论文标题
调整零订单算法以基于比较的优化
Adapting Zeroth Order Algorithms for Comparison-Based Optimization
论文作者
论文摘要
基于比较的优化（CBO）是一种优化范式，它仅假定对目标函数F（x）的访问非常有限。尽管CBO与现实世界应用的相关性越来越大，但与相邻的零级优化（Zoo）相比，该领域几乎没有得到关注。在这项工作中，我们提出了一种相对简单的方法，用于将动物园算法转换为CBO算法，从而大大扩大了CBO已知算法的池。通过pycutest，我们针对一系列不受约束的问题对这些算法进行了基准测试。然后，我们使用高参数调整来确定某些算法参数的最佳值，并利用可视化工具，例如热图和线图，以进行解释。我们所有的代码均可在https://github.com/ishaslavin/comparison_based_optimization上找到。
Comparison-Based Optimization (CBO) is an optimization paradigm that assumes only very limited access to the objective function f(x). Despite the growing relevance of CBO to real-world applications, this field has received little attention as compared to the adjacent field of Zeroth-Order Optimization (ZOO). In this work we propose a relatively simple method for converting ZOO algorithms to CBO algorithms, thus greatly enlarging the pool of known algorithms for CBO. Via PyCUTEst, we benchmarked these algorithms against a suite of unconstrained problems. We then used hyperparameter tuning to determine optimal values of the parameters of certain algorithms, and utilized visualization tools such as heat maps and line graphs for purposes of interpretation. All our code is available at https://github.com/ishaslavin/Comparison_Based_Optimization.
