A Review of Genetic Algorithms and Algorithm Fusion: Is Result Ranking More Effective.

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 [2][8]. 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 [1][3][4][7]. This review presents a comprehensive overview of GAs, their core principles, applications, and limitations [11]. Furthermore, it highlights recent trends in algorithm fusion approaches [5][6][9] and critically examines whether the resulting improvements in ranking and decision-making are significant compared to standalone GAs [2][8].