Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is an Associate Professor at the School of Computer Science and Information Technology, RMIT University, Melbourne, Australia. His research interests include evolutionary computation, machine learning, neural networks, complex systems, multiobjective optimization, data analytics, and swarm intelligence. He serves as an Associate Editor for the journal of IEEE Transactions on Evolutionary Computation, the journal of Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He has received several ARC grants in the past 5 years (Discovery and Linkage), He is the recipient of 2013 SIGEVO Impact Award. Further information can be found from his personal website.
- PhD in Artificial Intelligence, 1998
- Dip.Com in Information Science, 1992
- B.Sc. in Information Science, 1988
- Senior Member, Institute of Electrical and Electronics Engineers (IEEE).
- Member, IEEE Computational Intelligence Society.Vice Chair, Computational Intelligence Chapter, IEEE Victorian Section, Melbourne, Australia
- Member, Steering committee of Simulated Evolution And Learning (SEAL).
- Sun, Y.,Wang, S.,Shen, Y.,Li, X.,Ernst, A.,Kirley, M. (2022). Boosting ant colony optimization via solution prediction and machine learning In: Computers and Operations Research, 143, 1 - 16
- Weiner, J.,Ernst, A.,Li, X.,Sun, Y.,Deb, K. (2021). Solving the maximum edge disjoint path problem using a modified Lagrangian particle swarm optimisation hybrid In: European Journal of Operational Research, 293, 847 - 862
- Preuss, M.,Epitropakis, M.,Li, X.,Fieldsend, J. (2021). Multimodal Optimization: Formulation, Heuristics, and a Decade of Advances In: Metaheuristics for Finding Multiple Solutions, Springer, Switzerland
- Miessen, A.,Najman, J.,Li, X. (2021). Finding Representative Solutions in Multimodal Optimization for Enhanced Decision-Making In: Metaheuristics for Finding Multiple Solutions, Springer, Switzerland
- Sun, Y.,Ernst, A.,Li, X.,Weiner, J. (2021). Generalization of machine learning for problem reduction: a case study on travelling salesman problems In: OR Spectrum, 43, 607 - 633
- Sun, Y.,Li, X.,Ernst, A. (2021). Using statistical measures and machine learning for graph reduction to solve maximum weight clique problems In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 1746 - 1760
- Li, X.,Ma, X.,Zheng, Y.,Zhu, Z.,Wang, L.,Qi, Y.,Yang, J. (2021). Improving Evolutionary Multitasking Optimization by Leveraging Inter-Task Gene Similarity and Mirror Transformation In: IEEE Computational Intelligence Magazine, 16, 38 - 53
- Liu, D.,Qi, Y.,Yang, R.,Quan, Y.,Li, X.,Miao, Q. (2021). A tri-objective preference-based uniform weight design method using Delaunay triangulation In: Soft Computing, 25, 9703 - 9729
- Taylor, K.,Ha, H.,Li, M.,Chan, J.,Li, X. (2021). Bayesian Preference Learning for Interactive Multi-objective Optimisation In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France, 10 -14 July 2021
- Wu, J.,Li, X.,Lin, Y.,Yan, Y.,Tu, J. (2020). A PMV-based HVAC control strategy for office rooms subjected to solar radiation In: Building and Environment, 177, 1 - 10
- Machine learning techniques for fuel loss detection at service stations. Funded by: ARC Linkage Project Grants 2019 from (2021 to 2024)
- Research Challenge Topic – 23: Deep reasoning reinforcement learning for cognitive information warfare - AI for Decision Making (Administered by University of Melbourne). Funded by: Defence Science and Technology Group (scheme) - competitive from (2020 to 2021)
- A Novel and Efficient Approach for Optimization involving Iterative Solvers (administered by UNSW). Funded by: ARC Discovery Project via Other University from (2019 to 2022)
- Hybrid methods with decomposition for large scale optimization. Funded by: ARC Discovery Projects 2018 from (2018 to 2021)
- Optimisation and machine learning for wetstock management. Funded by: DIIS - Innovations Connections - Competitive from (2018 to 2019)
Note: Supervision projects since 2004
18 PhD Completions8 PhD Current Supervisions and 1 Masters by Research Current Supervisions
Artificial intelligence; learning algorithms; neural networks; connectionist learning models; evolutionary computation; genetic algorithms; parallel GA; genetic programming; artificial life; complex systems; adaptive systems; emergent behaviours; cellular automata; multi-agent simulation; intelligent agents; swarm intelligence; ant colony algorithms; percolation; self-organized criticality; phase transition.