Neumann, A.Neumann, F.Bäck, T.Preuss, M.Deutz, A.H.Wang, H.Doerr, C.Emmerich, M.T.M.Trautmann, H.2020-09-162020-09-162020Lecture Notes in Artificial Intelligence, 2020 / Bäck, T., Preuss, M., Deutz, A.H., Wang, H., Doerr, C., Emmerich, M.T.M., Trautmann, H. (ed./s), vol.12269, pp.404-41797830305811140302-97431611-3349http://hdl.handle.net/2440/128130Many real-world optimisation problems can be stated in terms of submodular functions. A lot of evolutionary multi-objective algorithms have recently been analyzed and applied to submodular problems with different types of constraints. We present a first runtime analysis of evolutionary multi-objective algorithms for chance-constrained submodular functions. Here, the constraint involves stochastic components and the constraint can only be violated with a small probability of α. We show that the GSEMO algorithm obtains the same worst case performance guarantees as recently analyzed greedy algorithms. Furthermore, we investigate the behavior of evolutionary multi-objective algorithms such as GSEMO and NSGA-II on different submodular chance constrained network problems. Our experimental results show that this leads to significant performance improvements compared to the greedy algorithm.en© Springer Nature Switzerland AG 2020Conference paper100002607610.1007/978-3-030-58112-1_282-s2.0-85091273367546977Neumann, A. [0000-0002-0036-4782]Neumann, F. [0000-0002-2721-3618]