Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical performance guarantees for a wide class of submodular problems under various types of constraints while clearly outperforming standard greedy approximation algorithms. This paper introduces a setup for benchmarking algorithms for submodular optimization problems with the aim to provide researchers with a framework to enhance and compare the performance of new algorithms for submodular problems. The focus is on the development of iterative search algorithms such as evolutionary algorithms with the implementation provided and integrated into IOHprofiler which allows for tracking and comparing the progress and performance of iterative search algorithms. We present a range of submodular optimization problems that have been integrated into IOHprofiler and show how the setup can be used for analyzing and comparing iterative search algorithms in various settings.

翻译：子模函数在优化领域扮演着关键角色，因为它们能够对许多面临收益递减的现实世界问题进行建模。进化算法已被证明在各类约束条件下，对于广泛的子模问题能够获得强理论性能保证，同时明显优于标准的贪心近似算法。本文介绍了一种用于子模优化问题上进行算法基准测试的框架，旨在为研究人员提供增强和比较子模问题新算法性能的平台。研究重点放在迭代搜索算法（如进化算法）的开发上，相关实现代码已提供并集成到IOHprofiler中，该工具可追踪并比较迭代搜索算法的进展与性能。我们展示了一系列已集成到IOHprofiler中的子模优化问题，并说明该框架如何用于在不同设置下分析和比较迭代搜索算法。