
Yue Xie
School of Computer Science
Faculty of Sciences, Engineering and Technology
Master degree in Beihang University in China
The PhD candidate in the University of Adelaide in Australia
Evolutionary Computation
Complexity of Algorithms
Combinatorial Optimization
Chance-constrained Optimization
-
Language Competencies
Language Competency Chinese (Mandarin) Can read, write, speak, understand spoken and peer review English Can read, write, speak, understand spoken and peer review -
Education
Date Institution name Country Title 2018 University of Adelaide Australia PhD 2015 - 2018 Beihang University China Research Master -
Research Interests
-
Journals
Year Citation 2022 Neumann, A., Xie, Y., & Neumann, F. (2022). Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13398 LNCS, 294-307.
2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints.. arXiv, abs/2102.05303, 1079-1087.
Scopus4 WoS12018 Xie, Y., Zhou, S., Xiao, Y., Kulturel-Konak, S., & Konak, A. (2018). A β -accurate linearization method of Euclidean distance for the facility layout problem with heterogeneous distance metrics. European Journal of Operational Research, 265(1), 26-38.
Scopus18 WoS122017 Xiao, Y., Xie, Y., Kulturel-Konak, S., & Konak, A. (2017). A problem evolution algorithm with linear programming for the dynamic facility layout problem—A general layout formulation. Computers & Operations Research, 88, 187-207.
Scopus26 WoS132016 Zhou, S., Hu, C., Xie, Y., & Chang, W. (2016). Research on supply chain risk assessment with intuitionistic fuzzy information. Journal of Intelligent & Fuzzy Systems, 30(6), 3367-3372.
Scopus5 WoS5 -
Conference Papers
Year Citation 2022 Xie, Y., Neumann, A., & Neumann, F. (2022). An optimization strategy for the complex large-scale stockpile blending problem. In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference (pp. 770-773). ACM.
2022 Xie, Y., Neumann, A., Stanford, T., Rasmussen, C. L., Dumuid, D., & Neumann, F. (2022). Evolutionary Time-Use Optimization for Improving Children’s Health Outcomes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13399 LNCS (pp. 323-337). Springer International Publishing.
2021 Xie, Y., Neumann, A., Neumann, F., & Sutton, A. M. (2021). Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights.. In Proceedings of Genetic and Evolutionary Computation Conference (GECCO'21) Vol. abs/2102.05778 (pp. 1-8). online: ACM.
Scopus6 WoS12021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems.. In 2021 IEEE Congress on Evolutionary Computation (CEC) Vol. abs/2104.03440 (pp. 1288-1295). Piscataway, NJ 08854 United States: IEEE.
Scopus22021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic strategies for solving complex interacting stockpile blending problem with chance constraints.. In F. Chicano, & K. Krawiec (Eds.), Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 1079-1087). online: ACM. 2021 Xie, Y., Neumann, A., Neumann, F., & Sutton, A. M. (2021). Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights.. In F. Chicano, & K. Krawiec (Eds.), GECCO (pp. 1187-1194). ACM. 2021 Xie, Y., Neumann, A., & Neumann, F. (2021). Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems. In Proceedings of Genetic and Evolutionary Computation Conference (GECCO'21) (pp. 1-9). online: ACM. 2020 Assimi, H., Harper, O., Xie, Y., Neumann, A., & Neumann, F. (2020). Evolutionary bi-objective optimization for the dynamic chance-constrained knapsack problem based on tail bound objectives. In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), as published in Frontiers in Artificial Intelligence and Applications Vol. 325 (pp. 307-314). Amsterdam, Netherlands: IOS Press BV.
Scopus10 WoS42020 Xie, Y., Neumann, A., & Neumann, F. (2020). Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO'20) (pp. 271-279). New York: Association for Computing Machinery.
Scopus9 WoS42019 Xie, Y., Harper, O., Assimi, H., Neumann, A., & Neumann, F. (2019). Evolutionary algorithms for the chance-constrained knapsack problem. In GECCO '19: Proceedings of the 2019 Genetic and Evolutionary Computation Conference Vol. abs/1902.04767 (pp. 338-346). New York: ACM.
Scopus14 WoS62017 Hu, C., Zhou, S., Xie, Y., & Chang, W. (2017). The Markov forecasting model of landing states based on flight data. In L. Walls, M. Revie, & T. Bedford (Eds.), Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (pp. 2277-2281). Oxfordshire, England, UK: Routledge. 2016 Xie, Y., & Shenhan, Z. (2016). A new area linearization method for Unequal area facility layout problem. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) Vol. 2016-December (pp. 1289-1293). online: IEEE.
Scopus12016 Xie, Y., Zhou, S., Chang, W., & Zhao, J. (2016). An improved supplier selection model for equipment R&D project with independent fuzzy cost information. In IEEE (pp. 6502-6505). online: IEEE.
Scopus32016 Xie, Y. (2016). An efficient solution approach based on hierarchical decomposition for facility layout problem. In ISTP. Beijing. 2016 Hu, C., Zhou, S. H., Xie, Y., & Chang, W. B. (2016). The study on hard landing prediction model with optimized parameter SVM method. In Chinese Control Conference, CCC Vol. 2016-August (pp. 4283-4287). Piscataway, New Jersey, United States: IEEE.
Scopus12 WoS9 -
Internet Publications
Year Citation 2019 Xie, Y., Harper, O., Assimi, H., Neumann, A., & Neumann, F. (2019). Evolutionary algorithms for the chance-constrained knapsack problem..
2018: National Scholarship, Beihang University.
2018: Outstanding Graduates, Beihang University.
2016: Second prize, Academic Scholarship, Beihang University.
2015: Provincial best graduation thesis, Hubei University of Technology, Hubei Province. 2014: National scholarship, Hubei University of Technology.
Evolutionary computation
Algorithm & Data Structure Analysis
Working with Big Data
-
Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2022 Co-Supervisor Evolutionary Algorithms for Solving Chance Constrained Combinatorial Optimization Problems Doctor of Philosophy Doctorate Full Time Miss Saba Sadeghi Ahouei 2022 Co-Supervisor Is complexity an evolutionary response to selection change? Master of Philosophy Master Part Time Mr Gabor Zoltai
Connect With Me
External Profiles