Multi-objective Evolutionary Approaches for the Knapsack Problem with Stochastic Profits

Author:

Perera Kokila KasuniORCID,Neumann FrankORCID,Neumann AnetaORCID

Publisher

Springer Nature Switzerland

Reference44 articles.

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3. Assimi, H., Harper, O., Xie, Y., Neumann, A., Neumann, F.: Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. In: 24th European Conference on Artificial Intelligence, vol. 325, pp. 307–314. IOS Press (2020)

4. Baumeister, D., Boes, L., Laußmann, C.: Time-constrained participatory budgeting under uncertain project costs. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), pp. 74–80 (2022)

5. Chauhan, A., Baranwal, M., Basumatary, A.: PowRL: a reinforcement learning framework for robust management of power networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 14757–14764 (2023)

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