Personalizing Performance Regression Models to Black-Box Optimization Problems
This work addresses the need for more accurate algorithm selection in black-box optimization, though it is incremental as it builds on existing landscape analysis methods.
The paper tackles the problem of predicting optimization algorithm performance for unseen problem instances by proposing personalized regression models tailored to specific problem types, achieving improved accuracy over a single model across diverse problems.
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.