Explainable Landscape Analysis in Automated Algorithm Performance Prediction
This work addresses the need for explainability in algorithm selection for optimization problems, but it is incremental as it focuses on analyzing existing methods without introducing new ones.
The study tackled the problem of predicting optimization algorithm performance using landscape features, finding that different supervised ML models use features differently with no common informative pattern.
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.