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Evolutionary multi-objective algorithms have successfully been used in thecontext of Pareto optimization where a given constraint is relaxed into anadditional objective. In this paper, we explore the use of 3-objectiveformulations for problems with chance constraints. Our formulation trades offthe expected cost and variance of the stochastic component as well as the givendeterministic constraint. We point out benefits that this 3-objectiveformulation has compared to a bi-objective one recently investigated for chanceconstraints with Normally distributed stochastic components. Our analysis showsthat the 3-objective formulation allows to compute all required trade-offsusing 1-bit flips only, when dealing with a deterministic cardinalityconstraint. Furthermore, we carry out experimental investigations for thechance constrained dominating set problem and show the benefit for thisclassical NP-hard problem.

3-Objective Pareto Optimization for Problems with Chance Constraints