Evolutionary Algorithm

Evolutionary algorithms (EAs) are computational optimization methods inspired by natural selection, aiming to find optimal or near-optimal solutions to complex problems by iteratively improving a population of candidate solutions. Current research emphasizes hybrid approaches, integrating EAs with other techniques like large language models (LLMs) for automated hyperparameter tuning and prompt engineering, reinforcement learning for robot design, and even quantum computing for enhanced search capabilities. These advancements are improving the efficiency and applicability of EAs across diverse fields, from logistics and manufacturing to drug discovery and materials science, by tackling previously intractable optimization challenges.