Personalizing Performance Regression Models to Black-Box Optimization Problems

by   Tome Eftimov, et al.

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.


page 1

page 2

page 3

page 4


Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants

Automated algorithm selection promises to support the user in the decisi...

Black Box Algorithm Selection by Convolutional Neural Network

Although a large number of optimization algorithms have been proposed fo...

SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison

Fair algorithm evaluation is conditioned on the existence of high-qualit...

Explainable Landscape Analysis in Automated Algorithm Performance Prediction

Predicting the performance of an optimization algorithm on a new problem...

The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants

Selecting the most suitable algorithm and determining its hyperparameter...

Machine Learning Optimization Algorithms & Portfolio Allocation

Portfolio optimization emerged with the seminal paper of Markowitz (1952...

Towards Feature-Based Performance Regression Using Trajectory Data

Black-box optimization is a very active area of research, with many new ...