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Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems
Its results are compared with those from other algorithms using different surrogate functions.
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties
Chance constraints are frequently used to limit the probability of constraint violations in real-world optimization problems where the constraints involve stochastic components.
Runtime Performance of Evolutionary Algorithms for the Chance-constrained Makespan Scheduling Problem
In addition, we investigate the experimental performance of the two algorithms for the two variants.
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