Neuroevolutionary Algorithms for Robot Control: Studying neuroevolutionary algorithms for optimizing robot controllers and behaviors through evolutionary processes (Journal of Computational Intelligence and Robotics, 2024)
Neuroevolutionary algorithms (NEAs) offer a promising approach for optimizing robot controllers and behaviors through evolutionary processes. By combining principles from neural networks and evolutionary computation, NEAs can efficiently search for optimal solutions in complex, high-dimensional spaces. This paper provides a comprehensive review of NEAs in the context of robot control, highlighting key algorithms, applications, and challenges. We discuss various NEA techniques, including genetic algorithms, genetic programming, and neuroevolution, and their application to robot control tasks. Additionally, we examine the integration of NEAs with simulation environments and hardware platforms for real-world robotic applications. Finally, we discuss current trends and future directions in the field of NEAs for robot control.