# Personalizing Performance Regression Models to Black-Box Optimization Problems

arXiv: https://arxiv.org/abs/2104.10999 · HTML: https://mlsanity.com/paper/2104.10999/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [Explainable Landscape-Aware Optimization Performance Prediction](https://mlsanity.com/paper/2110.11633/) (2021)
- [Explainable Landscape Analysis in Automated Algorithm Performance Prediction](https://mlsanity.com/paper/2203.11828/) (2022)
- [RF+clust for Leave-One-Problem-Out Performance Prediction](https://mlsanity.com/paper/2301.09524/) (2023)
- [OPTION: OPTImization Algorithm Benchmarking ONtology](https://mlsanity.com/paper/2211.11332/) (2022)

## Abstract (arXiv)

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.