# Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation

arXiv: https://arxiv.org/abs/2207.14112 · HTML: https://mlsanity.com/paper/2207.14112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2022.

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

- [Diversity from Human Feedback](https://mlsanity.com/paper/2310.06648/) (2023)
- [Trust Region-Based Bayesian Optimisation to Discover Diverse Solutions](https://mlsanity.com/paper/2511.00750/) (2025)

## Abstract (arXiv)

Diversification in a set of solutions has become a hot research topic in the evolutionary computation community. It has been proven beneficial for optimisation problems in several ways, such as computing a diverse set of high-quality solutions and obtaining robustness against imperfect modeling. For the first time in the literature, we adapt the evolutionary diversity optimisation for a real-world combinatorial problem, namely patient admission scheduling. We introduce an evolutionary algorithm to achieve structural diversity in a set of solutions subjected to the quality of each solution. We also introduce a mutation operator biased towards diversity maximisation. Finally, we demonstrate the importance of diversity for the aforementioned problem through a simulation.