Explainable Benchmarking for Iterative Optimization Heuristics

Abstract

This paper summarizes our recent work published in ACM Transactions on Evolutionary Learning and Optimization (2024), entitled "Explainable Benchmarking for Iterative Optimization Heuristics". We introduce IOHxplainer, a novel software library designed to systematically and explainably analyze the performance impact of various components and hyperparameters in modular or parametrized optimization heuristics. By leveraging explainable AI techniques such as SHAP values and machine learning models, IOHxplainer allows for an in-depth analysis of the contributions and interactions of algorithmic components across diverse benchmark scenarios.

Our approach significantly enhances transparency in algorithm benchmarking, facilitating informed algorithm selection and tuning. The original paper is available [9].