STAFF PROFILE
Professor Xiaodong Li
Position:
Professor
College / Portfolio:
School of Science Cluster
School / Department:
Computer Science and Software Engineering
Phone:
+61 3 9925 9585
Email:
xiaodong.li@rmit.edu.au
Campus:
Melbourne City Campus
Contact me about:
Research supervision
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).
- Xie, J.,Mei, Y.,Ernst, A.,Li, X.,Song, A. (2018). (In Press) A bi-level optimization model for grouping constrained storage location assignment problems In: IEEE Transactions on Cybernetics, , 1 - 14
- Tamassia, M.,Zambetta, F.,Raffe, W.,Mueller, F.,Li, X. (2018). Learning options from demonstrations: A Pac-Man case study In: IEEE Transactions on Computational Intelligence and AI in Games, 10, 91 - 96
- Demediuk, S.,Tamassia, M.,Raffe, W.,Zambetta, F.,Mueller, F.,Li, X. (2018). Measuring player skill using dynamic difficulty adjustment In: Proceedings of the Australasian Computer Science Week Multiconference ACSW '18, Brisbane, 29 January 29 - 2 February 2018
- Qi, Y.,Li, X.,Yu, J.,Miao, Q. (2018). In Press - User-preference based decomposition in MOEA/D without using an ideal point In: Swarm and Evolutionary Computation, , 1 - 1
- Haqqani, M.,Li, X.,Yu, X. (2018). Multi-objective Journey Planning under Uncertainty: A Genetic Approach In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan, 15 - 19 July 2018
- Taylor, K.,Li, X. (2018). Interactive Multiobjective Optimisation: Preference Changes and Algorithm Responsiveness In: Proceedings of the 2018 Conference on Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan, 15 - 19 July 2018
- Yang, M.,Omidvar, M.,Li, C.,Li, X.,Cai, Z.,Kazimipour, B.,Yao, X. (2017). Efficient resource allocation in cooperative co-evolution for large-scale global optimization In: IEEE Transactions on Evolutionary Computation, 21, 493 - 505
- Li, X.,Epitropakis, M.,Deb, K.,Engelbrecht, A. (2017). Seeking multiple solutions: an updated survey on niching methods and their applications In: IEEE Transactions on Evolutionary Computation, 21, 518 - 538
- Omidvar, M.,Yang, M.,Mei, Y.,Li, X.,Yao, X. (2017). DG2: a faster and more accurate differential grouping for large-scale black-box optimization In: IEEE Transactions on Evolutionary Computation, 21, 929 - 942
- Demediuk, S.,Tamassia, M.,Raffe, W.,Zambetta, F.,Li, X.,Mueller, F. (2017). Monte Carlo tree search based algorithms for dynamic difficulty adjustment In: Proceedings of the IEEE Conference on Computational Intelligence and Games 2017 (CIG), New York, United States, 22-25 August 2017
- Hybrid methods with decomposition for large scale optimization. Funded by: ARC Discovery Projects 2018 from (2018 to 2020)
- Optimisation and machine learning for wetstock management. Funded by: Environmental Monitoring Solutions Pty Ltd from (2018 to 2019)
- An integrated and real-time passenger travel and public transport service information system. Funded by: ARC Linkage Grant pre-2014 Round 2 from (2013 to 2018)
- Enhancing the Australian theme park experience by harnessing virtual-physical play. Funded by: ARC Linkage Grant 2013 from (2013 to 2017)
- Developing an Integrated Optimization Platform for Innovative Design of Composite Fabrication Process. Funded by: ARC Linkage Grant 2013 from (2013 to 2019)
Note: Supervision projects since 2004
12 PhD Current Supervisions13 PhD Completions
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.