Evolving neural networks to follow trajectories of arbitrary complexity

21 May 2019Benjamin IndenJürgen Jost

Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest... (read more)

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