Model-Based Stochastic Search Methods

Jiaqiao Hu ()
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Jiaqiao Hu: Stony Brook University

Chapter Chapter 12 in Handbook of Simulation Optimization, 2015, pp 319-340 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4939-1384-8_12

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