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Open AccessArticle

Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization

1
School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
2
School of Computer and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Received: 9 November 2020 / Revised: 24 December 2020 / Accepted: 6 January 2021 / Published: 11 January 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. View Full-Text
Keywords: algorithm selection; black-box optimization; single-objective continuous optimization; exploratory landscape analysis; performance prediction; randomized heuristics algorithm selection; black-box optimization; single-objective continuous optimization; exploratory landscape analysis; performance prediction; randomized heuristics
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MDPI and ACS Style

Muñoz, M.A.; Kirley, M. Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization. Algorithms 2021, 14, 19. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010019

AMA Style

Muñoz MA, Kirley M. Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization. Algorithms. 2021; 14(1):19. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010019

Chicago/Turabian Style

Muñoz, Mario A.; Kirley, Michael. 2021. "Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization" Algorithms 14, no. 1: 19. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010019

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