A Comparative Study of Large-scale Variants of CMA-ES
| Autor: | Yann Semet, Rami Kassab, Frédéric Barbaresco, Dimo Brockhoff, Konstantinos Varelas, Nikolaus Hansen, Anne Auger, Ouassim Ait ElHara |
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| Přispěvatelé: | Randomized Optimisation (RANDOPT ), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Technologie de Compiègne (UTC), Sondra, CentraleSupélec, Université Paris-Saclay (COmUE) (SONDRA), ONERA-CentraleSupélec-Université Paris Saclay (COmUE), Thales Air Systems, Thales Group [France], THALES [France] |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Testbed MathematicsofComputing_NUMERICALANALYSIS 020206 networking & telecommunications Scale (descriptive set theory) Context (language use) 02 engineering and technology Benchmarking [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Machine learning computer.software_genre Dimension (vector space) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] CMA-ES business computer Curse of dimensionality |
| Zdroj: | PPSN XV 2018-15th International Conference on Parallel Problem Solving from Nature PPSN XV 2018-15th International Conference on Parallel Problem Solving from Nature, Sep 2018, Coimbra, Portugal. pp.3-15, ⟨10.1007/978-3-319-99253-2_1⟩ Parallel Problem Solving from Nature – PPSN XV ISBN: 9783319992525 PPSN (1) |
| DOI: | 10.1007/978-3-319-99253-2_1⟩ |
| Popis: | International audience; The CMA-ES is one of the most powerful stochastic numerical optimizers to address difficult black-box problems. Its intrinsic time and space complexity is quadratic-limiting its applicability with increasing problem dimensionality. To circumvent this limitation, different large-scale variants of CMA-ES with subquadratic complexity have been proposed over the past ten years. To-date however, these variants have been tested and compared only in rather restrictive settings, due to the lack of a comprehensive large-scale testbed to assess their performance. In this context, we introduce a new large-scale testbed with dimension up to 640, implemented within the COCO benchmarking platform. We use this testbed to assess the performance of several promising variants of CMA-ES and the standard limited-memory L-BFGS. In all tested dimensions, the best CMA-ES variant solves more problems than L-BFGS for larger budgets while L-BFGS outperforms the best CMA-ES variant for smaller budgets. However, over all functions, the cumulative runtime distributions between L-BFGS and the best CMA-ES variants are close (less than a factor of 4 in high dimension). Our results illustrate different scaling behaviors of the methods, expose a few defects of the algorithms and reveal that for dimension larger than 80, LM-CMA solves more problems than VkD-CMA while in the cumulative runtime distribution over all functions the VkD-CMA dominates or shows almost equal success rate with LM-CMA for budgets up to 10 4 times dimension and for all budgets up to dimension 80. |
| Databáze: | OpenAIRE |
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