Runtime Analyses of NSGA-III on Many-Objective Problems-Reference-Cited by-同舟云学术

Runtime Analyses of NSGA-III on Many-Objective Problems

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

Opris Andre¹^ORCID,Dang Duc-Cuong¹^ORCID,Neumann Frank²^ORCID,Sudholt Dirk¹^ORCID

Affiliation:

1. University of Passau, Passau, Germany

2. University of Adelaide, Adelaide, Australia

Funder

Australian Research Council

Publisher

ACM

Reference41 articles.

1. G. Badkobeh, P. K. Lehre, and D. Sudholt. Black-box complexity of parallel search with distributed populations. In Proceedings of the Foundations of Genetic Algorithms (FOGA'15), pages 3--15. ACM Press, 2015.

2. C. Bian and C. Qian. Better running time of the non-dominated sorting genetic algorithm II (NSGA-II) by using stochastic tournament selection. In Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN '22), volume 13399 of LNCS, pages 428--441. Springer, 2022.

3. Investigating the Normalization Procedure of NSGA-III

4. S. Cerf, B. Doerr, B. Hebras, Y. Kahane, and S. Wietheger. The first proven performance guarantees for the non-dominated sorting genetic algorithm II (NSGA-II) on a combinatorial optimization problem. In Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI 2023, pages 5522--5530. ijcai.org, 2023.

5. C. Coello, D. Van Veldhuizen, and G. Lamont. Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic Algorithms and Evolutionary Computation. Springer US, 2013.

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