Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization

Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization

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...

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Bibliographic Details
Main Authors: Mario Andrés Muñoz, Michael Kirley
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Algorithms
Subjects:
algorithm selection
black-box optimization
single-objective continuous optimization
exploratory landscape analysis
performance prediction
randomized heuristics
Online Access:https://www.mdpi.com/1999-4893/14/1/19

Internet

https://www.mdpi.com/1999-4893/14/1/19

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