Personalizing Performance Regression Models to Black-Box Optimization Problems - Sorbonne Université
Conference Papers Year : 2021

Personalizing Performance Regression Models to Black-Box Optimization Problems

Tome Eftimov
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  • PersonId : 1099541
Anja Jankovic
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  • PersonId : 1099542
Gorjan Popovski
  • Function : Author
  • PersonId : 1099543
Peter Korošec
  • Function : Author
  • PersonId : 1099544

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 naïve, 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. CCS CONCEPTS • Computing methodologies → Continuous space search; Randomized search; • Theory of computation → Random search heuristics.
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Dates and versions

hal-03233825 , version 1 (25-05-2021)

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Tome Eftimov, Anja Jankovic, Gorjan Popovski, Carola Doerr, Peter Korošec. Personalizing Performance Regression Models to Black-Box Optimization Problems. Genetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. ⟨10.1145/3449639.3459407⟩. ⟨hal-03233825⟩
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