Advanced and Classical Selection Methods in Genetic Algorithms: A Comprehensive Comparative Analysis

Evolutionary polar fox optimization algorithm: an enhanced metaheuristic for feature selection

Exploring Elitism Strategies in Nested Tournament Selection for Multi-Objective Genetic Programming

This study systematically investigates multiple elitism mechanisms for NT within tree-based MO Genetic Programming (MOGP), including NSGA-II population replacement, crowding distance, first-objective, and a novel ideal-point strategy, comparing them against non-elitist NT and standard NSGA-II.

A Comparative Study of Metaheuristics on Raspberry Pi 5: Results to Guide Optimization on Constrained Edge Devices

 This study investigates the efficiency of five metaheuristic algorithms, namely Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Harmony Search (HS), and Particle Swarm Optimization (PSO), when deployed on a Raspberry Pi 5 edge device. The evaluation focuses on both optimization quality and computational cost, using four standard benchmark functions that represent a range of landscape characteristics: Sphere, Rosenbrock, Rastrigin, and Ackley. Each function is tested at dimensions 10, 30, and 50 to probe scalability. In addition to objective values, the experiments collect per-iteration processor usage and memory (RAM) to provide a practical view of runtime overhead under constrained resources. Among the five candidates, GWO consistently delivers the fastest or near-fastest convergence while keeping variability tight. Its trajectories show smooth descent across functions and dimensions, paired with comparatively modest CPU and RAM footprints. PSO typically ranks second in speed with stable dynamics, though brief CPU spikes often appear at early iterations as swarms synchronize. DE demonstrates resilience on rugged functions but generally requires more iterations to close the final gap. GA and HS can reach competitive objective values on some settings, yet they display wider dispersion and higher overhead at larger dimensions, which reduces their suitability for small devices. Overall, the evidence indicates that GWO is the most efficient choice for edge deployment on Raspberry Pi 5, striking a favorable balance between convergence speed, stability, and resource usage. PSO is a strong alternative when slightly higher processor activity is acceptable. These findings support the adoption of lightweight, variance-stable metaheuristics for edge optimization workloads where CPU and memory budgets are tight.

A Review of The Artificial Bee Colony (ABC) Algorithm: Variants, Applications, And Performance

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence technique inspired by the foraging behavior of honey bees. While widely studied, existing reviews often lack a comprehensive synthesis of algorithmic variants, comparative performance, and recent applications. This paper provides an integrated review of ABC, covering its standard formulation, major variants, domain applications, benchmarking results, and emerging research directions. A systematic survey of peer‑reviewed articles published between 2010 and 2025 was conducted using Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Results show that hybrid ABC approaches (e.g., ABC combined with Differential Evolution) achieve up to 30% faster convergence and improved robustness under noisy conditions compared to the standard algorithm. Applications span engineering design, machine learning, telecommunications, cloud computing, and image processing, with reported gains in accuracy, efficiency, and scalability. The review identifies key limitations, including sensitivity to parameter settings and slower early convergence, and highlights future opportunities in automated parameter tuning, multi‑objective optimization, and integration with deep learning frameworks. This work contributes a holistic perspective by combining performance comparisons with application insights, offering a valuable resource for both researchers and practitioners.

Adaptive Ensemble Multi-Objective Differential Evolution for High-Dimensional Gene Selection

This paper proposes Adaptive Ensemble Multi-Objective Differential Evolution for Feature Selection (AEMO-DEFS), a wrapper-based framework that simultaneously minimizes feature count and maximizes classification accuracy for high-dimensional gene expression data. AEMO-DEFS introduces two key adaptive mechanisms: (1) an ensemble of DE mutation strategies (rand/1/bin, best/1/bin, current-to-pbest/1/bin) with dynamic selection based on historical success, and (2) memory-based self-adaptation of the scaling factor F (using Cauchy distribution) and crossover rate CR (using Normal distribution). Non-dominated sorting combined with crowding distance maintains diverse Pareto-optimal solutions. Comprehensive experiments on five cancer microarray datasets demonstrate that AEMO-DEFS outperforms MOPSO, NSGA-III, and MOEA/D across all evaluation metrics, achieving superior Inverted Generational Distance (IGD), ϵ-indicator, classification accuracy, and significant feature reduction.

A Comparative Analysis of Evolutionary Metaheuristics: Genetic Algorithm vs. Particle Swarm Optimization for Feature Selection in High-Accuracy Fake News Detection

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.