论文标题
通过昂贵的评估优化离散空间：学习搜索框架
Optimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework
论文作者
论文摘要
我们考虑在离散空间（例如集合，序列，图形）上优化昂贵的黑框功能的问题。关键挑战是选择一系列组合结构来评估，以便尽快识别出高性能结构。我们的主要贡献是针对这个名为L2S-Disco的问题介绍和评估一个新的学习搜索框架。关键见解是采用每个步骤中由控制知识指导的搜索程序，以选择下一个结构，并在观察到新功能评估时改善控制知识。我们为L2S-DISCO提供了用于本地搜索程序的具体实例化，并在各种现实世界的基准测试中对其进行了经验评估。结果表明，L2S-DISCO对最新算法的功效在解决复杂的优化问题方面。
We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial structures to evaluate, in order to identify high-performing structures as quickly as possible. Our main contribution is to introduce and evaluate a new learning-to-search framework for this problem called L2S-DISCO. The key insight is to employ search procedures guided by control knowledge at each step to select the next structure and to improve the control knowledge as new function evaluations are observed. We provide a concrete instantiation of L2S-DISCO for local search procedure and empirically evaluate it on diverse real-world benchmarks. Results show the efficacy of L2S-DISCO over state-of-the-art algorithms in solving complex optimization problems.
