International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 16, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 489KB)
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 necessitates 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 demonstrates that evolutionary computation effectively enhances ML classifier performance by optimizing feature subsets.
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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