Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
This work addresses the issue of overfitting and poor guidelines in black-box optimization for machine learning researchers and practitioners, though it is incremental as it builds on existing algorithm selection techniques.
The authors tackled the problem of low generalizability in black-box optimization by creating a broad benchmark suite, OptimSuite, and using it to develop an algorithm selection wizard, ABBO, which achieves competitive performance and significantly outperforms previous state-of-the-art methods on benchmarks like YABBOB and LSGO.
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.