  We create an artificial system of agents (attention-based neural networks)
which selectively exchange messages with each-other in order to study the
emergence of memetic evolution and how memetic evolutionary pressures interact
with genetic evolution of the network weights. We observe that the ability of
agents to exert selection pressures on each-other is essential for memetic
evolution to bootstrap itself into a state which has both high-fidelity
replication of memes, as well as continuing production of new memes over time.
However, in this system there is very little interaction between this memetic
'ecology' and underlying tasks driving individual fitness - the emergent meme
layer appears to be neither helpful nor harmful to agents' ability to learn to
solve tasks. Sourcecode for these experiments is available at
https://github.com/GoodAI/memes
