3-Objective Pareto Optimization for Problems with Chance Constraints
Date
2023
Authors
Neumann, F.
Witt, C.
Editors
Paquete, L.
Advisors
Journal Title
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Conference paper
Citation
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23), 2023 / Paquete, L. (ed./s), vol.abs/2304.08774, pp.731-739
Statement of Responsibility
Frank Neumann, Carsten Witt
Conference Name
Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2023 - 19 Jul 2023 : Lisbon, Portugal)
Abstract
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.
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© 2023 by the Association for Computing Machinery, Inc. (ACM).