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NEUROEVOLUTION OF AUTO-TEACHING ARCHITECTURES

This paper explores the idea that auto-teaching neural networks with evolved selfsupervision signals can lead to improved performance in dynamic environments where there is insufficient training data available within an individual’s lifetime. Results are presented from a series of artificial life experiments which investigate whether, when, and how this approach can lead to performance enhancements, in a simple problem domain that captures season dependent foraging behaviour.