通过自动算法配置改进Nevergrad算法选择向导NGOpt
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
摘要：
算法选择向导是有效的和通用的工具，自动选择一个优化算法给出高层次的信息的问题和可用的计算资源，如决策变量的数量和类型，最大数量的评估，并行化评估的可能性，等国家的最先进的算法选择向导是复杂的，难以改善。在这项工作中，我们建议使用自动化配置方法，通过找到更好的配置组成它们的算法，以提高其性能。特别是，我们使用精英迭代竞赛（irace）来为特定的人工基准找到CMA配置，以取代Nevergrad平台提供的NGOpt向导中目前使用的手工CMA配置。我们将详细讨论irace的设置，以生成在每个基准测试中的不同问题实例集上工作良好的配置。我们的方法提高了NGOpt向导的性能，甚至在基准测试套件上也是如此，这些基准测试套件并不是irace调优的一部分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
