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Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficientcomputational models with the goal of approximating the fitness function inevolutionary computation systems. This area of research has been active forover two decades and has received significant attention from the specialisedresearch community in different areas, for example, single and many objectiveoptimisation or dynamic and stationary optimisation problems. An emergent andexciting area that has received little attention from the SAEAs community is inneuroevolution. This refers to the use of evolutionary algorithms in theautomatic configuration of artificial neural network (ANN) architectures,hyper-parameters and/or the training of ANNs. However, ANNs suffer from twomajor issues: (a) the use of highly-intense computational power for theircorrect training, and (b) the highly specialised human expertise required tocorrectly configure ANNs necessary to get a well-performing network. This workaims to fill this important research gap in SAEAs in neuroevolution byaddressing these two issues. We demonstrate how one can use a Kriging PartialLeast Squares method that allows efficient computation of good approximatesurrogate models compared to the well-known Kriging method, which normallycannot be used in neuroevolution due to the high dimensionality of the data.

Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares