  NeuroEvolution (NE) methods are known for applying Evolutionary Computation
to the optimisation of Artificial Neural Networks(ANNs). Despite aiding
non-expert users to design and train ANNs, the vast majority of NE approaches
disregard the knowledge that is gathered when solving other tasks, i.e.,
evolution starts from scratch for each problem, ultimately delaying the
evolutionary process. To overcome this drawback, we extend Fast Deep
Evolutionary Network Structured Representation (Fast-DENSER) to incremental
development. We hypothesise that by transferring the knowledge gained from
previous tasks we can attain superior results and speedup evolution. The
results show that the average performance of the models generated by
incremental development is statistically superior to the non-incremental
average performance. In case the number of evaluations performed by incremental
development is smaller than the performed by non-incremental development the
attained results are similar in performance, which indicates that incremental
development speeds up evolution. Lastly, the models generated using incremental
development generalise better, and thus, without further evolution, report a
superior performance on unseen problems.
