# Energy-aware frugal Bayesian optimization

arXiv: https://arxiv.org/abs/2609.31638 · HTML: https://mlsanity.com/paper/2609.31638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [pymoo: Multi-objective Optimization in Python](https://mlsanity.com/paper/2002.04504/) (2020)
- [A Nonmyopic Approach to Cost-Constrained Bayesian Optimization](https://mlsanity.com/paper/2106.06079/) (2021)
- [Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization](https://mlsanity.com/paper/2205.08835/) (2022)
- [The Hypervolume Indicator: Problems and Algorithms](https://mlsanity.com/paper/2005.00515/) (2020)
- [Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization](https://mlsanity.com/paper/1611.02120/) (2016)
- [Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design](https://mlsanity.com/paper/2504.08682/) (2025)
- [A distance for mixed-variable and hierarchical domains with meta variables](https://mlsanity.com/paper/2405.13073/) (2024)
- [Regularized infill criteria for multi-objective Bayesian optimization with application to aircraft design](https://mlsanity.com/paper/2504.08671/) (2025)
- [Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design](https://mlsanity.com/paper/2506.22621/) (2025)

## Abstract (arXiv)

Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason why scaled architecture and multidisciplinary design optimization problems are difficult to address, even with sample-efficient Bayesian optimizers. In this paper, a metric quantifying the computational energy footprint is introduced within a Bayesian optimization framework to guide the parameter setting of a model towards configurations that balance both performance and frugality. The computer experiments highlighted existing tradeoffs between optimum convergence and the underlying energy footprint, and sometimes resulted in both a better-found optimum and lower energy consumption.