DACBench: A Benchmark Library for Dynamic Algorithm Configuration

authored by

Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Thomas Lindauer

Abstract

Dynamic Algorithm Configuration (DAC) aims to dynamically control a target algorithm's hyperparameters in order to improve its performance. Several theoretical and empirical results have demonstrated the benefits of dynamically controlling hyperparameters in domains like evolutionary computation, AI Planning or deep learning. Replicating these results, as well as studying new methods for DAC, however, is difficult since existing benchmarks are often specialized and incompatible with the same interfaces. To facilitate benchmarking and thus research on DAC, we propose DACBench, a benchmark library that seeks to collect and standardize existing DAC benchmarks from different AI domains, as well as provide a template for new ones. For the design of DACBench, we focused on important desiderata, such as (i) flexibility, (ii) reproducibility, (iii) extensibility and (iv) automatic documentation and visualization. To show the potential, broad applicability and challenges of DAC, we explore how a set of six initial benchmarks compare in several dimensions of difficulty.

Organisation(s)

Automatic Image Interpretation Section
Machine Learning Section
Institute of Information Processing

External Organisation(s)

University of Freiburg

Type

Conference contribution

Pages

1668-1674

No. of pages

7

Publication date

2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Artificial Intelligence

Electronic version(s)

https://arxiv.org/abs/2105.08541 (Access: Open)
https://doi.org/10.24963/ijcai.2021/230 (Access: Open)