Model Based Evolutionary

Model-based evolutionary algorithms (MBEAs) enhance traditional evolutionary algorithms by incorporating learned models of the problem landscape to guide the search for optimal solutions, particularly in complex, high-dimensional, or multi-objective scenarios. Current research focuses on improving MBEA performance through techniques like large language model integration for faster convergence and more effective search operators, and the development of sophisticated surrogate models (e.g., neural networks) to handle expensive function evaluations in high-dimensional spaces. These advancements are significant because they enable efficient optimization in challenging domains, with applications ranging from engineering design to human-robot interaction, where traditional methods struggle.