# Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions

arXiv: https://arxiv.org/abs/1811.03862 · HTML: https://mlsanity.com/paper/1811.03862/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?](https://mlsanity.com/paper/2407.17482/) (2024)
- [A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement](https://mlsanity.com/paper/2208.03685/) (2022)
- [Bayesian preference elicitation for decision support in multiobjective optimization](https://mlsanity.com/paper/2507.16999/) (2025)
- [Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?](https://mlsanity.com/paper/2604.09417/) (2026)
- [One Step Preference Elicitation in Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2105.13278/) (2021)
- [Multi-objective robust optimization using adaptive surrogate models for problems with mixed continuous-categorical parameters](https://mlsanity.com/paper/2203.01996/) (2022)

## Abstract (arXiv)

Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed.