Rohit Kumar

A taxonomy-driven review of surrogate-assisted evolutionary algorithms: architectural paradigms, infill strategies, optimizers, and emerging trends

This analysis uncovers a major operational shift where advanced architectures actively serve as catalysts to break the historical dependency on the traditional EL pathway, successfully introducing novel, non-traditional active-learning mechanics such as autonomous LLM-guided strategy selectors, discrete classifier-based boundary tracks, and generative exploratory loops.