Multi-Objective Optimization and Hyperparameter Tuning With Desirability Functions

2025-03-30

AI Summary

This paper addresses the interpretability deficit and inflexibility of scalarization in multi-objective optimization (MOO) and hyperparameter optimization (HPO). To this end, it proposes a unified optimization framework grounded in utility theory. Methodologically, it presents the first systematic Python implementation of Kuhn’s utility theory, integrated into the SPOT platform; it supports direct optimization, surrogate-assisted sequential optimization (e.g., via Gaussian processes), and ML hyperparameter search—all enabled by configurable, interpretable utility modeling for principled scalarization. Key contributions include: (1) an open-source, production-ready package—spotdesirability; (2) empirical validation across three representative scenarios, demonstrating significant improvements in optimization efficiency, robustness, and decision transparency; and (3) the first scalable, reproducible, utility-theoretic unification of MOO and HPO.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Introduces desirability functions for multi-objective optimization
• Demonstrates Python implementation for optimization and tuning
• Applies desirability to classical and surrogate-based optimization

Innovation

• Python implementation of desirability functions
• Multi-objective optimization using surrogate models
• Hyperparameter tuning with desirability approach