Selecting Continuous Life-Like Cellular Automata for Halting Unpredictability: Evolving for Abiogenesis
This work addresses the challenge of designing CA with emergent properties for researchers in artificial life and complex systems, though it is incremental as it builds on existing Lenia CA frameworks.
The authors tackled the problem of evolving continuous cellular automata (CA) to support complex, unpredictable patterns by developing a two-step strategy that selects for indefinite growth and complete vanishing, and validated it by rediscovering gliders in 17 Lenia CA and discovering 4 new CA with novel glider patterns.
Substantial efforts have been applied to engineer CA with desired emergent properties, such as supporting gliders. Recent work in continuous CA has generated a wide variety of compelling bioreminiscent patterns, and the expansion of CA research into continuously-valued domains, multiple channels, and higher dimensions complicates their study. In this work we devise a strategy for evolving CA and CA patterns in two steps, based on the simple idea that CA are likely to be complex and computationally capable if they support patterns that grow indefinitely as well as patterns that vanish completely, and are difficult to predict the difference in advance. The second part of our strategy evolves patterns by selecting for mobility and conservation of mean cell value. We validate our pattern evolution method by re-discovering gliders in 17 of 17 Lenia CA, and also report 4 new evolved CA and 1 randomly evolved CA that support novel evolved glider patterns. The CA reported here share neighborhood kernels with previously described Lenia CA, but exhibit a wider range of typical dynamics than their Lenia counterparts. Code for evolving continuous CA is made available under an MIT License (https://github.com/rivesunder/yuca).