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Lookup NU author(s): Dr Varun OjhaORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 The Author(s). We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tunable hyperparameters of covariance matrix adaptation evolutionary strategy, differential evolution, non-dominated sorting genetic algorithm III, and multi-objective evolutionary algorithm based on decomposition, the framework reveals the behaviors of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what are hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.
Author(s): Ojha V, Timmis J, Nicosia G
Publication type: Article
Publication status: Published
Journal: Swarm and Evolutionary Computation
Year: 2022
Volume: 74
Print publication date: 01/10/2022
Online publication date: 23/07/2022
Acceptance date: 11/07/2022
Date deposited: 09/03/2023
ISSN (print): 2210-6502
ISSN (electronic): 2210-6510
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.swevo.2022.101130
DOI: 10.1016/j.swevo.2022.101130
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