The advent of the deep learning evolutionary algorithm EvoDN2 and its recent applications
Nirupam Chakraborti
Faculty of Mechanical Engineering, Czech Technical University in Prague, Czech Republic.
DOI:
https://doi.org/10.7494/cmms.2025.2.1021
Abstract:
The evolutionary deep learning algorithm EvoDN2 is an emerging strategy for data-driven intelligent learning and many-objective optimisation capable of handling a large volume of noisy and non-linear data. This article provides the essential details of this algorithm and highlights a number of its recent applications.
Cite as:
Chakraborti, N. (2025). The advent of the deep learning evolutionary algorithm EvoDN2 and its recent applications. Computer Methods in Materials Science, 25(2), 41–55. https://doi.org/10.7494/cmms.2025.2.1021
Article (PDF):
Keywords:
Deep learning, Data-driven modelling, Many-objective optimization, Evolutionary algorithms, Genetic algorithm, Pareto optimality, Big data, Machine learning, EvoDN2
Publication dates:
Received: 19.05.2025, Accepted: 10.06.2025, Published: 02.07.2025
Publication type:
Review paper
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