Version 1
: Received: 30 September 2024 / Approved: 2 October 2024 / Online: 3 October 2024 (08:24:54 CEST)
How to cite:
Cryzan, M. Single Solution Based Metaheuristics With Graph Network Based Machine Learning. Preprints2024, 2024100187. https://doi.org/10.20944/preprints202410.0187.v1
Cryzan, M. Single Solution Based Metaheuristics With Graph Network Based Machine Learning. Preprints 2024, 2024100187. https://doi.org/10.20944/preprints202410.0187.v1
Cryzan, M. Single Solution Based Metaheuristics With Graph Network Based Machine Learning. Preprints2024, 2024100187. https://doi.org/10.20944/preprints202410.0187.v1
APA Style
Cryzan, M. (2024). Single Solution Based Metaheuristics With Graph Network Based Machine Learning. Preprints. https://doi.org/10.20944/preprints202410.0187.v1
Chicago/Turabian Style
Cryzan, M. 2024 "Single Solution Based Metaheuristics With Graph Network Based Machine Learning" Preprints. https://doi.org/10.20944/preprints202410.0187.v1
Abstract
Simulated Annealing (SA), Tabu Search (TS), and Variable Neighborhood Search (VNS) are well-established single-solution-based metaheuristics known for their efficiency. In this paper, we explore the application of these techniques to enhance the performance of graph network models, specifically Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs). By leveraging these metaheuristics, we aim to optimize key aspects of graph-based learning, such as hyperparameter tuning, architecture design, feature selection, and graph structure learning. Our approach demonstrates how these metaheuristics can improve the generalization, efficiency, and robustness of GNNs/GCNs, facilitating their use in large-scale graph-based tasks. The proposed framework illustrates the potential of combining advanced optimization techniques with state-of-the-art graph machine learning models.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
