E. H. L. Aarts and J. H. M. Korst, Simulated Annealing and Boltzmann Machines, John Wiley & Sons: 1989.
E. H. L. Aarts and J. K. Lenstra, Local Search in Combinatorial Optimization
, Wiley: Chichester, 1997.Google Scholar
W. A. T. W. Abdullah, “Seeking Global Minima, ” Journal of Computational Physics
vol. 110 p. 320, 1994.Google Scholar
R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows Theory, Algorithms and Applications, Prentice Hall: 1993.
S. M. Ali and S. D. Silvey, “A General Class of Coefficients of Divergence of one Distribution from Another” Journal of the Royal Statistical Society, Series B
vol. 28, pp. 131–142, 1966.Google Scholar
S. Andradóttir, “A Method for Discrete Stochastic Optimization, ” Management Science
vol. 41 pp. 1946–1961, 1995.Google Scholar
S. Andradóttir, “A Global Search Method for Discrete Stochastic Optimization, ” SIAM J. Optimization
vol. 6 pp. 513–530, 1996.Google Scholar
S. Asmussen, Applied Probability and Queues, John Wiley & Sons: 1987.
H. Cohn and M. Fielding, “Simulated Annealing Searching for an Optimal Temperature Schedule, ” 1999 (to appear in SIAM journal of optimization).
L. Devroye, Non-Uniform Random Variate Generation
, Springer: New York, 1986.Google Scholar
M. Dyer, A. Frieze, and R. Kannan, “A Random Polynomial-Time Algorithm for Approximation the Volume of Convex Bodies” Journal on ACM
vol. 38 pp. 1–17, 1991.Google Scholar
B. L. Fox and G. W. Heine, “Probabilistic Search with Overrides, ” Annals of Applied Probability
vol. 6 pp. 1087–1094, 1996.Google Scholar
C. J. Geyer and E.A. Thompson, “Annealing Markov Chain Monte-Carlo with Applications to to Ancestral Inference” Journal of the American Statistical Association
vol. 90 pp. 909–920, 1995.Google Scholar
W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman & Hall: 1996.
F. Glover and M. Laguna, “Tabu Search, ” a chapter in Modern Heuristic Techniques for Combinatorial Optimization, 1992.
D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning
, Addison Wesley, MA., 1989.Google Scholar
W. B. Gong, Y. C. Ho, and W. Zhai “Stochastic Comparison Algorithm for Discrete Optimization with Estimation, ” Proceedings of the 31st IEEE Conference on Decision and Control, pp. 795–800, 1992.
W. J. Gutjahr and G. Ch. Pflug, “Simulated Annealing for Noisy Cost Functions, ” Journal of Global Optimization
vol. 8 pp. 1–13, 1996.Google Scholar
W. K. Hastings, “Monte Carlo Sampling Methods using Markov Chains and their Applications, ” Biometrika
vol. 57 pp. 92–109, 1970.Google Scholar
R. Horst, P. M. Pardalos, and N. V. Thoai, Introduction to Global Optimization, Kluwer Academic Publishers: 1996.
M. R. Jerrum and A. J. Sinclair, “Approximating the Permanent, ” SIAM Journal on Computing
vol. 18 pp. 1149–1178, 1989.Google Scholar
M. R. Jerrum and A. J. Sinclair, “Polynomial-Time Approximation Algorithms for the Ising Model, ” SIAM Journal on Computing
vol. 22 pp. 1087–1116, 1993.Google Scholar
N. L. Johnson and S. Kotz, Urn Models and Their Applications: An Approach to Modern Discrete Probability Theory, John Wiley and Sons, Inc.: 1977.
R. M. Karp and M. Luby, “Monte-Carlo Algorithms for Enumeration and Reliability Problems, ” Proceedings of the 24th Annual IEEE Symposium on Foundations of Computer Science pp. 56–64, 1983.
J. N. Kapur and H. K. Kesavan, Entropy Optimization Principles with Applications, Academic Press: 1992.
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing, ” Science
vol. 220 pp. 671–680, 1983.Google Scholar
D. Lieber, Rare Events Estimation Via Cross-Entropy and Importance Sampling
, PHD Thesis,William Davidson Faculty of Industrial Engineering and Management, Technion, Haifa, Israel, 1999.Google Scholar
D. Lieber, R. Y. Rubinstein, and D. Elmakis, “Quick Estimation of Rare events in Stochastic Networks, ” IEEE, Transactions on Reliability
vol. 46(2) pp. 254–265, 1997.Google Scholar
L. Lovasz, “Randomized Algorithms in Combinatorial Optimization, ” DIMACS Seies in Discrete Mathematics and Theoretical Computer Science
vol. 25, pp. 153–179, 1995.Google Scholar
E. Marinari and G. Parisi, “Simulated Tempering: A New Monte Carlo Scheme, ” Europhisics letters
vol. 19 pp. 451–458, 1992.Google Scholar
R. Mead and J. A. Nedler, “A Simplex Method for Function Minimization, ” Computer Journal
vol. 7 pp. 308–313, 1965.Google Scholar
M. Metropolis, A.W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller “Equations of State Calculations by Fast Computing Machines, ” J. of Chemical Physics
vol. 21 pp. 1087–1092, 1953.Google Scholar
W. I. Norkin, G. C. Pflug, and A. Ruszczyński, “A Branch and Bound Method for Stochastic Global Optimization”. Working paper, International Institute for Applied System Analysis, WP-96-065, Laxenburg, Austria, 1996.
I. H. Osman and G. Laporte, “Metaheuristics: A bibliography, ” Annals of Operations Research
vol. 63 pp. 513–523, 1996.Google Scholar
J. D. Pinter, Global Optimization in Action, Kluwer Academic Publishers: 1996.
R. G. Parker and R. L. Rardin, Discrete Optimization
, Academic Press: San Diego, 1988.Google Scholar
G. Potamianos and J. Goutsias, “Stochastic Approximation Algorithms for Partition Function Estimation of Gibbs Random Fields, ” IEEE Transactions on Information Theory
vol. 43(6) pp. 1948–1965, 1997.Google Scholar
V. J. Rayward-Smith, I. H. Osman, C. R. Reeves, and G. D. Smith, Modern Heuristic Search Methods
, Wiley: Chichester, 1996.Google Scholar
C. R. Reeves, Modern heuristic techniques, in: Modern Heuristic Search Methods
(eds. V. J. Rayward-Smith, I. H. Osman, C. R. Reeves and G. D. Smith), Wiley: Chichester, 1996.Google Scholar
H. E. Romeijn and R. L. Smith, “Simulated Annealing for Constrained Global Optimization, ” Journal of Global Optimization
vol. 5 pp. 101–126, 1994.Google Scholar
R. Y. Rubinstein, Simulation and the Monte Carlo Methods, John Wiley and Sons, Inc., 1981.
R. Y. Rubinstein, “Optimization of Computer Simulation Models with Rare Events, ” European Journal of Operations Research
vol. 99 pp. 89–112, 1997.Google Scholar
R. Y. Rubinstein and B. Melamed, Efficient Simulation and Modeling, John Wiley & Sons: 1998.
R. Y. Rubinstein and A. Shapiro, Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization via the Score Function Method, John Wiley & Sons: 1993.
L. Shi and S. Olafsson, “Nested Partitioning Method for Global Optimization” Manuscript, Department of Industrial Engineering, University of Wisconsin-Madison.
A. J. Walker, “An Efficient Method for Generating Discrete Random Variables with General Distributions, ” Assoc. Comput. Mach. Trans. Math. Software
vol. 3, pp. 253–256, 1977.Google Scholar