DACBench: A Benchmark Library for Dynamic Algorithm Configuration

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.

Authors

• Theresa Eimer
• André Biedenkapp Albert-Ludwigs-Universität
• Maximilian Reimer
• Steven Adriansen
• Frank Hutter
• Marius Lindauer Institute of Artificial Intelligence L3S Research Center Leibniz University Hannover

Keywords

• Heuristic Search and Game Playing: Evaluation and Analysis
• Heuristic Search and Game Playing: Heuristic Search and Machine Learning
• Heuristic Search and Game Playing: Meta-Reasoning and Meta-Heuristics