Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem-Reference-Cited by-同舟云学术

Multi-Objective Evolutionary Algorithms with Sliding Window Selection for the Dynamic Chance-Constrained Knapsack Problem

Published:2024-07-14 Issue: Volume:325 Page:223-231
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference
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Author:

Perera Kokila Kasuni¹^ORCID,Neumann Aneta¹^ORCID

Affiliation:

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

Publisher

ACM

Reference39 articles.

1. SCIP: solving constraint integer programs

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 24th European Conference on Artificial Intelligence, Vol. 325. IOS Press, 307--314.

4. Jakob Bossek, Aneta Neumann, and Frank Neumann. 2023. On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2023. ACM, 248--256.

5. Reliability-Based Optimization Using Evolutionary Algorithms

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-objective Evolutionary Approaches for the Knapsack Problem with Stochastic Profits;Lecture Notes in Computer Science;2024

2. Sliding Window Bi-objective Evolutionary Algorithms for Optimizing Chance-Constrained Monotone Submodular Functions;Lecture Notes in Computer Science;2024