TY - CHAP
T1 - Model-based stochastic search methods
AU - Hu, Jiaqiao
N1 - Publisher Copyright:
© Springer Science+Business Media New York 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/84955124160
U2 - 10.1007/978-1-4939-1384-8_12
DO - 10.1007/978-1-4939-1384-8_12
M3 - Chapter
AN - SCOPUS:84955124160
T3 - International Series in Operations Research and Management Science
SP - 319
EP - 340
BT - International Series in Operations Research and Management Science
PB - Springer New York LLC
ER -