Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
In: De.arXiv.org (2025-03-30)
2025-03-30Essay / Article in JournalOA Green
Faculty of Computer Science and Engineering Science » Institut für Data Science, Engineering, and Analytics
Title:
Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions
Author:
Bartz-Beielstein, ThomasTH Köln
- DHSB-ID
- THK0001582
- GND
- 124999476
- ORCID
-
0000-0002-5938-5158
- SCOPUS
- 57190702501
- Other
- person connected with TH Köln
Date published:
2025-03-30
„Publication Channel“:
OA Green
arXiv.org ID
arXiv.org ID
WWW URL:
Language of text:
English
Keyword, Topic:
desirability function ; multi-objective optimization ; surrogate modeling ; hyperparameter tuning
Type of resource:
Text
Access Rights:
open access
Practice Partner:
No
Category:
Research
Part of statistic:
Part of statistic
Abstract in English:
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.