  Multi-objective optimisation is a popular approach for finding solutions to
complex problems with large search spaces that reliably yields good
optimisation results. However, with the rise of cyber-physical systems, emerges
a new challenge of noisy fitness functions, whose objective value for a given
configuration is non-deterministic, producing varying results on each
execution. This leads to an optimisation process that is based on
stochastically sampled information, ultimately favouring solutions with fitness
values that have co-incidentally high outlier noise. In turn, the results are
unfaithful due to their large discrepancies between sampled and expectable
objective values. Motivated by our work on noisy automated driving systems, we
present the results of our ongoing research to counteract the effect of noisy
fitness functions without requiring repeated executions of each solution. Our
method kNN-Avg identifies the k-nearest neighbours of a solution point and uses
the weighted average value as a surrogate for its actually sampled fitness. We
demonstrate the viability of kNN-Avg on common benchmark problems and show that
it produces comparably good solutions whose fitness values are closer to the
expected value.
