Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration - Sorbonne Université
Conference Papers Year : 2022

Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration

Ana Nikolikj
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JSI
Gjorgjina Cenikj
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  • PersonId : 1148403
JSI
Fabien Teytaud
Olivier Teytaud
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  • PersonId : 1089196
Tome Eftimov
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  • PersonId : 1099541
JSI
Carola Doerr

Abstract

Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc. State-of-the-art algorithm selection wizards are complex and difficult to improve. We propose in this work the use of automated configuration methods for improving their performance by finding better configurations of the algorithms that compose them. In particular, we use elitist iterated racing (irace) to find CMA configurations for specific artificial benchmarks that replace the hand-crafted CMA configurations currently used in the NGOpt wizard provided by the Nevergrad platform. We discuss in detail the setup of irace for the purpose of generating configurations that work well over the diverse set of problem instances within each benchmark. Our approach improves the performance of the NGOpt wizard, even on benchmark suites that were not part of the tuning by irace.
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Dates and versions

hal-03740762 , version 1 (29-07-2022)

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Risto Trajanov, Ana Nikolikj, Gjorgjina Cenikj, Fabien Teytaud, Mathurin Videau, et al.. Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration. 17th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Sep 2022, Dortmund, Germany. pp.18-31, ⟨10.1007/978-3-031-14714-2_2⟩. ⟨hal-03740762⟩
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