6.0 Overview

6.0.1 Evolution Engine Concept

Each node contains an evolution module that observes load, packet loss, and authority volatility, then mutates its configuration accordingly.

pub struct EvolutionEngine {
    pub learning_rate: f32,
    pub adaptation_window: Duration,
    pub history: Vec<SystemSample>,
}

6.0.2 Evolution Cycle

Each cycle involves:

OBSERVE → SCORE → MUTATE → APPLY → SYNC.

Adaptations are broadcast to peers and scored globally against consensus metrics.

6.0.3 Behavioral Reinforcement

If a mutation reduces latency or drift, its traits are reinforced (kept). Otherwise, they decay from memory in subsequent epochs.

6.1 Regional Learning and Predictive Balancing

Regions evolve unique behavioral patterns — they learn optimal synchronization intervals and authority rotation timing based on regional latency models.

6.1.1 Predictive Drift Model

Each region maintains a rolling regression predicting state divergence:

let predicted_drift = alpha * last_drift + (1.0 - alpha) * observed_delta;

6.1.2 Learning Scheduler