[P] The FE Algorithm: Replication Library and Validation Results (Protein Folding, TSP, VRP, NAS, Quantum, Finance)

I’ve been working on The FE Algorithm, a paradox‑retention optimization method that treats contradiction as signal instead of noise. Instead of discarding candidates that look unpromising, it preserves paradoxical ones that carry hidden potential.

The Replication Library is now public with machine‑readable JSONs, replication code, and validation across multiple domains:

• Protein Folding: 2,000 trials, p < 0.001, 2.1× faster than Monte Carlo, ~80% higher success rate
• Traveling Salesman Problem (TSP): 82.2% improvement at 200 cities
• Vehicle Routing Problem (VRP): 79 year Monte Carlo breakthrough, up to 89% improvement at enterprise scale
• Neural Architecture Search (NAS): 300 trials, 3.8 to 8.4% accuracy gains
• Quantum Compilation (simulation): IBM QX5 model, 27.8% gate reduction, 3.7% fidelity gain vs Qiskit baseline
• Quantitative Finance (simulation and backtest): 14.7M datapoints, Sharpe 3.4 vs 1.2, annualized return 47% vs 16%

All experiments are documented in machine‑readable form to support reproducibility and independent verification.

I would love to hear thoughts on whether schema‑driven replication libraries could become a standard for publishing algorithmic breakthroughs.