Abstract: 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.
Keywords: algorithm selection, meta-algorithmics, algorithm portfolios
One-sentence Summary: In this paper we show how to analyze algorithms to improve algorithm selection.
Track: Main track
Reproducibility Checklist: Yes
Broader Impact Statement: Yes
Paper Availability And License: Yes
Code Of Conduct: Yes
Reviewers: Damir Pulatov, email@example.com
Main Paper And Supplementary Material: pdf
CPU Hours: 0
GPU Hours: 0
TPU Hours: 0
Evaluation Metrics: No