International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 538KB)
A Comparative Analysis of Evolutionary Metaheuristics: Genetic Algorithm vs. Particle Swarm Optimization for Feature Selection in High-Accuracy Fake News Detection
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Author(s)
Nikita Garg
1,*
Pritam Singh Negi
1
Index Terms
Fake News Detection, Feature Selection, Genetic Algorithm, Particle Swarm Optimization, Evolutionary Computation, Wrapper Method, High Dimensionality, TF-IDF
Abstract
The proliferation of online misinformation demands the development of highly accurate and computationally efficient automated systems for Fake News Detection. A primary impediment to system performance is the high dimensionality of textual features derived from techniques like TF-IDF, making optimal Feature Selection a critical step. This paper presents a detailed comparative experimental study of two prominent bio-inspired evolutionary metaheuristics, the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) used as wrapper-based FS techniques for FND. The methodologies were rigorously tested across two distinct textual datasets: the complex, large-scale FakeNewsNet corpus and a moderate-scale general news dataset. The feature sets, once optimised, were evaluated using six standard Machine Learning (ML) classifiers. The GA-based FS approach, emphasising global exploration, achieved state-of-the-art accuracy of 99.91% with the Random Forest classifier on the FakeNewsNet dataset. In contrast, the PSO-based FS approach, valued for its rapid convergence, yielded a maximum accuracy of 93.29% with the Support Vector Machine (SVM) on the general news dataset. This analysis provides empirical evidence of the intrinsic trade-off between the algorithms: GA is superior for maximising accuracy in high-dimensional, complex textual spaces, while PSO offers a more efficient and practical solution for resource-constrained or moderate-scale FND tasks. The study confirms that evolutionary computation provides a robust, effective pathway for significantly enhancing ML classifier performance in this critical domain.
Cite This Paper
Nikita Garg, Pritam Singh Negi, “A Comparative Analysis of Evolutionary Metaheuristics: Genetic Algorithm vs. Particle Swarm Optimization for Feature Selection in High-Accuracy Fake News Detection”, International Journal of Education and Management Engineering (IJEME), Vol.16, No.4, pp. 16-28, 2026. DOI:10.5815/ijeme.2026.04.02
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