Complexity is a signature quality of interest in artificial life systems. Alongside other dimensions of assessment, it is common to quantify genome sites that contribute to fitness as a complexity measure. However, limitations to the sensitivity of fitness assays in models with implicit replication criteria involving rich biotic interactions introduce the possibility of difficult-to-detect ``cryptic'' adaptive sites, which contribute small fitness effects below the threshold of individual detectability or involve epistatic redundancies. Here, we propose three knockout-based assay procedures designed to quantify cryptic adaptive sites within digital genomes. We report initial tests of these methods on a simple genome model with explicitly configured site fitness effects. In these limited tests, estimation results reflect ground truth cryptic sequence complexities well. Presented work provides initial steps toward development of new methods and software tools that improve the resolution, rigor, and tractability of complexity analyses across alife systems, particularly those requiring expensive in situ assessments of organism fitness.

翻译：复杂度是人工生命系统中备受关注的关键特征。除其他评估维度外，量化对适应度有贡献的基因组位点常被用作复杂度度量标准。然而，在具有隐含复制标准且涉及丰富生物相互作用的模型中，适应度检测的灵敏度存在局限，这可能导致难以检测的"隐性"适应性位点——这些位点产生的适应度效应低于个体检测阈值，或涉及上位冗余效应。本文提出三种基于基因敲除的检测程序，旨在量化数字基因组中的隐性适应性位点。我们在具有显式配置位点适应度效应的简单基因组模型中对这些方法进行了初步测试。在有限测试中，估算结果能较好地反映隐性序列复杂度的真实情况。本研究为开发新方法和软件工具迈出了初步步伐，这些工具将提升人工生命系统复杂度分析的分辨率、严谨性和可处理性，尤其适用于需要昂贵原位生物适应度评估的系统。