  Deep neural networks proved to be a very useful and powerful tool with many
practical applications. They especially excel at learning from large data sets
with labeled samples. However, in order to achieve good learning results, the
network architecture has to be carefully designed. Creating an optimal topology
requires a lot of experience and knowledge. Unfortunately there are no
practically applicable algorithms which could help in this situation. Using an
evolutionary process to develop new network topologies might solve this
problem. The limiting factor in this case is the speed of evaluation of a
single specimen (a single network architecture), which includes learning based
on the whole large dataset. In this paper we propose to overcome this problem
by using a fitness prediction technique: use subsets of the original training
set to conduct the training process and use its results as an approximation of
specimen's fitness. We discuss the feasibility of this approach in context of
the desired fitness predictor features and analyze whether subsets obtained in
an evolutionary process can be used to estimate the fitness of the network
topology. Finally we draw conclusions from our experiments and outline plans
for future work.
