Abstract - Genetic Algorithms (GAs) are widely recognized as robust optimization techniques inspired by natural evolution 10. Over the past decades, they have been applied to diverse domains such as scheduling, machine learning, image processing, and data mining 28. With the rapid growth of data-driven applications, researchers have explored the integration of GAs with other algorithms known as algorithm fusion to enhance accuracy, convergence speed, and result ranking performance 1347. This review presents a comprehensive overview of GAs, their core principles, applications, and limitations 11. Furthermore, it highlights recent trends in algorithm fusion approaches 569 and critically examines whether the resulting improvements in ranking and decision-making are significant compared to standalone GAs 28. Key Words: Data Mining, Fusion, image processing, Genetic Algorithm, Ranking
Haneesh et al. (Fri,) studied this question.