Experiments in Evolutionary Synthesis of Robotic Neurocontrollers

Karthik Balakrishnan, Vasant Honavar

Artificial neural networks offer an attractive paradigm for the design of behavior and control systems in robots and autonomous agents for a variety of reasons, including: ability to adapt and learn, potential for resistance to noise, faults and component failures, potential for real-time performance in dynamic environments (through massive parallelism and suitable hardware realization) etc.


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