Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem-Reference-Cited by-同舟云学术

Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem

Published:2024-07-14 Issue: Volume:53 Page:520-528
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Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Hewa Pathiranage Ishara¹^ORCID,Neumann Frank¹^ORCID,Antipov Denis¹^ORCID,Neumann Aneta¹^ORCID

Affiliation:

1. Optimisation and Logistics,School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia

Funder

Australian Research Council

Publisher

ACM

Reference41 articles.

1. Resource and network management for satellite communications systems: a chance-constrained approach;Abe Yuma;IFAC-PapersOnLine,2020

2. Saba Sadeghi Ahouei, Jakob de Nobel, Aneta Neumann, Thomas Bäck, and Frank Neumann. 2024. Evolving reliable differentiating constraints for the chance-constrained maximum coverage problem. In Genetic and Evolutionary Computation Conference, GECCO 2024. ACM. To appear.

3. Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann, and Frank Neumann. 2020. Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. In European Conference on Artificial Intelligence, ECAI 2020, Vol. 325. IOS Press, 307--314.

4. Jakob Bossek, Frank Neumann, Pan Peng, and Dirk Sudholt. 2019. Runtime analysis of randomized search heuristics for dynamic graph coloring. In Genetic and Evolutionary Computation Conference, GECCO 2019. ACM, 1443--1451.

5. Leslie Pérez Cáceres and Thomas Stützle (Eds.). 2023. Evolutionary Computation in Combinatorial Optimization - 23rd European Conference, EvoCOP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, 2023, Proceedings. Lecture Notes in Computer Science, Vol. 13987. Springer.