Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization
Mario Andres Munoz, Michael Kirley
Algorithms | MDPI | Published : 2021
In this paper, we investigate how systemic errors due to random sampling impact on automated algorithm selection for bound-constrained, single-objective, continuous black-box optimization. We construct a machine learning-based algorithm selector, which uses exploratory landscape analysis features as inputs. We test the accuracy of the recommendations experimentally using resampling techniques and the hold-one-instance-out and hold-one-problem-out validation methods. The results demonstrate that the selector remains accurate even with sampling noise, although not without trade-offs.
This work was funded by The University of Melbourne through MIRS/MIFRS scholarships awarded to M.A. Munoz.