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-
- 1
-
Arsham, H. (1998), ''Techniques for Monte Carlo Optimizing,'' Monte Carlo Methods and Applications, vol. 4, pp. 181-229.
- 2
-
Baba, N., Shoman, T., and Sawaragi, Y. (1977), ''A Modified Convergence
Theorem for a Random Optimization Method,'' Information Sciences, vol. 13, pp. 159-166.
- 3
-
Bazaraa, M.S., Sherali, H.D., and Shetty, C.M. (1993), Nonlinear Programming: Theory and Algorithms (2nd ed.), Wiley,
New York.
- 4
-
Blum, J.R. (1954), ''Multidimensional Stochastic Approximation Methods,''
Annals of Mathematical Statistics, vol. 25, pp. 737-744.
- 5
-
Box, G.E.P. (1957), ''Evolutionary Operation: A Method for Increasing
Industrial Productivity,'' Journal of the Royal Statistical Society, Ser. C., vol. 6, pp. 81-101.
- 6
-
De Jong, K.A. (1975), ''An Analysis of the Behavior of a Class of Genetic
Adaptive Systems,'' Ph.D. dissertation, University of Michigan, Ann Arbor, MI (University Microfilms no. 76-9381).
- 7
-
Dippon, J. and Renz, J. (1997), ''Weighted Means in Stochastic Approximation
of Minima,'' SIAM Journal of Control and Optimization, vol. 35, pp. 1811-1827.
- 8
-
Fabian, V. (1971), ''Stochastic Approximation,'' in Optimizing Methods in Statistics (J.S. Rustigi, ed.),
Academic Press, New York, pp. 439-470
- 9
-
Fogel, D.B. (2000), Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (2nd ed.), IEEE Press, Piscataway, NJ.
- 10
-
Fouskakis, D. and Draper, D. (2002), ''Stochastic Optimization: A Review,''
International Statistical Review, vol. 70, pp. 315-349.
- 11
-
Fu, M.C. (2002), ''Optimization for Simulation: Theory vs. Practice'' (with
discussion by S. Andradóttir, P. Glynn, and J.P. Kelly), INFORMS Journal on Computing, vol. 14, pp.
192-227.
- 12
-
Gentle, J.E. (2003), Random Number Generation and Monte Carlo Methods (2nd ed.), Springer-Verlag, New York.
- 13
-
Gerencsér, L. (1999), ''Convergence Rate of Moments in Stochastic
Approximation with Simultaneous Perturbation Gradient Approximation and
Resetting,'' IEEE Transactions on Automatic Control, vol. 44, pp. 894-905.
- 14
-
Gerencsér, L., Hill, S.D., and Vágó, Z. (1999), ''Fixed Gain SPSA
for Discrete Optimization,'' in Proceedings of the IEEE Conference on
Decision and Control, 7-10 December 1999, Phoenix, AZ,
pp. 1791-1795.
- 15
-
Goldberg, D.E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.
- 16
-
Gosavi, A. (2003), Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, Kluwer Academic, Boston.
- 17
-
Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI.
- 18
-
Karnopp, D.C. (1963), ''Random Search Techniques for Optimization
Problems,'' Automatica, vol. 1, pp. 111-121.
- 19
-
Kiefer, J. and Wolfowitz, J. (1952), ''Stochastic Estimation of a Regression
Function,'' Annals of Mathematical Statistics, vol. 23, pp. 462-466.
- 20
-
Kolda, T.G., Lewis, R.M., and Torczon, V. (2003), ''Optimization by Direct
Search: New Perspectives on Some Classical and Modern Methods,'' SIAM Review, vol. 45,
pp. 385-482.
- 21
-
Kushner, H.J. and Yin, G.G. (1997), Stochastic Approximation Algorithms and Applications, Springer-Verlag, New York.
- 22
-
Maryak, J.L. and Chin, D.C. (2001), ''Global Random Optimization by
Simultaneous Perturbation Stochastic Approximation,'' in Proceedings of the American Control Conference, 25-27 June
2001, Arlington, VA, pp. 756-762.
- 23
-
Matyas, J. (1965), ''Random Optimization,'' Automation and Remote Control, vol. 26, pp. 244-251.
- 24
-
Michalewicz, Z. (1996), Genetic Algorithms + Data Structures = Evolution Programs (3rd ed.), Springer-Verlag, New York.
- 25
-
Michalewicz, Z. and Fogel, D.B. (2000), How to Solve It: Modern Heuristics, Springer-Verlag, New York.
- 26
-
Mitchell, M. (1996), An Introduction to Genetic Algorithms, MIT Press, Cambridge, MA
- 27
-
Nelder, J.A. and Mead, R. (1965), ''A Simplex Method for Function
Minimization,'' The Computer Journal, vol. 7, pp. 308-313.
- 28
-
Pflug, G.Ch. (1996), Optimization of Stochastic Models: The Interface Between Simulation and Optimization, Kluwer Academic, Boston.
- 29
-
Reeves, C.R. and Rowe, J.E. (2003), Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory, Kluwer Academic, Boston.
- 30
-
Robbins, H. and Monro, S. (1951), ''A Stochastic Approximation Method,''
Annals of Mathematical Statistics, vol. 22, pp. 400-407.
- 31
-
Rudolph, G. (1994), ''Convergence Analysis of Canonical Genetic
Algorithms,'' IEEE Transactions on Neural Networks, vol. 5, pp. 96-101.
- 32
-
Rudolph, G. (1997), Convergence Properties of Evolutionary Algorithms, Verlag Kovac, Hamburg.
- 33
-
Ruppert, D. (1991), ''Stochastic Approximation,'' in Handbook of Sequential Analysis (B.K. Ghosh and P.K.
Sen, eds.), Marcel Dekker, New York, pp. 503-529.
- 34
-
Schwefel, H.-P. (1995), Evolution and Optimum Seeking, Wiley, New York.
- 35
-
Solis, F.J. and Wets, J.B. (1981), ''Minimization by Random Search
Techniques,'' Mathematics of Operations Research, vol. 6, pp. 19-30.
- 36
-
Spall, J.C. (1992), ''Multivariate Stochastic Approximation Using
a Simultaneous Perturbation Gradient Approximation,'' IEEE Transactions on Automatic Control, vol. 37, pp.
332-341.
- 37
-
Spall, J.C. (2000), ''Adaptive Stochastic Approximation by the Simultaneous
Perturbation Method,'' IEEE Transactions on Automatic Control, vol. 45, pp. 1839-1853.
- 38
-
Spall, J.C. (2003), Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiley, Hoboken, NJ.
- 39
-
Suzuki, J. (1995), ''A Markov Chain Analysis on Simple Genetic Algorithms,''
IEEE Transactions on Systems, Man, and Cybernetics, vol. 25, pp. 655-659.
- 40
-
Wolpert, D.H. and Macready, W.G. (1997), ''No Free Lunch Theorems for
Optimization,'' IEEE Transactions on Evolutionary Computation, vol. 1, pp. 67-82.
- 41
-
Yin, G. (1999), ''Rates of Convergence for a Class of Global Stochastic
Optimization Algorithms,'' SIAM Journal on Optimization, vol. 10, pp. 99-120.
- 42
-
Zhigljavsky, A.A. (1991), Theory of Global Random Search, Kluwer Academic, Boston.
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