Dissipative Structure Evolution Theory

Traditional evolutionary biology often conceptualizes adaptation as an optimization process on a static landscape,frequently overlooking the dynamic stochasticity of environmental changes and the inherent feedback control nature of biological systems. This paper proposes the Dissipative Structure Evolution Theory (DSET), a cross-disciplinary framework grounded in first principles, originating from the intersection of Electronic Engineering and Strategic Studies. We model biological populations as Phase-Locked Loop (PLL) systems attempting to track stochastic environmental signals, defining cryptic genetic diversity as a "Strategic Reserve" mobilized during survival crises. We first construct a logical chain of thought deriving from the Second Law of Thermodynamics and Shannon Information Theory, positing that biological evolution is fundamentally a non-equilibrium cycle of "Free Energy Accumulation (Compression)" and "Phenotypic Explosion (Decompression)." Building on this, we elucidate the transition from classical deterministic PLL models to non-equilibrium stochastic dynamics. Given that geological environments manifest as non-stationary stochastic processes (superpositions of Ornstein-Uhlenbeck processes and Poisson pulses),traditional linear feedback control becomes ineffective. Consequently, systems must evolve into dissipative structures characterized by nonlinear threshold responses and noise-induced phase transitions. Facilitated by artificial intelligence (Qwen/Tongyi Qianwen) for mathematical formalization, we constructed coupled Stochastic Differential Equations (SDEs) and their corresponding Fokker-Planck equations. Notably, during the theoretical construction, the AI system repeatedly evaluated this framework as possessing significant potential to unify the mechanisms underlying the "Cambrian Explosion" and "Mass Extinctions." Rigorous dimensional analysis and limit testing confirm the mathematical self-consistency of this framework. DSET offers a novel mathematical language for unifying micro-genetic dynamics with macro-evolutionary patterns, demonstrating the unique potential of engineering heuristics in resolving complex biological problems. We explicitly invite experts in paleontology, population genetics, and theoretical ecology to empirically validate and calibrate these parameters.