# Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations

arXiv: https://arxiv.org/abs/2503.17397 · HTML: https://mlsanity.com/paper/2503.17397/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Optimization by Pairwise Linkage Detection, Incremental Linkage Set, and Restricted / Back Mixing: DSMGA-II](https://mlsanity.com/paper/1807.11669/) (2018)
- [Incremental Recursive Ranking Grouping for Large Scale Global Optimization](https://mlsanity.com/paper/2206.04168/) (2022)

## Built on by (most cited first, 1 of 1)

- [Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure](https://mlsanity.com/paper/2604.11862/) (2026)

## Abstract (arXiv)

Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL is important for practice. In this work, we analytically estimate the size of a population sufficient for obtaining a perfect decomposition in case of concatenations of certain unitation-based functions. The experimental study confirms the accuracy of the proposed estimate. Finally, using the proposed estimate, we identify those problem types that may be considered hard for SLL-using optimizers.