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Automated algorithm selection promises to support the user in the decisivetask of selecting a most suitable algorithm for a given problem. A commoncomponent of these machine-trained techniques are regression models whichpredict the performance of a given algorithm on a previously unseen probleminstance. In the context of numerical black-box optimization, such regressionmodels typically build on exploratory landscape analysis (ELA), whichquantifies several characteristics of the problem. These measures can be usedto train a supervised performance regression model.  First steps towards ELA-based performance regression have been made in thecontext of a fixed-target setting. In many applications, however, the userneeds to select an algorithm that performs best within a given budget offunction evaluations. Adopting this fixed-budget setting, we demonstrate thatit is possible to achieve high-quality performance predictions withoff-the-shelf supervised learning approaches, by suitably combining twodifferently trained regression models. We test this approach on a verychallenging problem: algorithm selection on a portfolio of very similaralgorithms, which we choose from the family of modular CMA-ES algorithms.

Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants