Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
In: De.arXiv.org (2025-03-30)
2025-03-30Aufsatz / Artikel in ZeitschriftOA Grün
Fakultät für Informatik und Ingenieurwissenschaften » Institut für Data Science, Engineering, and Analytics
Titel:
Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
Autor*in:
Bartz-Beielstein, ThomasTH Köln
- DHSB-ID
- THK0001582
- GND
- 124999476
- ORCID
-
0000-0002-5938-5158
- SCOPUS
- 57190702501
- Sonstiges
- der TH Köln zugeordnete Person
Veröffentlicht am:
2025-03-30
OA-Publikationsweg:
OA Grün
arXiv.org ID
arXiv.org ID
WWW URL:
Sprache des Textes:
Englisch
Schlagwort, Thema:
desirability function ; multi-objective optimization ; surrogate modeling ; hyperparameter tuning
Ressourcentyp:
Text
Access Rights:
Open Access
Praxispartner*in:
Nein
Kategorie:
Forschung
Teil der Statistik:
Teil der Statistik
Abstract in Englisch:
The goal of this article is to provide an introduction to the desirability function approach to multi-objective optimization (direct and surrogate model-based), and multi-objective hyperparameter tuning. This work is based on the paper by Kuhn (2016). It presents a `Python` implementation of Kuhn's `R` package `desirability`. The `Python` package `spotdesirability` is available as part of the `sequential parameter optimization` framework. After a brief introduction to the desirability function approach is presented, three examples are given that demonstrate how to use the desirability functions for classical optimization, surrogate-model based optimization, and hyperparameter tuning.