This repository offers an approach to combine the Covariance Matrix Adaptive Evolution Strategy (CMA-ES) and Bayesian Optimization (BO).

This part mainly focusses on searching the approximate best solution to the surrogate model built by gaussian processes. CMA-ES is adopting on the acquisition function as the optimization method

This part mainly focuses on Surrogate model assisted evolutionary Strategies (SAES). The fitness approximation with gaussian processes firstly used for the pre-selection of the offspring which can reduce the computational cost of the expensive fitness function.