Meta-Evolutionary Self-Evolution Universal Paradigm: A Fourth-Generation Evolutionary Theory for Complex Intelligent Systems (Revised Edition)

This paper presents the Meta-Evolutionary Self-Evolution Universal Paradigm, a fourth-generation evolutionary theory that unifies error-driven binary fission, unfit-driven elimination, and attractor-state convergence into a single framework for complex intelligent systems. The revised edition adds: (1) formal mathematical notation with 7 axiomatized principles, 2 theorems with proofs, and 1 falsifiable proposition; (2) four architectural diagrams; (3) a comprehensive comparison table with Novelty Search, POET, NEAT, and artificial embryogeny; (4) a case study applying the framework to a quantitative trading system. Key contributions: A formal framework where evolution is driven by error signals rather than fitness gradients, implementing dual fission (competence + escape) and three-layer elimination (unfit + redundant + dormant). The paradigm is domain-agnostic and applicable to AI systems, synthetic biology, and distributed computing.