Non Dominated Sorting Genetic Algorithm

The Non-dominated Sorting Genetic Algorithm (NSGA-II and its variants like NSGA-III) is a widely used multi-objective evolutionary algorithm aiming to find optimal solutions across multiple, often conflicting, objectives. Current research focuses on improving its efficiency and robustness, particularly for problems with many objectives, through modifications to crowding distance calculations and hybrid approaches combining NSGA-II with other techniques such as deep reinforcement learning or support vector machines. These advancements are significant for various applications, including vehicle routing, scheduling, feature selection, and even training physics-informed neural networks, where finding Pareto optimal solutions across multiple performance metrics is crucial.