagation of error. Electronics Letters 26:1745–1746
Tsai J-T, Liu T-K, Chou J-H (2004) Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 8(4):365–377
Tu Z, Lu Y (2004) A robust stochastic genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 8(5):456–470
Ursem RK, Vadstrup P (2004) Parameter identification of induction motors using differential evolution. Applied Soft Computing 4(1): 49–64
Van den Bergh F, Englebrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3):225–239
Vesterstrøm JS, Riget J (2002) Particle swarms: Extensions for improved local, multi-modal and dynamic search in numerical optimization. Master’s thesis, EVALife, Dept. of Computer Science, University of Aarhus, Denmark
Vesterstrøm J, Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proceedings of the 2004 congress on evolutionary computing, vol 2, pp 1980–1987
Voigt H-M (1995) Soft genetic operators in evolutionary computation and biocomputation. In: Lecture Notes in Computer Science 899. Springer, Berlin, pp 123–141
Whitley D, Mathias K, Rana S, Dzubera J (1996) Evaluating evolutionary algorithms. Artificial Intelligence 85:1–32
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE transactions on evolutionary computation, IEEE Press, 1(1):67–82
Wu S-J, Chow P-T (1995) Genetic algorithms for nonlinear mixed discreteinterger optimization problems via meta-genetic parameter optimization. Engineering Optimization 24(2): 137–159
Yao X, Liu Y (1997) Fast Evolution Strategies. In: Angeline PJ, Reynolds RJ, McDonnell JR, Eberhart R (eds) Evolutionary programming VI. Springer, Berlin, pp 151–161
Yao X, Liu Y, Lin GM (1999) Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3:82–102
Yen J, Lee B (1997) A simplex genetic algorithm hybrid. In: Proceedings of the 1997 IEEE conference on evolutionary computation, Indianapolis, Indiana, April 13–16. IEEE Press, pp 175–180
Zimmermann W (1990) Operations research. Oldenbourg
Zitzler E, Thiele I (1999) Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 4:257–271
Zitzler E, Deb K, Thiele L (2000) Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation 8:173–195
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(2005). Benchmarking Differential Evolution. In: Differential Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31306-0_3
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