Personalizing Performance Regression Models to Black-Box Optimization Problems
| العنوان: | Personalizing Performance Regression Models to Black-Box Optimization Problems |
|---|---|
| المؤلفون: | Carola Doerr, Tome Eftimov, Anja Jankovic, Gorjan Popovski, Peter Korošec |
| المساهمون: | Recherche Opérationnelle (RO), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU) |
| المصدر: | Genetic and Evolutionary Computation Conference (GECCO 2021) Genetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. ⟨10.1145/3449639.3459407⟩ GECCO |
| بيانات النشر: | HAL CCSD, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Optimization problem, business.industry, Computer science, Computer Science - Neural and Evolutionary Computing, Context (language use), Regression analysis, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Machine learning, computer.software_genre, 01 natural sciences, Regression, Set (abstract data type), 010201 computation theory & mathematics, Black box, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Neural and Evolutionary Computing (cs.NE), Artificial intelligence, business, Heuristics, computer |
| الوصف: | 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. To appear in the Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021), ACM |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a68a6ef71f4984ac5507887b824b93df https://hal.sorbonne-universite.fr/hal-03233825/document |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....a68a6ef71f4984ac5507887b824b93df |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |