Benchmarking an algorithm for expensive high-dimensional objectives on the bbob and bbob-largescale testbeds

Abstract

We report benchmarks for a recently developed algorithm on the bbob and bbob-largescale benchmarking testbeds in COCO. This algorithm is designed for expensive high-dimensional multimodal objectives (such as arise in hyperparameter optimization or via simulations), and this regime introduces challenges for benchmarking. In particular, while the COCO experimental procedure yields evidence that this algorithm improves on the state of the art, the COCO framework also exhibits shortcomings for the very low evaluation budgets involved here. Consequently, we also report the results of ad hoc experiments that demonstrate a more obvious if less rigorous advantage for the algorithm over its nearest competition. CCS CONCEPTS• Computing methodologies → Continuous space search.