Assessment of Surrogate Model Settings Using Landscape Analysis

Abstrakt

This work in progress concerns assessment of surrogate model settings for expensive black-box optimization. The assessment is performed in the context of Gaussian process models used in the Doubly Trained Surrogate (DTS) variant of the state-of-the-art black-box optimizer, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This work focuses on the connection between Gaussian process surrogate model predictive accuracy and an essential model hyper-parameter – the covariance function. The performance of DTS-CMA-ES is related to the results of landscape analysis of the objective function. To this end various classification and regression methods are used, proposed in the traditional framework for algorithm selection by Rice. Several single-label classification, multi-label classification, and regression methods are experimentally evaluated on data from DTS-CMAES runs on the noiseless benchmark functions from the COCO platform for comparing continuous optimizers in black-box settings.