3-Objective Pareto Optimization for Problems with Chance Constraints

Frank Neumann, Carsten Witt

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the 2023 Genetic and Evolutionary Computation Conference (GECCO)
PublisherAssociation for Computing Machinery
Publication date2023
ISBN (Electronic)979-8-4007-0119-1
Publication statusPublished - 2023
Event2023 Genetic and Evolutionary Computation Conference - Lisbon, Portugal
Duration: 15 Jul 202319 Jul 2023


Conference2023 Genetic and Evolutionary Computation Conference
SponsorAssociation for Computing Machinery


  • Chance constraints
  • Evolutionary multi-objective optimization
  • Runtime analysis
  • Theory


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