I'm by no means an expert in the field but I do find it exceptionally interesting so i try and keep tabs on some of the research done by people who originated from the same group as Kenneth Stanley.

Seems like many from this group now pursue open-endedness in AI and view evolution as a way towards this goal (or lack thereof).

A very interesting evolution (ha!) of these ideas was presented in POET[0] towards evolution of agents in evolving environments.

There is also an interesting paper about accelerating neural architecture search when generating fake training data in generative teacher networks[1].

Lastly, a paper that i find very very interesting but might not be as relevant but still is 'First return, then explore'[2]

[0] : https://eng.uber.com/poet-open-ended-deep-learning/

[1] : http://proceedings.mlr.press/v119/such20a.html

[2] : https://arxiv.org/pdf/2004.12919.pdf