Information can evolve as a physical consequence of non-equilibrium dynamics, even in the absence of genes, replication, or predefined fitness functions. We present Stability-Driven Assembly (SDA), a framework in which stochastic assembly combined with differential persistence biases populations toward longer-lived motifs. Assemblies that persist longer become more frequent and are therefore more likely to participate in subsequent interactions, generating feedback that reshapes the population distribution and implements fitness-proportional sampling, realizing evolution as a natural, emergent genetic algorithm (SDA/GA) driven solely by stability. We apply SDA/GA to chemical symbol space using SMILES fragments with recombination, mutation, and a heuristic stability function. Simulations show hallmark features of evolutionary search, including scaffold-level dominance, sustained novelty, and entropy reduction, yielding open-ended dynamics absent from equilibrium models with fixed transition rates. These results motivate an evolutionary ladder hypothesis where persistence-driven selection precedes genetic replication.

翻译：信息可以在没有基因、复制或预定义适应度函数的情况下，作为非平衡动力学的物理结果而演化。我们提出稳定性驱动组装（SDA）框架，其中随机组装与差异持久性相结合，使种群偏向于更长寿的基序。持久时间更长的组装体变得更频繁，因此更有可能参与后续相互作用，从而产生反馈，重塑种群分布，并实现适应度比例采样，将演化实现为一种完全由稳定性驱动的自然涌现遗传算法（SDA/GA）。我们将SDA/GA应用于化学符号空间，使用带有重组、突变和启发式稳定性函数的SMILES片段。模拟显示了进化搜索的标志性特征，包括支架级主导、持续新颖性和熵减少，产生了具有固定跃迁速率的平衡模型所不具备的开放动力学。这些结果支持了持久性驱动选择先于遗传复制的进化阶梯假说。