选择连续类生命细胞自动机停止不可预测性：进化为无生源
Selecting Continuous Life-Like Cellular Automata for Halting Unpredictability: Evolving for Abiogenesis
摘要：
大量的努力已经应用到工程CA所需的紧急属性，如支持滑翔机。最近在连续CA的工作已经产生了各种各样的引人注目的生物修复模式，CA的研究扩展到连续值域，多通道，和更高的维度复杂化他们的研究。在这项工作中，我们设计了一个战略，CA和CA模式的发展分两步，基于一个简单的想法，CA很可能是复杂的和计算能力，如果他们支持的模式，无限增长，以及完全消失的模式，是很难预测的差异提前。我们的策略的第二部分通过选择平均单元值的移动性和保守性来发展模式。我们验证了我们的模式进化方法，重新发现滑翔机在17个Lenia CA，并报告4个新的进化CA和1个随机进化CA，支持新的进化滑翔机模式。这里报道的CA与先前描述的Lenia CA共享邻域内核，但表现出比Lenia对应物更广泛的典型动态。用于演进连续CA的代码在MIT许可证（https：//github.com/rivesunder/yuca）下提供。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
