The Forgetting Engine: A New Optimization Paradigm : The Forgetting Engine (FE) is a patent‑pending algorithm that introduces strategic elimination with paradox retention as a new computational primitive. Instead of only searching for the right answer, FE works by intelligently forgetting the wrong ones—turning elimination into a source of information.

In large‑scale tests across four domains, FE consistently outperformed standard methods:

• 82.2% improvement in Traveling Salesman Problems (200 cities)

• 89.3% improvement in Vehicle Routing Problems (800 customers)

• 6.68% improvement in Neural Architecture Search

• 80% improvement + 2.15× speedup in protein folding

Explore the FE Algorithm Replication Library

Below you’ll find the official JSON documentation packages for The FE Algorithm. Each file is machine‑readable, legally timestamped, and designed for replication.

Together, they provide the full picture: the discovery, the algorithm itself, and the validation across multiple domains.

Available JSONs:

Dataset Manifest – Index of all experimental datasets and validation standards.

• Discovery & Patent Draft (v1.0) – Canonical definition of the FE Algorithm, paradox metrics, and the protein folding benchmark.

• Validation Suite (Replication Package) – Structured experimental record for replication across TSP, VRP, NAS, and GCP.

• Master Validation Whitepaper – Enterprise‑grade validation, scaling law, patent portfolio, and commercial applications.

• TSP Experimental Series – Full Traveling Salesman Problem runs (15 → 200 cities) with raw data, code, and charts.

• VRP Pharmaceutical‑Grade Validation – Vehicle Routing Problem validation 250 TRIALS

• Protein Folding Discovery (2000 Trials) – Original benchmark experiment where the FE Algorithm achieved +80% success rate, 2.15× faster convergence, and 14% better energy minima compared to Monte Carlo.

• Neural Architecture Search Validation – 300 trials across small, medium, and large datasets, showing +3.8% to +8.4% accuracy improvements over random search with more reliable convergence and higher‑quality architectures.

• Quantum Compilation Breakthrough (IBM QX5) SIMULATION** 5,000 trials on IBM’s 16‑qubit processor. FE reduced gate count by 27.8%, improved fidelity by 3.7%, and became the first algorithm to outperform IBM’s own compiler. A transformational leap for the NISQ era.

• Quantitative Finance Validation SIMULATION** SEC‑grade backtesting across 14.7M data points (2022–2024). FE achieved a Sharpe ratio of 3.4 vs 1.2 industry standard, reduced drawdowns by 55%, and outperformed during the FTX collapse and banking crisis. A commercially viable, regulator‑compliant market‑making breakthrough.