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, including a tool called GenericWrapper4AC for preventing the many possible problems in measuring the performance of the algorithm being optimized by executing it in a standardized, controlled manner.
- Pitfalls and Best Practices in Algorithm Configuration (on Arxiv)
Data and script for the experiments in this paper: BestPracticeExperiments.tar