Model-based algorithms are a class of stochastic search methods that have successfully addressed some hard deterministic optimization problems. However, their application to simulation optimization is relatively undeveloped. This chapter reviews the basic structure of model-based algorithms, describes some recently developed frameworks and approaches to the design and analysis of a class of model-based algorithms, and discusses their extensions to simulation optimization.
CITATION STYLE
Hu, J. (2015). Model-based stochastic search methods. In International Series in Operations Research and Management Science (Vol. 216, pp. 319–340). Springer New York LLC. https://doi.org/10.1007/978-1-4939-1384-8_12
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