Yue Xie

Yue Xie

School of Computer Science

Faculty of Sciences, Engineering and Technology

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


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 

 

 

  • 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.
    DOI
    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.
    DOI Scopus3 WoS1
    2018 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.
    DOI Scopus17 WoS11
    2017 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.
    DOI Scopus25 WoS13
    2016 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.
    DOI 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.
    DOI
    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.
    DOI
    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.
    DOI Scopus5 WoS1
    2021 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.
    DOI Scopus2
    2021 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.
    DOI Scopus9 WoS4
    2020 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.
    DOI Scopus8 WoS4
    2019 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.
    DOI Scopus14 WoS6
    2017 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.
    DOI Scopus1
    2016 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.
    DOI Scopus3
    2016 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.
    DOI 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

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