# Heuristic Search for Rank Aggregation with Application to Label Ranking

arXiv: https://arxiv.org/abs/2201.03893 · HTML: https://mlsanity.com/paper/2201.03893/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2022.

## Built on by (most cited first, 2 of 2)

- [A comparative analysis of rank aggregation methods for the partial label ranking problem](https://mlsanity.com/paper/2502.17077/) (2025)
- [Calibrated Preference Learning: The Case of Label Ranking](https://mlsanity.com/paper/2605.30447/) (2026)

## Abstract (arXiv)

Rank aggregation aims to combine the preference rankings of a number of alternatives from different voters into a single consensus ranking. As a useful model for a variety of practical applications, however, it is a computationally challenging problem. In this paper, we propose an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and a late acceptance local search reinforced by an efficient incremental evaluation technique. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, which is an important machine learning task.