Random search in the presence of noise, with application to machine learning
SIAM Journal on Scientific and Statistical Computing
Annals of Operations Research
Stochastic discrete optimization
SIAM Journal on Control and Optimization
A method for discrete stochastic optimization
Management Science
A branch and bound method for stochastic global optimization
Mathematical Programming: Series A and B
Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
The Sample Average Approximation Method for Stochastic Discrete Optimization
SIAM Journal on Optimization
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Discrete Optimization via Simulation Using COMPASS
Operations Research
A framework for locally convergent random-search algorithms for discrete optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A comparative study of genetic algorithm components in simulation-based optimisation
Proceedings of the 40th Conference on Winter Simulation
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A brief introduction to optimization via simulation
Winter Simulation Conference
Rapid Screening Procedures for Zero-One Optimization via Simulation
INFORMS Journal on Computing
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This article is concerned with proving the almost sure and global convergence of a broad class of algorithms for solving simulation optimization problems with countably infinite number of feasible points. We first describe the class of simulation optimization algorithms under consideration and discuss how the estimate of the optimal solution should be chosen when the feasible region of the underlying optimization problem is countably infinite. Then, we present a general result that guarantees the global convergence with probability one of the simulation optimization algorithms in this class. The assumptions of this result are sufficiently weak to allow the algorithms under consideration to be efficient, in that they are not required to either allocate the same amount of computer effort to all the feasible points these algorithms visit, or to spend an increasing amount of computer effort per iteration as the number of iterations grows. This article concludes with a discussion of how our assumptions can be satisfied and also generalized.