Memetic Algorithm

Memetic algorithms (MAs) are evolutionary computation techniques that combine the global exploration of genetic algorithms with the local refinement of other optimization methods, aiming to efficiently solve complex optimization problems. Current research focuses on enhancing MA performance through adaptive weighting of objective functions, incorporating gradient-based searches or reinforcement learning, and developing problem-specific operators for improved exploration and exploitation of the solution space. This approach has demonstrated success across diverse applications, including antenna design, quantum circuit optimization, and scheduling problems, showcasing MAs' potential for tackling computationally challenging tasks in various scientific and engineering domains.