Stochastic discrete optimization
SIAM Journal on Control and Optimization
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Nested Partitions Method for Global Optimization
Operations Research
Discrete Optimization via Simulation Using COMPASS
Operations Research
A Model Reference Adaptive Search Method for Global Optimization
Operations Research
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Stochastic Kriging for Simulation Metamodeling
Operations Research
Adaptive search with stochastic acceptance probabilities for global optimization
Operations Research Letters
Efficient discrete optimization via simulation using stochastic kriging
Proceedings of the Winter Simulation Conference
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Random search algorithms are often used to solve optimization-via-simulation (OvS) problems. The most critical component of a random search algorithm is the sampling distribution that is used to guide the allocation of the search effort. A good sampling distribution can balance the tradeoff between the effort used in searching around the current best solution (which is called exploitation) and the effort used in searching largely unknown regions (which is called exploration). However, most of the random search algorithms for OvS problems have difficulties in balancing this tradeoff in a seamless way. In this paper we propose a new random search algorithm, called the Gaussian Process-based Search (GPS) algorithm, which derives a sampling distribution from a fast fitted Gaussian process in each iteration of the algorithm. We show that the sampling distribution has the desired properties and it can automatically balance the exploitation and exploration tradeoff.