Empirical comparison of four MOEAs on bi-objective stochastic MKP with chance constraints and dynamic capacities shows differences in behavior across uncertainty levels and change frequencies.
Optimizing monotone chance-constrained submodular functions using evolutionary multiobjective algorithms.Evolutionary Computation, 33(3):363–393
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On the Use of Bi-Objective Evolutionary Algorithms for the Stochastic MKP under Dynamic Constraints
Empirical comparison of four MOEAs on bi-objective stochastic MKP with chance constraints and dynamic capacities shows differences in behavior across uncertainty levels and change frequencies.