Global Simple Evolutionary Multi Objective

Global Simple Evolutionary Multi-Objective (GSEMO) algorithms are a class of evolutionary algorithms designed to efficiently find multiple optimal solutions in complex problems with conflicting objectives. Current research focuses on improving GSEMO's performance through techniques like adaptive sliding windows to manage population size, sampling-based methods for chance-constrained problems, and self-adaptive mutation strategies. These advancements aim to enhance the algorithm's speed and solution quality, particularly for challenging problems involving stochasticity, dynamism, and high dimensionality, with applications in areas like resource allocation and scheduling.