  In NeuroEvolution, the topologies of artificial neural networks are optimized
with evolutionary algorithms to solve tasks in data regression, data
classification, or reinforcement learning. One downside of NeuroEvolution is
the large amount of necessary fitness evaluations, which might render it
inefficient for tasks with expensive evaluations, such as real-time learning.
For these expensive optimization tasks, surrogate model-based optimization is
frequently applied as it features a good evaluation efficiency. While a
combination of both procedures appears as a valuable solution, the definition
of adequate distance measures for the surrogate modeling process is difficult.
In this study, we will extend cartesian genetic programming of artificial
neural networks by the use of surrogate model-based optimization. We propose
different distance measures and test our algorithm on a replicable benchmark
task. The results indicate that we can significantly increase the evaluation
efficiency and that a phenotypic distance, which is based on the behavior of
the associated neural networks, is most promising.
