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It is not uncommon that meta-heuristic algorithms contain some intrinsicparameters, the optimal configuration of which is crucial for achieving theirpeak performance. However, evaluating the effectiveness of a configuration isexpensive, as it involves many costly runs of the target algorithm. Perhapssurprisingly, it is possible to build a cheap-to-evaluate surrogate that modelsthe algorithm's empirical performance as a function of its parameters. Suchsurrogates constitute an important building block for understanding algorithmperformance, algorithm portfolio/selection, and the automatic algorithmconfiguration. In principle, many off-the-shelf machine learning techniques canbe used to build surrogates. In this paper, we take the differential evolution(DE) as the baseline algorithm for proof-of-concept study. Regression modelsare trained to model the DE's empirical performance given a parameterconfiguration. In particular, we evaluate and compare four popular regressionalgorithms both in terms of how well they predict the empirical performancewith respect to a particular parameter configuration, and also how well theyapproximate the parameter versus the empirical performance landscapes.

Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution