学术报告
题目：
Optimization via Simulation Using Gaussian Process-based Search
时间：3月30日（星期五）上午10:00
地点：创新园大厦A1101
报告人：胡照林
Abstract
：
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 for solving discrete optimization-via-simulation(DOvS) problems. We show that the sampling distribution has the desired properties and it can automatically balance the exploitation and exploration tradeoff.
报告人简介：
胡照林，同济大学经管学院讲师。2008年本科毕业于浙江大学， 2011年在香港科技大学工业工程与物流管理系取得博士学位，主要研究方向为随机优化，仿真优化，模拟仿真。
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