Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking

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 us...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2021-02
Hauptverfasser:	Meunier, Laurent, Rakotoarison, Herilalaina, Pak Kan Wong, Roziere, Baptiste, Rapin, Jeremy, Teytaud, Olivier, Moreau, Antoine, Doerr, Carola
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Benchmarks Collection Mixed integer Optimization Parameterization Solvers
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!