# Fast Pareto Optimization Using Sliding Window Selection

arXiv: https://arxiv.org/abs/2305.07178 · HTML: https://mlsanity.com/paper/2305.07178/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 11 May 2023.

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

- [Pareto Optimization for Subset Selection with Dynamic Cost Constraints](https://mlsanity.com/paper/1811.07806/) (2018)
- [Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms](https://mlsanity.com/paper/2006.11444/) (2020)
- [Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences](https://mlsanity.com/paper/2104.09884/) (2021)

## Built on by (most cited first, 3 of 3)

- [Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems](https://mlsanity.com/paper/2404.11907/) (2024)
- [Sliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints](https://mlsanity.com/paper/2406.04899/) (2024)
- [Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization](https://mlsanity.com/paper/2406.13414/) (2024)

## Abstract (arXiv)

Pareto optimization using evolutionary multi-objective algorithms has been widely applied to solve constrained submodular optimization problems. A crucial factor determining the runtime of the used evolutionary algorithms to obtain good approximations is the population size of the algorithms which grows with the number of trade-offs that the algorithms encounter. In this paper, we introduce a sliding window speed up technique for recently introduced algorithms. We prove that our technique eliminates the population size as a crucial factor negatively impacting the runtime and achieves the same theoretical performance guarantees as previous approaches within less computation time. Our experimental investigations for the classical maximum coverage problem confirms that our sliding window technique clearly leads to better results for a wide range of instances and constraint settings.