Neuroevolutionary Algorithms for Robot Control: Studying neuroevolutionary algorithms for optimizing robot controllers and behaviors through evolutionary processes
Keywords:
Neuroevolutionary Algorithms, Robot Control, Evolutionary Computation, Neural Networks, Optimization, Robotics, Evolutionary Robotics, Genetic Algorithms, Genetic ProgrammingAbstract
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.
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