ENTRO-EVO: Adaptive Entropy Weighting for Self-Calibrating Intelligence Systems

This work introduces ENTRO-EVO, an adaptive entropy-weighting framework for self-calibrating intelligent systems operating under stochastic perturbations and constrained environments. Unlike classical optimization approaches, ENTRO-EVO models system regulation as a thermodynamic control process, where entropy plays a central role in maintaining dynamic balance between competing objectives. The system integrates feedback control, adaptive gain tuning, and entropy-driven weight updates to regulate a composite objective function (Ψ) under uncertainty. Across 11 iterative versions, the system demonstrates:- Robust self-regulation under random spikes, bursts, and noise- Emergent phase transitions between stabilization, control, and exploration regimes- Temperature-driven annealing behavior enabling adaptive exploration-exploitation balance- Convergence toward a target constraint without strict optimization guarantees A key finding of this research is the intrinsic trade-off between objective minimization and weight balance: Ψ minimization ↔ entropy preservation This suggests that entropy-constrained systems cannot simultaneously achieve perfect optimization and structural balance, revealing a fundamental property of adaptive intelligent systems. ENTRO-EVO does not behave as a traditional optimizer but rather as a controlled chaotic system capable of approximate convergence through continuous adaptation. This work contributes to the intersection of artificial intelligence, control theory, and complex systems, proposing a novel paradigm for entropy-driven intelligence under uncertainty.