Pitfalls and Best Practices in Algorithm Configuration
Pitfalls and Best Practices in Algorithm Configuration
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DOI：
10.1613/jair.1.11420
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发表时间：
2019-01-01
影响因子：
5
通讯作者：
Hutter, Frank
中科院分区：
文献类型：
--
作者：
Eggensperger, Katharina;Lindauer, Marius;Hutter, Frank
良好的参数设置对于在人工智能（AI）的许多领域实现高性能至关重要，例如命题可满足性求解，AI规划，调度和机器学习（特别是深度学习）。自动算法配置方法最近在AI社区受到了广泛关注，因为它们取代了繁琐，不可复制和容易出错的手动参数调整，并可以带来新的最先进的性能。然而，算法配置的实际应用在实验设计中容易出现几个（通常是微妙的）陷阱，这些陷阱会使该过程无效。我们确定了几个常见问题，并提出了避免这些问题的最佳实践。作为自动处理尽可能多的这些问题的一种可能性，我们还提出了一个名为GenericWrapper4AC的工具
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 con figuration 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