# Seeking and leveraging alternative variable dependency concepts in gray-box-elusive bimodal land-use allocation problems

arXiv: https://arxiv.org/abs/2504.11882 · HTML: https://mlsanity.com/paper/2504.11882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [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)
- [Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems](https://mlsanity.com/paper/2009.08929/) (2020)

## Abstract (arXiv)

Solving land-use allocation problems can help us to deal with some of the most urgent global environmental issues. Since these problems are NP-hard, effective optimizers are needed to handle them. The knowledge about variable dependencies allows for proposing such tools. However, in this work, we consider a real-world multi-objective problem for which standard variable dependency discovery techniques are inapplicable. Therefore, using linkage-based variation operators is unreachable. To address this issue, we propose a definition of problem-dedicated variable dependency. On this base, we propose obtaining masks of dependent variables. Using them, we construct three novel crossover operators. The results concerning real-world test cases show that introducing our propositions into two well-known optimizers (NSGA-II, MOEA/D) dedicated to multi-objective optimization significantly improves their effectiveness.