# Sliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints

arXiv: https://arxiv.org/abs/2406.04899 · HTML: https://mlsanity.com/paper/2406.04899/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [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)
- [Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables](https://mlsanity.com/paper/2109.05799/) (2021)
- [Optimization of Chance-Constrained Submodular Functions](https://mlsanity.com/paper/1911.11451/) (2019)
- [Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem](https://mlsanity.com/paper/2004.03205/) (2020)
- [3-Objective Pareto Optimization for Problems with Chance Constraints](https://mlsanity.com/paper/2304.08774/) (2023)
- [Fast Pareto Optimization Using Sliding Window Selection](https://mlsanity.com/paper/2305.07178/) (2023)

## Abstract (arXiv)

Constrained single-objective problems have been frequently tackled by evolutionary multi-objective algorithms where the constraint is relaxed into an additional objective. Recently, it has been shown that Pareto optimization approaches using bi-objective models can be significantly sped up using sliding windows (Neumann and Witt, ECAI 2023). In this paper, we extend the sliding window approach to 3-objective formulations for tackling chance constrained problems. On the theoretical side, we show that our new sliding window approach improves previous runtime bounds obtained in (Neumann and Witt, GECCO 2023) while maintaining the same approximation guarantees. Our experimental investigations for the chance constrained dominating set problem show that our new sliding window approach allows one to solve much larger instances in a much more efficient way than the 3-objective approach presented in (Neumann and Witt, GECCO 2023).