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.
| Original language | English |
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| Title of host publication | Proceedings of the 2023 Genetic and Evolutionary Computation Conference (GECCO) |
| Publisher | Association for Computing Machinery |
| Publication date | 2023 |
| Pages | 731-739 |
| ISBN (Electronic) | 979-8-4007-0119-1 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 2023 Genetic and Evolutionary Computation Conference - Lisbon, Portugal Duration: 15 Jul 2023 → 19 Jul 2023 |
Conference
| Conference | 2023 Genetic and Evolutionary Computation Conference |
|---|---|
| Country/Territory | Portugal |
| City | Lisbon |
| Period | 15/07/2023 → 19/07/2023 |
| Sponsor | Association for Computing Machinery |
Keywords
- Chance constraints
- Evolutionary multi-objective optimization
- Runtime analysis
- Theory