How Information Evolves: Stability-Driven Assembly and the Emergence of a Natural Genetic Algorithm

2026-01-22

AI Summary

This study investigates how information can spontaneously evolve through stability-driven processes in non-equilibrium systems devoid of genes, replication, or predefined fitness functions. To this end, the authors propose a Stability-Driven Assembly (SDA) framework, which models evolution as an emergent genetic algorithm (SDA/GA): randomly generated structures exhibit differential persistence, creating positive feedback that favors long-lived structures to participate more frequently in subsequent reactions, thereby implementing a form of natural selection based on persistence rather than replication. This mechanism supports the “evolutionary ladder” hypothesis, wherein selection can precede genetic inheritance. Simulations in a chemical symbolic space constructed from SMILES fragments—incorporating recombination, mutation, and a heuristic stability function—demonstrate hallmark evolutionary features such as scaffold dominance, sustained novelty, and entropy reduction, achieving open-ended, non-equilibrium dynamics beyond traditional equilibrium models.

Technology Category

• Machine Learning: Evolutionary Learning
• Search and Optimization: Evolutionary Computation
• Game Theory and Economic Paradigms: Imperfect Information

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• information evolution
• non-equilibrium dynamics
• stability-driven selection
• prebiotic evolution
• emergent genetic algorithm

Innovation

• Stability-Driven Assembly
• emergent genetic algorithm
• non-equilibrium dynamics
• differential persistence
• evolutionary search