A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models

Schneider, Lennart; Pfisterer, Florian; Thomas, Janek und Bischl, Bernd (2022): A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models. GECCO '22: Genetic and Evolutionary Computation Conference, Boston Massachusetts (hybrid), July 9-13, 2022. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Association of Computing Machinery: New York. S. 2136-2142

Abstract

The goal of Quality Diversity Optimization is to generate a collection of diverse yet high-performing solutions to a given problem at hand. Typical benchmark problems are, for example, finding a repertoire of robot arm configurations or a collection of game playing strategies. In this paper, we propose a set of Quality Diversity Optimization problems that tackle hyperparameter optimization of machine learning models - a so far underexplored application of Quality Diversity Optimization. Our benchmark problems involve novel feature functions, such as interpretability or resource usage of models. To allow for fast and efficient benchmarking, we build upon YAHPO Gym, a recently proposed open source benchmarking suite for hyperparameter optimization that makes use of high performing surrogate models and returns these surrogate model predictions instead of evaluating the true expensive black box function. We present results of an initial experimental study comparing different Quality Diversity optimizers on our benchmark problems. Furthermore, we discuss future directions and challenges of Quality Diversity Optimization in the context of hyperparameter optimization.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Ort: Association of Computing Machinery
• Bemerkung: ISBN 978-1-4503-9268-6
• Sprache: Englisch