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Per-instance automated algorithm configuration and selection are gainingsignificant moments in evolutionary computation in recent years. Two crucial,sometimes implicit, ingredients for these automated machine learning (AutoML)methods are 1) feature-based representations of the problem instances and 2)performance prediction methods that take the features as input to estimate howwell a specific algorithm instance will perform on a given problem instance.Non-surprisingly, common machine learning models fail to make predictions forinstances whose feature-based representation is underrepresented or not coveredin the training data, resulting in poor generalization ability of the modelsfor problems not seen during training.In this work, we studyleave-one-problem-out (LOPO) performance prediction. We analyze whetherstandard random forest (RF) model predictions can be improved by calibratingthem with a weighted average of performance values obtained by the algorithm onproblem instances that are sufficiently close to the problem for which aperformance prediction is sought, measured by cosine similarity in featurespace. While our RF+clust approach obtains more accurate performance predictionfor several problems, its predictive power crucially depends on the chosensimilarity threshold as well as on the feature portfolio for which the cosinesimilarity is measured, thereby opening a new angle for feature selection in azero-shot learning setting, as LOPO is termed in machine learning.

RF+clust for Leave-One-Problem-Out Performance Prediction