Explainable Landscape Analysis in Automated Algorithm Performance Prediction

TL;DR

This paper explores how different supervised machine learning models utilize landscape features to predict algorithm performance, highlighting the importance of model selection and the lack of a universal feature importance pattern.

Contribution

It investigates the expressiveness of landscape features across various ML models, emphasizing the impact of model choice on feature utilization in performance prediction.

Findings

Model selection critically affects feature utilization.

No universal pattern in feature importance across models.

Different ML models leverage landscape features differently.

Abstract

Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance. For this purpose, recent studies learn a supervised machine learning (ML) model using a set of problem landscape features linked to the performance achieved by the optimization algorithm. However, these models are black-box with the only goal of achieving good predictive performance, without providing explanations which landscape features contribute the most to the prediction of the performance achieved by the optimization algorithm. In this study, we investigate the expressiveness of problem landscape features utilized by different supervised ML models in automated algorithm performance prediction. The experimental results point out that the selection of the supervised ML method is crucial, since different supervised ML regression models utilize the problem landscape features differently and there is no common pattern with regard to which landscape features are the most informative.