  We present an empirical study of a range of evolutionary algorithms applied
to various noisy combinatorial optimisation problems. There are three sets of
experiments. The first looks at several toy problems, such as OneMax and other
linear problems. We find that UMDA and the Paired-Crossover Evolutionary
Algorithm (PCEA) are the only ones able to cope robustly with noise, within a
reasonable fixed time budget. In the second stage, UMDA and PCEA are then
tested on more complex noisy problems: SubsetSum, Knapsack and SetCover. Both
perform well under increasing levels of noise, with UMDA being the better of
the two. In the third stage, we consider two noisy multi-objective problems
(CountingOnesCountingZeros and a multi-objective formulation of SetCover). We
compare several adaptations of UMDA for multi-objective problems with the
Simple Evolutionary Multi-objective Optimiser (SEMO) and NSGA-II. We conclude
that UMDA, and its variants, can be highly effective on a variety of noisy
combinatorial optimisation, outperforming many other evolutionary algorithms.
