Viet Anh Do
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and 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 Competencies
Language Competency English Can read, write, speak, understand spoken and peer review Vietnamese Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2018 - 2020 University of Adelaide Australia Master of Computer Science -
Research Interests
-
Journals
Year Citation 2024 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2024). Evolutionary Multi-objective Diversity Optimization. CoRR, abs/2401.07454, 117-134.
2023 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2023). Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint. IJCAI International Joint Conference on Artificial Intelligence, 2023-August, 5558-5566.
Scopus4 -
Book Chapters
Year Citation 2023 Yan, X., Do, A. V., Shi, F., Qin, X., & Neumann, F. (2023). Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties. In Frontiers in Artificial Intelligence and Applications (Vol. 372, pp. 2826-2833). IOS Press.
DOI -
Conference Papers
Year Citation 2023 Yan, X., Do, A. V., Shi, F., Qin, X., & Neumann, F. (2023). Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties.. In K. Gal, A. Nowé, G. J. Nalepa, R. Fairstein, & R. Radulescu (Eds.), ECAI Vol. 372 (pp. 2826-2833). Online: IOS Press. 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.), NeurIPS Vol. 36 (pp. 15 pages). Online: Neural information processing systems foundation.
Scopus12023 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 2023 IEEE Congress on Evolutionary Computation, CEC 2023 Vol. 277 (pp. 1-9). Online: IEEE.
DOI2022 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 Scopus22022 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. 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 Scopus24 WoS202020 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 Scopus6 -
Preprint
Year Citation 2024 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2024). Evolutionary Multi-Objective Diversity Optimization. 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..
Connect With Me
External Profiles
