进化可能像多层自编码器，通过抽象压缩遗传信息实现突变跃迁。
The Stacked Autoencoder Evolution Hypothesis
- 将进化视为分层自编码过程，基因信息在抽象层级间压缩与重构。
- 模拟显示人工化学系统能自发形成分层自编码结构。
- 解释了跳跃式进化和看似有目的的表型突变现象。
本研究提出一种新理论框架——堆叠自编码器演化假说，认为生物进化系统通过多层自编码与解码过程运作，类似于深度学习中的堆叠自编码器。进化不仅由突变和选择驱动的渐进变化，更包含自我复制对遗传信息在多层次抽象中进行压缩与重建的机制。这种分层结构使突变可在序列层面及更高阶表征层面探索多样性，甚至导致深层潜在层的微小变异引发剧烈表型改变。为验证该机制的合理性，开展了人工化学模拟，结果显示可自发生成层次化自编码结构。该框架为连续与非连续进化变化的信息动力学提供了新视角。
原文摘要 · Abstract (English)
This study introduces a novel theoretical framework, the Stacked Autoencoder Evolution Hypothesis, which proposes that biological evolutionary systems operate through multi-layered self-encoding and decoding processes, analogous to stacked autoencoders in deep learning. Rather than viewing evolution solely as gradual changes driven by mutation and selection, this hypothesis suggests that self-replication inherently compresses and reconstructs genetic information across hierarchical layers of abstraction. This layered structure enables evolutionary systems to explore diverse possibilities not only at the sequence level but also across progressively more abstract layers of representation, making it possible for even simple mutations to navigate these higher-order spaces.Such a mechanism may explain punctuated evolutionary patterns and changes that can appear as if they are goal-directed in natural evolution, by allowing mutations at deeper latent layers to trigger sudden, large-scale phenotypic shifts. To illustrate the plausibility of this mechanism, artificial chemistry simulations were conducted, demonstrating the spontaneous emergence of hierarchical autoencoder structures. This framework offers a new perspective on the informational dynamics underlying both continuous and discontinuous evolutionary change.
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