  Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient
computational models with the goal of approximating the fitness function in
evolutionary computation systems. This area of research has been active for
over two decades and has received significant attention from the specialised
research community in different areas, for example, single and many objective
optimisation or dynamic and stationary optimisation problems. An emergent and
exciting area that has received little attention from the SAEAs community is in
neuroevolution. This refers to the use of evolutionary algorithms in the
automatic configuration of artificial neural network (ANN) architectures,
hyper-parameters and/or the training of ANNs. However, ANNs suffer from two
major issues: (a) the use of highly-intense computational power for their
correct training, and (b) the highly specialised human expertise required to
correctly configure ANNs necessary to get a well-performing network. This work
aims to fill this important research gap in SAEAs in neuroevolution by
addressing these two issues. We demonstrate how one can use a Kriging Partial
Least Squares method that allows efficient computation of good approximate
surrogate models compared to the well-known Kriging method, which normally
cannot be used in neuroevolution due to the high dimensionality of the data.
