# Memetic Multilevel Hypergraph Partitioning

arXiv: https://arxiv.org/abs/1710.01968 · HTML: https://mlsanity.com/paper/1710.01968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [More Recent Advances in (Hyper)Graph Partitioning](https://mlsanity.com/paper/2205.13202/) (2022)
- [K-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning](https://mlsanity.com/paper/2305.06167/) (2023)
- [Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy](https://mlsanity.com/paper/2106.07501/) (2021)
- [An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning](https://mlsanity.com/paper/2403.17395/) (2024)

## Abstract (arXiv)

Hypergraph partitioning has a wide range of important applications such as VLSI design or scientific computing. With focus on solution quality, we develop the first multilevel memetic algorithm to tackle the problem. Key components of our contribution are new effective multilevel recombination and mutation operations that provide a large amount of diversity. We perform a wide range of experiments on a benchmark set containing instances from application areas such VLSI, SAT solving, social networks, and scientific computing. Compared to the state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our new algorithm computes the best result on almost all instances.