Cooperative Coevolution

Cooperative coevolution is a computational approach that tackles complex problems by dividing them into smaller, interacting subproblems, each solved by a separate evolutionary algorithm. Current research focuses on applying this framework to diverse areas, including adversarial optimization, neural network pruning (especially for spiking neural networks), and reinforcement learning, often employing modified differential evolution or hybrid algorithms incorporating techniques like NSGA-II. This strategy shows promise for efficiently optimizing high-dimensional spaces and handling large-scale problems, offering significant advantages in areas like model compression and the development of robust, efficient AI systems.