# Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler

arXiv: https://arxiv.org/abs/2302.01464 · HTML: https://mlsanity.com/paper/2302.01464/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 13 of 13)

- [Fast Genetic Algorithms](https://mlsanity.com/paper/1703.03334/) (2017)
- [IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics](https://mlsanity.com/paper/2111.04077/) (2021)
- [IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics](https://mlsanity.com/paper/2007.03953/) (2020)
- [Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms](https://mlsanity.com/paper/2006.11444/) (2020)
- [Optimization of Chance-Constrained Submodular Functions](https://mlsanity.com/paper/1911.11451/) (2019)
- [Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits](https://mlsanity.com/paper/2204.05597/) (2022)
- [Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem](https://mlsanity.com/paper/2004.03205/) (2020)
- [The (1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate](https://mlsanity.com/paper/1704.02191/) (2017)
- [Maximizing Submodular or Monotone Functions under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms](https://mlsanity.com/paper/2006.12773/) (2020)
- [Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints](https://mlsanity.com/paper/2012.08738/) (2020)
- [The Packing While Traveling Problem](https://mlsanity.com/paper/1512.08831/) (2015)
- [Towards a More Practice-Aware Runtime Analysis of Evolutionary Algorithms](https://mlsanity.com/paper/1812.00493/) (2018)
- [Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation](https://mlsanity.com/paper/1901.05573/) (2019)

## Built on by (most cited first, 1 of 1)

- [LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics](https://mlsanity.com/paper/2405.20132/) (2024)

## Abstract (arXiv)

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.