This paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE.

This paper utilizes the expected gradient of the Gaussian Process (GP) in a surrogate-assisted evolutionary algorithm. Specifically, our proposal iteratively runs a quasi-Newton method (L-BFGS-B) changing initial points on multiple GPs constructed to approximate the promising region of the objective function.

This paper presents a generalized prior-validation framework for algorithmic configurations, which can be applicable to major variants of self-adaptive DEs that adapt the scaling factor, the crossover rate, and/or the mutation/crossover strategies for each individual.