  The interest in accelerating black-box optimizers has resulted in several
surrogate model-assisted version of the Covariance Matrix Adaptation Evolution
Strategy, a state-of-the-art continuous black-box optimizer. The version called
Surrogate CMA-ES uses Gaussian processes or random forests surrogate models
with a generation-based evolution control. This paper presents an adaptive
improvement for S-CMA-ES based on a general procedure introduced with the
s*ACM-ES algorithm, in which the number of generations using the surrogate
model before retraining is adjusted depending on the performance of the last
instance of the surrogate. Three algorithms that differ in the measure of the
surrogate model's performance are evaluated on the COCO/BBOB framework. The
results show a minor improvement on S-CMA-ES with constant model lifelengths,
especially when larger lifelengths are considered.
