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Personalizing Performance Regression Models to Black-Box Optimization Problems
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques.
The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection.
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP.
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