Random search in the presence of noise, with application to machine learning
SIAM Journal on Scientific and Statistical Computing
Stochastic discrete optimization
SIAM Journal on Control and Optimization
A method for discrete stochastic optimization
Management Science
New development of optimal computing budget allocation for discrete event simulation
Proceedings of the 29th conference on Winter simulation
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)
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
On Optimal Allocation of Indivisibles Under Uncertainty
Operations Research
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
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)
Simulation optimization using balanced explorative and exploitative search
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation optimization with countably infinite feasible regions: Efficiency and convergence
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Discrete Optimization via Simulation Using COMPASS
Operations Research
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
Rapid Screening Procedures for Zero-One Optimization via Simulation
INFORMS Journal on Computing
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We discuss desirable features that optimization algorithms should possess to exhibit good empirical performance when applied to solve simulation optimization problems possessing little known structure. Our framework emphasizes maintaining an appropriate balance between exploration, exploitation, and estimation. With the exception of estimation, our ideas are also applicable in (unstructured) deterministic optimization. Exploration refers to (globally) searching the entire feasible region for promising solutions, exploitation refers to the (local) search for improved solutions in promising subregions, and estimation refers to obtaining enhanced estimates of the objective function values at promising solutions and of the optimal solution. We also present two new random search methods that possess these desirable features, prove their almost-sure global convergence, and provide preliminary numerical results that suggest that the proposed framework is promising from a practical point of view.