NEUROEVOLUTION OF AUTO-TEACHING ARCHITECTURES
Edward Robinson, John A. Bullinaria · 2009
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. 1.