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).
- Ghasemishabankareh, B.,Ozlen, M.,Li, X.,Deb, K. (2020). A genetic algorithm with local search for solving single-source single-sink nonlinear non-convex minimum cost flow problems In: Soft Computing, 24, 1153 - 1169
- Zambetta, F.,Raffe, W.,Tamassia, M.,Mueller, F.,Li, X.,Quinten, N.,Dang, D.,Satterley, J. (2020). (In Press) Reducing Perceived Waiting Time in Theme Park Queues via an Augmented Reality Game In: ACM Transactions on Computer-Human Interaction, 1, 1 - 31
- Ghasemishabankareh, B.,Li, X.,Ozlen, M.,Neumann, F. (2020). Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions In: Applied Soft Computing Journal, 86, 1 - 14
- Ma, X.,Li, X.,Zhang, Q.,Tang, K.,Liang, Z.,Xie, W.,Zhu, Z. (2019). A Survey on Cooperative Co-evolutionary Algorithms In: IEEE Transactions on Evolutionary Computation, 16, 37 - 46
- Qi, Y.,Li, X.,Yu, J.,Miao, Q. (2019). User-preference based decomposition in MOEA/D without using an ideal point In: Swarm and Evolutionary Computation, 44, 597 - 611
- Yue, C.,Qu, B.,Yu, K.,Liang, J.,Li, X. (2019). A novel scalable test problem suite for multimodal multiobjective optimization In: Swarm and Evolutionary Computation, 48, 62 - 71
- Qi, Y.,Liu, D.,Li, X.,Lei, J.,Xu, X.,Miao, Q. (2019). An adaptive penalty-based boundary intersection method for many-objective optimization problem In: Information Sciences, 509, 356 - 375
- Li, X. (2019). Iterated feature selection algorithms with layered recurrent neural network for software fault prediction In: Expert Systems with Applications, 122, 27 - 42
- Ghasemishabankareh, B.,Ozlen, M.,Li, X. (2019). NSGA-II for Solving Multiobjective Integer Minimum Cost Flow Problem with Probabilistic Tree-Based Representation In: In: Deb K. et al. (eds) Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science, vol 11411., East Lansing, Michigan, USA, March 10-13, 2019
- Taylor, K.,Li, X.,Chan, J. (2019). Improving Algorithm Response to Preference Changes in Multiobjective Optimisation Using Archives In: IEEE Congress on Evolutionary Computation, Wellington, New Zealand, New Zealand, 10-13 June 2019
- 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: Environmental Monitoring Solutions Pty Ltd from (2018 to 2019)
- Enhancing the Australian theme park experience by harnessing virtual-physical play. Funded by: ARC Linkage Grant 2013 from (2014 to 2017)
- Developing an Integrated Optimization Platform for Innovative Design of Composite Fabrication Process. Funded by: ARC Linkage Grant 2013 from (2014 to 2019)
Note: Supervision projects since 2004
15 PhD Completions6 PhD 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.