Model-Based Stochastic Search Methods

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 216)

Abstract

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.

Keywords

Candidate Solution Stochastic Approximation Deterministic Optimization Simulation Optimization Stochastic Average 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported in part by the National Science Foundation (NSF) under Grant CMMI-1130761 and by the Air Force Office of Scientific Research (AFOSR) under Grant FA95501010340.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Stony Brook UniversityStony BrookUSA

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