# Clustering-Based Subset Selection in Evolutionary Multiobjective Optimization

arXiv: https://arxiv.org/abs/2108.08453 · HTML: https://mlsanity.com/paper/2108.08453/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Benchmarking Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization](https://mlsanity.com/paper/2201.06700/) (2022)
- [Towards KAB2S: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem](https://mlsanity.com/paper/2206.12906/) (2022)
- [On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications](https://mlsanity.com/paper/2311.00964/) (2023)

## Abstract (arXiv)

Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering, as a classic method to group similar data points together, has been used for subset selection in some fields. However, clustering-based methods have not been evaluated in the context of subset selection from solution sets obtained by EMO algorithms. In this paper, we first review some classic clustering algorithms. We also point out that another popular subset selection method, i.e., inverted generational distance (IGD)-based subset selection, can be viewed as clustering. Then, we perform a comprehensive experimental study to evaluate the performance of various clustering algorithms in different scenarios. Experimental results are analyzed in detail, and some suggestions about the use of clustering algorithms for subset selection are derived. Additionally, we demonstrate that decision maker's preference can be introduced to clustering-based subset selection.