Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions

Published: 01 Jan 2025, Last Modified: 06 May 2025CoRR 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: 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.
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