Exploring Black‑Box Optimization [R]

SGOLab is an early-stage open-source research framework targeting scalable geometric optimization of black-box problems, available on GitHub under misa-hdez/sgo-lab. The core idea replaces explicit gradient or surrogate model dependencies with domain-driven reference systems to steer search, a structurally different approach from Bayesian optimization or CMA-ES baselines. Currently at 1 contributor and 1 fork, benchmarks and formal comparisons remain unpublished. Researchers working on derivative-free optimization and practitioners hitting scalability walls with standard black-box solvers should watch this project closely as methodology matures.