Data-Driven Framework for Uncovering Hidden Control Strategies in Evolutionary Analysis

Data-Driven Framework for Uncovering Hidden Control Strategies in Evolutionary Analysis

We devised a data-driven framework for uncovering hidden control strategies used by an evolutionary system described by an evolutionary probability distribution. This innovative framework enables deciphering of the concealed mechanisms that contribute to the progression or mitigation of such situati...

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Bibliographic Details
Main Authors: Nourddine Azzaoui, Tomoko Matsui, Daisuke Murakami
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Mathematical and Computational Applications
Subjects:
data-driven optimization algorithm
model predictive control
evolutionary probability distribution
generalized additive model
classification
COVID-19 evolution
Online Access:https://www.mdpi.com/2297-8747/28/5/103

Internet

https://www.mdpi.com/2297-8747/28/5/103

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