Coevolutionary Algorithm

Coevolutionary algorithms (CEAs) are computational methods that simultaneously evolve multiple populations, whose fitnesses are interdependent, to solve complex problems. Current research focuses on improving CEA efficiency and applicability, particularly through cooperative coevolutionary approaches that decompose problems into subproblems and utilize techniques like surrogate models and reinforcement learning to enhance optimization. These advancements are impacting diverse fields, including reinforcement learning, hyperparameter optimization for deep learning, and the design of robust machine learning models, by enabling the efficient exploration of high-dimensional search spaces and the discovery of novel solutions.