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Found 3 papers, 1 papers with code
Growing Artificial Neural Networks for Control: the Role of Neuronal Diversity
Artificial neural networks (ANNs), on the other hand, are traditionally optimized in the space of weights.
Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the network structure.
Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization
Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules.
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