Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

B Bischl, M Binder, M Lang, T Pielok… - … : Data Mining and …, 2023 - Wiley Online Library
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

A new taxonomy of global optimization algorithms

J Stork, AE Eiben, T Bartz-Beielstein - Natural Computing, 2022 - Springer
Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations
have become state of the art in algorithm design for solving real-world optimization …

Multi-Objective Hyperparameter Optimization--An Overview

F Karl, T Pielok, J Moosbauer, F Pfisterer… - arXiv preprint arXiv …, 2022 - arxiv.org
Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

Nullifying the inherent bias of non-invariant exploratory landscape analysis features

RP Prager, H Trautmann - International Conference on the Applications of …, 2023 - Springer
Exploratory landscape analysis (ELA) in single-objective black-box optimization relies on a
comprehensive and large set of numerical features characterizing problem instances. Those …

Constrained multi-objective optimization with a limited budget of function evaluations

R de Winter, P Bronkhorst, B van Stein, T Bäck - Memetic Computing, 2022 - Springer
This paper proposes the Self-Adaptive algorithm for Multi-Objective Constrained
Optimization by using Radial Basis Function Approximations, SAMO-COBRA. This algorithm …

A collection of deep learning-based feature-free approaches for characterizing single-objective continuous fitness landscapes

MV Seiler, RP Prager, P Kerschke… - Proceedings of the …, 2022 - dl.acm.org
Exploratory Landscape Analysis is a powerful technique for numerically characterizing
landscapes of single-objective continuous optimization problems. Landscape insights are …

Automated algorithm selection in single-objective continuous optimization: a comparative study of deep learning and landscape analysis methods

RP Prager, MV Seiler, H Trautmann… - … Conference on Parallel …, 2022 - Springer
In recent years, feature-based automated algorithm selection using exploratory landscape
analysis has demonstrated its great potential in single-objective continuous black-box …

Multi-objective hyperparameter optimization in machine learning—An overview

F Karl, T Pielok, J Moosbauer, F Pfisterer… - ACM Transactions on …, 2023 - dl.acm.org
Hyperparameter optimization constitutes a large part of typical modern machine learning
(ML) workflows. This arises from the fact that ML methods and corresponding preprocessing …

SAMO-COBRA: a fast surrogate assisted constrained multi-objective optimization algorithm

R de Winter, B van Stein, T Bäck - International conference on evolutionary …, 2021 - Springer
This paper proposes a novel Self-Adaptive algorithm for Multi-Objective Constrained
Optimization by using Radial Basis Function Approximations, SAMO-COBRA. The algorithm …

Hyperparameter Tuning: The Art of Fine-Tuning Machine and Deep Learning Models to Improve Metric Results

PP Ippolito - Applied data science in tourism: Interdisciplinary …, 2022 - Springer
Hyperparameter tuning is considered one of the most important steps in the machine
learning pipeline and can turn, what may be viewed as, an “unsuccessful” model into a solid …