# Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization

arXiv: https://arxiv.org/abs/2411.04547 · HTML: https://mlsanity.com/paper/2411.04547/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 65 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [Knowledge Transfer for Dynamic Multi-objective Optimization with a Changing Number of Objectives](https://mlsanity.com/paper/2306.10668/) (2023)

## Abstract (arXiv)

Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the context of interactive EMOAs, preference information elicited from the DM during the optimization process can be leveraged to identify and discard irrelevant objectives, a crucial step when objective evaluations are computationally expensive. However, much of the existing literature fails to account for the dynamic nature of DM preferences, which can evolve throughout the decision-making process and affect the relevance of objectives. This study addresses this limitation by simulating dynamic shifts in DM preferences within a ranking-based interactive algorithm. Additionally, we propose methods to discard outdated or conflicting preferences when such shifts occur. Building on prior research, we also introduce a mechanism to safeguard relevant objectives that may become trapped in local or global optima due to the diminished correlation with the DM-provided rankings. Our experimental results demonstrate that the proposed methods effectively manage evolving preferences and significantly enhance the quality and desirability of the solutions produced by the algorithm.