A Survey of Methods for Automated Algorithm Configuration

TL;DR

This survey comprehensively reviews automated algorithm configuration methods, introduces taxonomies for classifying problem variants and approaches, and discusses future research directions in the field.

Contribution

It provides a complete classification scheme for AC problem variants and methods, addressing gaps in existing reviews and offering a structured overview of the field.

Findings

Taxonomies effectively categorize AC problem variants and methods.

Comparison of different AC approaches highlights their strengths and limitations.

Discussion of industry applications indicates practical relevance of AC methods.

Abstract

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.