A Nonparametric Approach to Noisy and Costly Optimization
A Nonparametric Approach to Noisy and Costly Optimization
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噪声和成本优化的非参数方法
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
David A. Cohn
中科院分区：
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作者：
Brigham S. Anderson;A. Moore;David A. Cohn
本文描述了一种用很少的函数求值来优化含噪函数的非参数方法。该算法利用简单的几何关系的非参数推理来最小化通常是最优化的两个因素。噪声和成本输出可以包含大量的噪声或误差，而时间或金钱只允许少数几个实验。这里使用两两等分方法试图使稳健和有效的实验设计的过程自动化。真实世界的函数也倾向于违反连续和高斯噪声的传统假设，因为非参数统计量不依赖于这些假设。该算法可以用较少的噪声限制来优化各种各样的现象在几个不同的测试函数上，将S算法与三种竞争算法阿米巴Pmax和Q的性能进行了比较，结果表明，这些算法在竞争性能和抗噪声能力方面都有很好的表现
This paper describes Pairwise Bisection a nonparametric approach to optimizing a noisy function with few function evaluations The algorithm uses nonparametric reasoning about simple geometric relationships to nd minima e ciently Two factors often frus trate optimization noise and cost Output can contain signi cant quantities of noise or error while time or money allows for only a handful of experiments Pairwise bisection is used here to attempt to automate the pro cess of robust and e cient experiment design Real world functions also tend to violate tra ditional assumptions of continuousness and Gaussian noise Since nonparametric statis tics do not depend on these assumptions this algorithm can optimize a wide variety of phenomena with fewer restrictions placed on noise The algorithm s performance is com pared to that of three competing algorithms Amoeba PMAX and Q on several di erent test functions Results on these functions in dicate competitive performance and superior resistance to noise