Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
This provides a framework for researchers in optimization to enhance and compare new algorithms, but it is incremental as it builds on existing benchmarking tools.
The paper tackles the need for standardized benchmarking of algorithms for submodular optimization problems by introducing a setup integrated into IOHprofiler, enabling researchers to track and compare the performance of iterative search algorithms like evolutionary algorithms across various problem settings.
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.