  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.
