Evolution and design of distributed learning rules

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Neural Optimizer Search with Reinforcement Learning (2017, methodology): “A related approach is using genetic programming to evolve update equations for neural networks (e.g., Bengio et al. (1994); Runarsson & Jonsson (2000); Orchard & Wang (2016)).”
AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (2020, background): “tures, such as a second neural network [73, 60], an LSTM [67], a hierarchical RNN [88], or even a different LSTM for each weight [53].”
Practical tradeoffs between memory, compute, and performance in learned optimizers (2022, background): “Originally proposed in Bengio et al. (1992); Runarsson and Jonsson (2000), interest in learned optimizers has undergone a recent revival.”
Meta-Learning Update Rules for Unsupervised Representation Learning (2018, background): “Additionally, Runarsson and Jonsson (2000) meta-learn supervised learning rules which mix local and global network information.”
Learning to Learn Using Gradient Descent (2001, background): “Several researchers have suggested meta-1 learning systems based on neural networks and used genetic algorithms to adjust the subordinate learning algorithm [2,10,19].”
The evolution of a generalized neural learning rule (2016, methodology): “We improve substantially on [12] by adopting a recurrent, fully-connected brain network, and evolve it in a foraging task that requires the organism to adapt.”