title: Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
abstract: 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 benchmark collection, the ABBO wizard, its base solvers,
  as well as all experimental data are reproducible and open source in OptimSuite.
authors: Laurent Meunier Herilalaina Rakotoarison Jeremy Rapin Paco Wong Baptiste Roziere Olivier Teytaud Antoine Moreau Carola Doerr
hIndex: Olivier Teytaud 32