Pitfalls and Best Practices in Algorithm Configuration

verfasst von

Katharina Eggensperger, Marius Lindauer, Frank Hutter

Abstract

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.

Externe Organisation(en)

Albert-Ludwigs-Universität Freiburg

Typ

Artikel

Journal

Journal of Artificial Intelligence Research

Band

64

Seiten

861-893

Anzahl der Seiten

33

ISSN

1076-9757

Publikationsdatum

16.04.2019

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence

Elektronische Version(en)

https://arxiv.org/abs/1705.06058v3 (Zugang: Offen)
https://doi.org/10.1613/jair.1.11420 (Zugang: Offen)