黑箱优化再访：通过大规模基准测试改进算法选择向导
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
摘要：
针对机器学习的黑盒优化中的现有研究遭受低概括性，这是由用于训练和测试不同优化算法的问题实例的典型选择性选择引起的。除其他问题外，这种做法促进了过度拟合和性能低下的用户指南。为了解决这个缺点，我们在这项工作中提出了一个基准套件，OptimSuite，它涵盖了广泛的黑盒优化问题，从学术基准到现实世界的应用，从离散数值到混合整数问题，从小到非常大规模的问题，从动态到静态问题的噪声，我们证明了这样一个广泛的集合的优点，从它派生的自动黑盒优化（ABBO），一个通用的算法选择向导。使用三种不同类型的算法选择技术，ABBO在所有基准测试套件上实现了具有竞争力的性能。它在其中一些方面的表现明显优于以前的最先进技术，包括YABBOB和LSGO。ABBO依赖于许多高质量的基础组件。它的优异性能是在没有任何特定任务参数化的情况下获得的。OptimSuite基准集合、ABBO向导及其基本求解器都已合并到开源Nevergrad平台中，可用于可重复的研究。
英文摘要：
Existing studies in black-box optimization for machine learning suffer from low generalizability, caused by a typically selective choice of problem instances used for training and testing different optimization algorithms. Among other issues, this practice promotes overfitting and poor-performing user guidelines. To address this shortcoming, we propose in this work a benchmark suite, OptimSuite, which covers a broad range of black-box optimization problems, ranging from academic benchmarks to real-world applications, from discrete over numerical to mixed-integer problems, from small to very large-scale problems, from noisy over dynamic to static problems, etc. We demonstrate the advantages of such a broad collection by deriving from it Automated Black Box Optimizer (ABBO), a general-purpose algorithm selection wizard. Using three different types of algorithm selection techniques, ABBO achieves competitive performance on all benchmark suites. It significantly outperforms previous state of the art on some of them, including YABBOB and LSGO. ABBO relies on many high-quality base components. Its excellent performance is obtained without any task-specific parametrization. The OptimSuite benchmark collection, the ABBO wizard and its base solvers have all been merged into the open-source Nevergrad platform, where they are available for reproducible research.
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