Viet Anh Do

Research Interests

Analysis of Algorithms and Complexity Neural, Evolutionary and Fuzzy Computation Optimisation

Mr Viet Anh Do

Postdoctoral Research Fellow (A) (with PhD)

School of Computer Science and Information Technology

College of Engineering and Information Technology

I obtained the Master of Computer Science degree from University of Adelaide in 2020, and am currently a PhD candidate. My research focuses on optimisation and evolutionary computation. I am mainly interested in fundamental concepts and questions, as well as runtime analysis of algorithms.

Language Competency
English Can read, write, speak, understand spoken and peer review
Vietnamese Can read, write, speak, understand spoken and peer review

Date Institution name Country Title
2020 - 2024 University of Adelaide Australia PhD
2018 - 2020 University of Adelaide Australia Master of Computer Science

Year Citation
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem.. CoRR, abs/2201.10316.
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Analysis of Evolutionary Diversity Optimization for Permutation Problems.. ACM Trans. Evol. Learn. Optim., 2, 11:1.

Year Citation
2026 Do, A. V., Galhenage, E., Neumann, A., Neumann, F., Uzunov, A. V., & Szabo, C. (2026). A Hybrid Multi-Agent Reinforcement Learning Framework for Decentralised Search-And-Interact Tasks Under Partial Observability. In Lecture Notes in Computer Science Vol. 16371 LNAI (pp. 387-401). Springer Nature Singapore.
DOI
2024 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2024). Evolutionary Multi-objective Diversity Optimization. In M. Affenzeller, S. M. Winkler, A. V. Kononova, H. Trautmann, T. Tusar, P. Machado, & T. Bäck (Eds.), Proeedings of the 18th International Conference on Parallel Problem Solving from Nature, Part IV (PPSN 2024), as published in Lecture Notes in Computer Science Vol. 15151 (pp. 117-134). Cham, Switzerland: Springer Nature.
DOI Scopus2 WoS1
2023 Do, A. V., Neumann, A., Neumann, F., & Sutton, A. M. (2023). Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Abstracts of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS, 2023) as published in Advances in Neural Information Processing Systems Vol. 36 (pp. 15 pages). Online: Neural information processing systems foundation.
Scopus13 WoS7
2023 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2023). Diverse approximations for monotone submodular maximization problems with a matroid constraint. In E. Elkind (Ed.), Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Vol. 2023-August (pp. 5558-5566). Macao, S.A.R: IJCAI.
DOI Scopus6 WoS6
2023 Neumann, F., Neumann, A., Qian, C., Do, A., De Nobel, J., Vermetten, D., . . . Back, T. (2023). Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2023) (pp. 1-9). Online: Institute of Electrical and Electronics Engineers (IEEE).
DOI Scopus2
2023 Yan, X., Do, A. V., Shi, F., Qin, X., & Neumann, F. (2023). Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties. In H. Fujita, H. Perez-Meana, & A. Hernandez-Matamoros (Eds.), Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023), as published in Frontiers in Artificial Intelligence and Applications Vol. 372 (pp. 2826-2833). Kraków, Poland: IOS Press.
DOI Scopus6
2022 Nikfarjam, A., Do, A. V., & Neumann, F. (2022). Analysis of Quality Diversity Algorithms for the Knapsack Problem.. In G. Rudolph, A. V. Kononova, H. E. Aguirre, P. Kerschke, G. Ochoa, & T. Tusar (Eds.), PPSN (2) Vol. 13399 (pp. 413-427). Springer.
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Niching-based evolutionary diversity optimization for the traveling salesperson problem. In J. E. Fieldsend, & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) (pp. 684-693). Online: Association for Computing Machinery.
DOI Scopus4 WoS3
2022 Nikfarjam, A., Viet Do, A., & Neumann, F. (2022). Analysis of Quality Diversity Algorithms for the Knapsack Problem. In Proceedings, Part II of the 17th International Conference on Parallel Problem Solving from Nature (PPSN XVII), as published in Lecture Notes in Computer Science Vol. 13399 (pp. 413-427). Online: Springer.
DOI Scopus12 WoS9
2020 Do, A. V., Bossek, J., Neumann, A., & Neumann, F. (2020). Evolving diverse sets of tours for the Travelling Salesperson Problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'20) (pp. 681-689). New York, NY, USA: Association for Computing Machinery.
DOI Scopus29 WoS25
2020 Do, V., & Neumann, F. (2020). Maximizing submodular or monotone functions under partition matroid constraints by multi-objective evolutionary algorithms. In T. Bäck, M. Preuss, A. H. Deutz, H. Wang, C. Doerr, M. T. M. Emmerich, & H. Trautmann (Eds.), Proceedings of the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI), as published in Parallel Problem Solving from Nature – PPSN XVI, Part II Vol. 12270 (pp. 588-603). Switzerland: Springer Nature.
DOI Scopus8 WoS6

Year Citation
2024 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2024). Evolutionary Multi-Objective Diversity Optimization..
2023 Neumann, F., Neumann, A., Qian, C., Do, A. V., Nobel, J. D., Vermetten, D., . . . Bäck, T. (2023). Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler..
2023 Do, A. V., Neumann, A., Neumann, F., & Sutton, A. M. (2023). Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems..
2023 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2023). Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint..
2023 Yan, X., Do, A. V., Shi, F., Qin, X., & Neumann, F. (2023). Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties..

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