Lenia and Expanded Universe
This work advances artificial life by generating complex, lifelike patterns, though it is incremental as it builds on existing Lenia frameworks.
The authors extended Lenia, a continuous cellular automata family, to higher dimensions and multiple kernels/channels, discovering new self-organizing patterns such as polyhedral symmetries, self-replication, and 'virtual eukaryotes' with internal division of labor.
We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.