# An Efficient Evolutionary Algorithm for Minimum Cost Submodular Cover

arXiv: https://arxiv.org/abs/1908.01029 · HTML: https://mlsanity.com/paper/1908.01029/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

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

- [Mathematical Runtime Analysis for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)](https://mlsanity.com/paper/2112.08581/) (2021)
- [Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Many Objectives](https://mlsanity.com/paper/2211.13084/) (2022)
- [The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem](https://mlsanity.com/paper/2305.13459/) (2023)

## Abstract (arXiv)

In this paper, the Minimum Cost Submodular Cover problem is studied, which is to minimize a modular cost function such that the monotone submodular benefit function is above a threshold. For this problem, an evolutionary algorithm EASC is introduced that achieves a constant, bicriteria approximation in expected polynomial time; this is the first polynomial-time evolutionary approximation algorithm for Minimum Cost Submodular Cover. To achieve this running time, ideas motivated by submodularity and monotonicity are incorporated into the evolutionary process, which likely will extend to other submodular optimization problems. In a practical application, EASC is demonstrated to outperform the greedy algorithm and converge faster than competing evolutionary algorithms for this problem.