Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
| العنوان: | Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants |
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| المؤلفون: | Carola Doerr, Anja Jankovic |
| 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) |
| المصدر: | GECCO'20 Proceedings of the ACM Genetic and Evolutionary Computation Conference ACM Genetic and Evolutionary Computation Conference (GECCO'20) ACM Genetic and Evolutionary Computation Conference (GECCO'20), Jul 2020, Cancun, Mexico. ⟨10.1145/3377930.3390183⟩ GECCO |
| بيانات النشر: | arXiv, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Context (language use), 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, Machine Learning (cs.LG), Component (UML), 0202 electrical engineering, electronic engineering, information engineering, Neural and Evolutionary Computing (cs.NE), CMA-ES, Selection (genetic algorithm), business.industry, Supervised learning, Computer Science - Neural and Evolutionary Computing, Regression analysis, Modular design, Regression, 010201 computation theory & mathematics, Portfolio, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer |
| الوصف: | Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trained techniques are regression models which predict the performance of a given algorithm on a previously unseen problem instance. In the context of numerical black-box optimization, such regression models typically build on exploratory landscape analysis (ELA), which quantifies several characteristics of the problem. These measures can be used to train a supervised performance regression model. First steps towards ELA-based performance regression have been made in the context of a fixed-target setting. In many applications, however, the user needs to select an algorithm that performs best within a given budget of function evaluations. Adopting this fixed-budget setting, we demonstrate that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches, by suitably combining two differently trained regression models. We test this approach on a very challenging problem: algorithm selection on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms. Comment: To appear in Proc. of Genetic and Evolutionary Computation Conference (GECCO'20) |
| DOI: | 10.48550/arxiv.2006.09855 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83f11248e0b63625f1f0fda61fa705c1 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....83f11248e0b63625f1f0fda61fa705c1 |
| قاعدة البيانات: | OpenAIRE |
| DOI: | 10.48550/arxiv.2006.09855 |
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