  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).
