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Optimizing Reservoir Computing with Genetic Algorithm for High-Dimensional SARS-CoV-2 Hospitalization Forecasting: Impacts of Genetic Algorithm Hyperparameters on Feature Selection and Reservoir Computing Hyperparameter Tuning

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Thomas Ferté
Dan Dutartre
Boris Hejblum
Romain Griffier
Vianney Jouhet
Rodolphe Thiébaut
Xavier Hinaut
Pierrick Legrand
Published in
Artificial Evolution
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the optimization of reservoir computing using genetic algorithms for forecasting SARS-CoV-2 hospitalizations. The study focuses on the impact of genetic algorithm hyperparameters on feature selection and reservoir computing performance in a high-dimensional setting. Key topics include the influence of genetic algorithm hyperparameters on model performance, the behavior of reservoir computing with different hyperparameter settings, and the robustness of the model over time. The results show that higher mutation rates in the genetic algorithm can lead to better generalization and reduced overfitting, while lower mutation rates may hinder performance. The study also explores strategies for achieving sparser models and their impact on performance. The findings highlight the potential of combining reservoir computing with genetic algorithms for accurate and robust forecasting in healthcare.
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previous chapter Tackling Long-Range Dependencies in Dynamic Range Compression Modeling via Deep Learning
next chapter Can Mutations Replace Local Search? Studying the Effect of Repeated Genetic Programming Operators in the Unrelated Machines Environment
Title
Optimizing Reservoir Computing with Genetic Algorithm for High-Dimensional SARS-CoV-2 Hospitalization Forecasting: Impacts of Genetic Algorithm Hyperparameters on Feature Selection and Reservoir Computing Hyperparameter Tuning
Authors
Thomas Ferté
Dan Dutartre
Boris Hejblum
Romain Griffier
Vianney Jouhet
Rodolphe Thiébaut
Xavier Hinaut
Pierrick Legrand
Copyright Year
2026
Book
Artificial Evolution

Print ISBN: 978-3-032-07997-8

Electronic ISBN: 978-3-032-07998-5

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-07998-5_10
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