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Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization

Surrogate model selection is an active-learning approach to cost-aware continuous black-box optimization in domains where the evaluation of the black-box objective function is expensive, e. g., obtained experimentally or resulting from comprehensive simulations. Active reusing of knowledge represented by landscape properties of the objective function accross different tasks can provide additional information for more reliable decisions in terms of a suitable surrogate model and a suitable setting of its hyperparameters. However, research into using metalearing [13] and especially Exploratory Landscape Analysis (ELA) [14] in this context is only starting [20]. Our goal is to develop a learning system capable to recommend a surrogate model on the basis of the knowledge obtained in previous black-box optimization tasks. In this paper, we provide a first step necessary to construct a learning system applying knowledge from previous tasks to a new one: a study of the applicability of ELA to two important kinds of surrogate models – Gaussian processes (GP) [18] and ensembles of regression trees (random forests, RF) [3,4,6]. Results using the noiseless benchmarks of the Comparing-Continuous-Optimisers (COCO) platform [9] in the expensive scenario, where at most 50D evaluations are available, are analysed for statistical dependences between model performance and a broad variety of landscape features.