Adaptive Generation-Based Evolution Control for Gaussian Process Surrogate Models
This is an incremental improvement for researchers and practitioners using surrogate-assisted evolution strategies in continuous black-box optimization.
The paper tackled the problem of accelerating black-box optimizers by improving the Surrogate CMA-ES algorithm with adaptive generation-based evolution control for Gaussian process surrogate models, resulting in minor improvements, especially with larger lifelengths, as evaluated on the COCO/BBOB framework.
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.