NeuroEvolution

Random Neural Networks

Pedersen&Risi, 2023, GECCO

Evolution of Spiking Neural Networks (SNN)

Wang, G., Sun, Y., Cheng, S. and Song, S., 2024. Evolving connectivity for recurrent spiking neural networks. Advances in Neural Information Processing Systems, 36.

Reference

Whitelam, S. and Tamblyn, I., 2021. Neuroevolutionary learning of particles and protocols for self-assembly. Physical Review Letters, 127(1), p.018003.
Whitelam, S., Selin, V., Park, S.W. and Tamblyn, I., 2021. Correspondence between neuroevolution and gradient descent. Nature Communications, 12(1), p.6317.
https://towardsdatascience.com/paper-repro-deep-neuroevolution-756871e00a66

https://cacm.acm.org/news/225641-can-neuroevolution-change-machine-learning/fulltext

https://direct.mit.edu/neco/article-abstract/22/7/1860/7554/Autonomous-Evolution-of-Topographic-Regularities

Evolving neural networks for strategic decision-making problems

https://ieeexplore.ieee.org/document/942536

https://www.jair.org/index.php/jair/article/view/10367

https://proceedings.neurips.cc/paper/2020/hash/d1dc3a8270a6f9394f88847d7f0050cf-Abstract.html

https://proceedings.neurips.cc/paper/1989/hash/8c19f571e251e61cb8dd3612f26d5ecf-Abstract.html

https://direct.mit.edu/evco/article-abstract/10/2/99/1123/Evolving-Neural-Networks-through-Augmenting

https://dl.acm.org/doi/10.5555/1642293.1642430

https://direct.mit.edu/neco/article-abstract/19/3/757/7156/Training-Recurrent-Networks-by-Evolino?redirectedFrom=fulltext

[2017] Neuroevolution in Games - State of the Art and Open Challenges [NYU+Togelius+Risi]

[2019] Neural Architecture Search - A Survey [JMLR+Hutter]