@inproceedings{0b772d67743d4aee9922461924623cf2,
title = "On the implementation of a class of stochastic search algorithms",
abstract = "We propose a stochastic approximation approach for implementing a class of random search-based optimization algorithms called the model-based methods. The approach makes efficient use of the past sampling information as the search progresses and can significantly reduce the number of function evaluations needed to obtain high quality solutions. We illustrate our approach through a specific algorithm called Model-based Annealing Random Search with Stochastic Averaging (MARS-SA), which maintains the per-iteration sample size at a small constant value. We present the global convergence property of MARS-SA and report on numerical results.",
keywords = "Global optimization, Model-based annealing random search, Stochastic approximation",
author = "Jiaqiao Hu and Enlu Zhou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 3rd World Congress on Global Optimization in Engineering and Science, WCGO 2013 ; Conference date: 08-07-2013 Through 12-07-2013",
year = "2015",
doi = "10.1007/978-3-319-08377-3\_42",
language = "English",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer New York LLC",
pages = "427--435",
editor = "Wenxun Xing and David Gao and Ning Ruan",
booktitle = "Advances in Global Optimization",
}