Pitfalls and Best Practices in Algorithm Configuration

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 m...

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Bibliographic Details
Published in:arXiv.org 2019-03
Main Authors: Eggensperger, Katharina, Lindauer, Marius, Hutter, Frank
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Configurations
Design of experiments
Machine learning
Parameters
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Summary: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.
ISSN:2331-8422