  One key challenge in Artificial Life is designing systems that display an
emergence of complex behaviors. Many such systems depend on a high-dimensional
parameter space, only a small subset of which displays interesting dynamics.
Focusing on the case of continuous systems, we introduce the 'Phase Transition
Finder'(PTF) algorithm, which can be used to efficiently generate parameters
lying at the border between two phases. We argue that such points are more
likely to display complex behaviors, and confirm this by applying PTF to Lenia
showing it can increase the frequency of interesting behaviors more than
two-fold, while remaining efficient enough for large-scale searches.
