  Evolutionary Algorithms (EAs) play a crucial role in the architectural
configuration and training of Artificial Deep Neural Networks (DNNs), a process
known as neuroevolution. However, neuroevolution is hindered by its inherent
computational expense, requiring multiple generations, a large population, and
numerous epochs. The most computationally intensive aspect lies in evaluating
the fitness function of a single candidate solution. To address this challenge,
we employ Surrogate-assisted EAs (SAEAs). While a few SAEAs approaches have
been proposed in neuroevolution, none have been applied to truly large DNNs due
to issues like intractable information usage. In this work, drawing inspiration
from Genetic Programming semantics, we use phenotypic distance vectors,
outputted from DNNs, alongside Kriging Partial Least Squares (KPLS), an
approach that is effective in handling these large vectors, making them
suitable for search. Our proposed approach, named Neuro-Linear Genetic
Programming surrogate model (NeuroLGP-SM), efficiently and accurately estimates
DNN fitness without the need for complete evaluations. NeuroLGP-SM demonstrates
competitive or superior results compared to 12 other methods, including
NeuroLGP without SM, convolutional neural networks, support vector machines,
and autoencoders. Additionally, it is worth noting that NeuroLGP-SM is 25% more
energy-efficient than its NeuroLGP counterpart. This efficiency advantage adds
to the overall appeal of our proposed NeuroLGP-SM in optimising the
configuration of large DNNs.
