A gradient approach for smartly allocating computing budget for discrete event simulation
WSC '96 Proceedings of the 28th conference on Winter simulation
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Stochastic Learning and Optimization: A Sensitivity-Based Approach (International Series on Discrete Event Dynamic Systems)
A Knowledge-Gradient Policy for Sequential Information Collection
SIAM Journal on Control and Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
May the best man win: simulation optimization for match-making in e-sports
Proceedings of the Winter Simulation Conference
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We address the problem of calibrating an approximate dynamic programming model, where we need to find a vector of parameters to produce the best fit of the model against historical data. The problem requires adaptively choosing the sequence of parameter settings on which to run the model, where each run of the model requires approximately twelve hours of CPU time and produces noisy non-stationary output. We describe an application of the knowledge-gradient algorithm with correlated beliefs to this problem and show that this algorithm finds a good parameter vector out of a population of one thousand with only three runs of the model.