Selector: Ensemble-Based Automated Algorithm Configuration
提出一种名为Selector的集成方法，结合多种算法配置模型，通过超可配置选择算法分配计算资源，实验表明其性能优于现有方法PyDGGA和SMAC。
Abstract Solvers contain parameters that influence their performance and these must be set by the user to ensure that high-quality solutions are generated, or optimal solutions are found quickly. Manually setting these parameters is tedious and error-prone, since search spaces may be large or even infinite. Existing approaches to automate the task of algorithm configuration (AC) make use of a single machine learning model that is trained on previous runtime data and used to create or evaluate promising new configurations. We combine a variety of successful models from different AC approaches into an ensemble that proposes new configurations. To this end, each model in the ensemble suggests configurations and a hyper-configurable selection algorithm chooses a subset of configurations to match the amount of computational resources available. We call this approach Selector , and we examine its performance against the state-of-the-art AC methods PyDGGA and SMAC, respectively. The new configurator will be made available as an open source software package.