Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Applying model reference adaptive search to American-style option pricing
Proceedings of the 38th conference on Winter simulation
The mathematics of continuous-variable simulation optimization
Proceedings of the 40th Conference on Winter Simulation
A particle filtering framework for randomized optimization algorithms
Proceedings of the 40th Conference on Winter Simulation
New global optimization algorithms for model-based clustering
Computational Statistics & Data Analysis
A review of recent advances in global optimization
Journal of Global Optimization
Simulation optimization using the cross-entropy method with optimal computing budget allocation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Journal of Computational Neuroscience
A brief introduction to optimization via simulation
Winter Simulation Conference
On the performance of the cross-entropy method
Winter Simulation Conference
Simulation optimization with hybrid golden region search
Winter Simulation Conference
Dynamic sample budget allocation in model-based optimization
Journal of Global Optimization
International Journal of Robotics Research
Combining gradient-based optimization with stochastic search
Proceedings of the Winter Simulation Conference
Optimization via simulation using Gaussian process-based search
Proceedings of the Winter Simulation Conference
Discrete optimization via approximate annealing adaptive search with stochastic averaging
Proceedings of the Winter Simulation Conference
Model-based evolutionary optimization
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
On sample size control in sample average approximations for solving smooth stochastic programs
Computational Optimization and Applications
Global optimization of expensive black box problems with a known lower bound
Journal of Global Optimization
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Model reference adaptive search (MRAS) for solving global optimization problems works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the probabilistic model in such a way that the future search will be biased toward the region containing high-quality solutions. The parameter updating procedure in MRAS is guided by a sequence of implicit probabilistic models we call reference models. We provide a particular algorithm instantiation of the MRAS method, where the sequence of reference models can be viewed as the generalized probability distribution models for estimation of distribution algorithms (EDAs) with proportional selection scheme. In addition, we show that the model reference framework can also be used to describe the recently proposed cross-entropy (CE) method for optimization and to study its properties. Hence, this paper can also be seen as a study on the effectiveness of combining CE and EDAs. We prove global convergence of the proposed algorithm in both continuous and combinatorial domains, and we carry out numerical studies to illustrate the performance of the algorithm.