Opening the Black Box: Automated Software Analysis for Algorithm Selection
Opening the Black Box: Automated Software Analysis for Algorithm Selection
复制标题
DOI：
--
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Damir Pulatov;Marie Anastacio;Lars Kotthoff;H. Hoos
中科院分区：
文献类型：
--
作者：
Damir Pulatov;Marie Anastacio;Lars Kotthoff;H. Hoos
通过元算法技术，例如自动算法选择和配置，在AI的许多领域都实现了令人印象深刻的性能改进。然而，现有的技术将它们所应用的目标算法视为黑箱-对其内部工作原理一无所知。这使得元算法技术被广泛使用，但留下了未开发的潜在性能改进，使从目标算法的更深入的分析获得的信息。在本文中，我们打开黑盒子，而不牺牲通用的元算法技术的自动分析算法的适用性。我们展示了如何使用这些信息来执行算法选择，并展示了改进的性能相比，以前的方法，处理算法的黑盒子。
Impressive performance improvements have been achieved in many areas of AI by meta-algorithmic techniques, such as automated algorithm selection and configuration. However, existing techniques treat the target algorithms they are applied to as black boxes – nothing is known about their inner workings. This allows meta-algorithmic techniques to be used broadly, but leaves untapped potential performance improvements enabled by information gained from a deeper analysis of the target algorithms. In this paper, we open the black box without sacrificing universal applicability of meta-algorithmic techniques by automatically analyzing algorithms. We show how to use this information to perform algorithm selection, and demonstrate improved performance compared to previous approaches that treat algorithms as black boxes.