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.

翻译：人工生命领域的一个关键挑战是设计能够涌现复杂行为的系统。许多此类系统依赖于高维参数空间，而其中只有一小部分子集能够呈现有趣动力学。聚焦于连续系统这一特定情况，我们提出了"相变探测器"（PTF）算法，该算法能够高效生成位于两相边界处的参数。我们论证了此类参数更有可能展现复杂行为，并通过将PTF应用于Lenia进行验证，结果表明该算法能使有趣行为出现的频率提升两倍以上，同时保持足够高效以支持大规模搜索。