The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
| العنوان: | The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection |
|---|---|
| المؤلفون: | Anja Jankovic, Tome Eftimov, Carola Doerr, Gorjan Popovski |
| 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. pp.687-696, ⟨10.1145/3449639.3459406⟩ GECCO |
| بيانات النشر: | HAL CCSD, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer science, media_common.quotation_subject, Decision tree, 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, Evolutionary computation, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), Neural and Evolutionary Computing (cs.NE), Selection (genetic algorithm), media_common, Hyperparameter, business.industry, Computer Science - Neural and Evolutionary Computing, Regression analysis, Regression, Random forest, 010201 computation theory & mathematics, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer |
| الوصف: | Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine learning models are often chosen in an ad-hoc manner. We show in this work that three classical regression methods are able to achieve meaningful results for ELA-based algorithm selection. For those three models -- random forests, decision trees, and bagging decision trees -- the quality of the regression models is highly impacted by the chosen hyper-parameters. This has significant effects also on the quality of the algorithm selectors that are built on top of these regressions. By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection. We point out that a choice of the machine learning model merits to be carefully undertaken and further investigated. 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___::4de24b35d308d67ac6cab08fa982a784 https://hal.sorbonne-universite.fr/hal-03233811 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....4de24b35d308d67ac6cab08fa982a784 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |