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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)