Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler

TL;DR

This paper presents a benchmarking framework within IOHprofiler for evaluating and comparing iterative search algorithms, especially evolutionary algorithms, on submodular optimization problems, facilitating performance analysis and development.

Contribution

It introduces a new benchmarking setup for submodular optimization algorithms, integrated into IOHprofiler, enabling systematic performance tracking and comparison.

Findings

Framework supports various submodular problems

Enables performance comparison of evolutionary algorithms

Facilitates analysis of iterative search strategies

Abstract

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.