论文标题
选择连续的寿命样细胞自动机，以制止不可预测性：用于临时发生
Selecting Continuous Life-Like Cellular Automata for Halting Unpredictability: Evolving for Abiogenesis
论文作者
论文摘要
已经对具有所需的新兴特性（例如支持滑翔机）的工程师CA应用了大量努力。连续CA的最新工作产生了多种引人注目的生物活力模式，并将CA研究扩展到连续值的域，多个通道和更高的维度使他们的研究变得复杂。在这项工作中，我们基于一个简单的想法，即CA可能会无限期地生长出来以及完全消失的模式，并且很难预测提前的差异，我们就可以通过两个步骤制定了一种发展CA和CA模式的策略。我们策略的第二部分通过选择迁移率和平均细胞值的保存来发展模式。我们通过在17个Lenia CA中的17个中重新发现滑翔机来验证我们的模式演化方法，并报告了4个新进化的Ca和1个随机进化的Ca，这些CA支持了新型进化的滑翔机模式。此处报道的CA与先前描述的Lenia CA共享邻里内核，但比Lenia对应物的典型动态更广泛。根据MIT许可（https://github.com/rivesunder/yuca）提供了不断发展的CA的代码。
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).
