# A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty

arXiv: https://arxiv.org/abs/1809.02437 · HTML: https://mlsanity.com/paper/1809.02437/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

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

- [Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input](https://mlsanity.com/paper/2608.02073/) (2026)

## Abstract (arXiv)

We consider box-constrained robust optimisation problems with implementation uncertainty. In this setting, the solution that a decision maker wants to implement may become perturbed. The aim is to find a solution that optimises the worst possible performance over all possible perturbances. Previously, only few generic search methods have been developed for this setting. We introduce a new approach for a global search, based on placing a largest empty hypersphere. We do not assume any knowledge on the structure of the original objective function, making this approach also viable for simulation-optimisation settings. In computational experiments we demonstrate a strong performance of our approach in comparison with state-of-the-art methods, which makes it possible to solve even high-dimensional problems.