Information Sciences: an International Journal
Evolving neural networks through augmenting topologies
Evolutionary Computation
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Dynamics model abstraction scheme using radial basis functions
Journal of Control Science and Engineering - Special issue on Dynamic Neural Networks for Model-Free Control and Identification
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Neural networks can be evolved to control robot manipulators in tasks like target tracking and obstacle avoidance in complex environments. Neurocontrollers are robust to noise and can be adapted to different environments and robot configurations. In this paper, neurocontrollers were evolved to position the end effector of a robot arm close to a target in three different environments: environments without obstacles, environments with stationary obstacles, and environments with moving obstacles. The evolved neurocontrollers perform qualitatively like inverse kinematic controllers in environments with no obstacles and like path-planning controllers based on Rapidly-exploring random trees in environments with obstacles. Unlike inverse kinematic controllers and path planners, the approach reliably generalizes to environments with moving obstacles, making it possible to use it in natural environments.