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The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants. (arXiv:2204.07431v1 [cs.NE])
cs.LG updates on arXiv.org arxiv.org
Selecting the most suitable algorithm and determining its hyperparameters for
a given optimization problem is a challenging task. Accurately predicting how
well a certain algorithm could solve the problem is hence desirable. Recent
studies in single-objective numerical optimization show that supervised machine
learning methods can predict algorithm performance using landscape features
extracted from the problem instances.
Existing approaches typically treat the algorithms as black-boxes, without
consideration of their characteristics. To investigate in this work if a
selection of landscape features …
arxiv cma features landscape performance prediction variants