Two Gaussian Approaches to Black-Box Optomization

This work addresses optimization challenges for researchers and practitioners in fields like machine learning and engineering, but it appears incremental as it builds on existing CMA-ES and Gaussian process methods.

The paper tackles the problem of improving black-box optimization by integrating Gaussian processes as surrogate models into the CMA-ES framework, resulting in enhanced performance metrics such as reduced function evaluations or improved convergence rates, though specific numbers are not provided in the abstract.