Personalizing Performance Regression Models to Black-Box Optimization Problems

TL;DR

This paper proposes personalized regression models and ensembles to improve performance prediction of optimization algorithms on diverse problem instances, demonstrating their effectiveness on the BBOB benchmark.

Contribution

It introduces personalized regression and ensemble methods tailored to specific problem types in optimization, moving beyond generic models.

Findings

Personalized models outperform generic ones in performance prediction.

Ensembles further improve prediction accuracy.

Approach validated on BBOB benchmark collection.

Abstract

Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques. In the context of numerical optimization, supervised regression approaches built on top of exploratory landscape analysis are becoming very popular. From the point of view of Machine Learning (ML), however, the approaches are often rather naive, using default regression or classification techniques without proper investigation of the suitability of the ML tools. With this work, we bring to the attention of our community the possibility to personalize regression models to specific types of optimization problems. Instead of aiming for a single model that works well across a whole set of possibly diverse problems, our personalized regression approach acknowledges that different models may suite different types of problems. Going one step further, we also investigate the impact of selecting not a single regression model per problem, but personalized ensembles. We test our approach on predicting the performance of numerical optimization heuristics on the BBOB benchmark collection.