Neumann, F.Witt, C.Affenzeller, M.Winkler, S.M.Kononova, A.V.Trautmann, H.Tusar, T.Machado, P.Back, T.2025-07-082025-07-082024Lecture Notes in Artificial Intelligence, 2024 / Affenzeller, M., Winkler, S.M., Kononova, A.V., Trautmann, H., Tusar, T., Machado, P., Back, T. (ed./s), vol.15150, pp.36-52978-3-031-70071-20302-97431611-3349https://hdl.handle.net/2440/145751Constrained single-objective problems have been frequently tackled by evolutionary multi-objective algorithms where the constraint is relaxed into an additional objective. Recently, it has been shown that Pareto optimization approaches using bi-objective models can be significantly sped up using sliding windows [16]. In this paper, we extend the sliding window approach to 3-objective formulations for tackling chance constrained problems. On the theoretical side, we show that our new sliding window approach improves previous runtime bounds obtained in [15] while maintaining the same approximation guarantees. Our experimental investigations for the chance constrained dominating set problem show that our new sliding window approach allows one to solve much larger instances in a much more efficient way than the 3-objective approach presented in [15].en© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024chance constraints; evolutionary algorithms; multi-objective optimizationConference paper10.1007/978-3-031-70071-2_3702185Neumann, F. [0000-0002-2721-3618]