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

作     者：Trajanov, Risto Nikolikj, Ana Cenikj, Gjorgjina Teytaud, Fabien Videau, Mathurin Teytaud, Olivier Eftimov, Tome López-Ibáñez, Manuel Doerr, Carola 

作者机构：Faculty of Computer Science and Engineering Ss. Cyril and Methodius Skopje Macedonia Jožef Stefan Institute Ljubljana Slovenia Jozef Stefan International Postgraduate School Ljubljana Slovenia Universitś Littoral Côte d'Opale France Facebook AI Research ITIS Software Universidad de Málaga Spain Sorbonne Universitś CNRS LIP6 Paris France 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Benchmarking 

摘      要：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. Copyright © 2022, The Authors. All rights reserved.