An Analysis of the RBF Hyperparameter Impact on Surrogate-Assisted Evolutionary Optimization

Algorithm 1

A basic SAEA.

	Input: initial sample size, max evaluations, EA parameters;
	Generate an initial set of vectors and evaluate with the true function;
	Repeat
	Train a surrogate model by using the vectors evaluated so far;
	Search for an optimum of the surrogate by using an EA;
	Evaluate the predicted solution (and possibly additional vectors) with the true function;
	until max evaluations;
	Output: the best solution found;