  Evolutionary multi-objective algorithms have successfully been used in the
context of Pareto optimization where a given constraint is relaxed into an
additional objective. In this paper, we explore the use of 3-objective
formulations for problems with chance constraints. Our formulation trades off
the expected cost and variance of the stochastic component as well as the given
deterministic constraint. We point out benefits that this 3-objective
formulation has compared to a bi-objective one recently investigated for chance
constraints with Normally distributed stochastic components. Our analysis shows
that the 3-objective formulation allows to compute all required trade-offs
using 1-bit flips only, when dealing with a deterministic cardinality
constraint. Furthermore, we carry out experimental investigations for the
chance constrained dominating set problem and show the benefit for this
classical NP-hard problem.
