Research

Design heuristics for complex real-world optimisation problems using genetic programming hyper-heuristics.

Design effective evolutionary algorithms for scheduling and combinatorial optimisation.

Design effective divide-and-conquer algorithms to solve large scale global optimisation.

Grants & Awards

Grants

  • 2020-2027, “A data-science driven evolution of aquaculture for building the blue economy (AI/ML Advanced Research and Applications to Aquaculture)”. MBIE SSIF Fund on Data Science, $13,000,000 NZD. (Key Researcher)
  • 2019-2020, “AI/ML techniques for Waste Collection in NZ”, industrial project with Northland Waste, $16,000 NZD. (PI)
  • 2017-2020, “Automatic Design of Heuristics for Dynamic Arc Routing Problem with Genetic Programming”, 16-VUW-079, Marsden Fund Fast-Start Grant, $300,000 NZD. (Sole PI)
  • 2017-2020, “Cooperative Co-evolution for Large Scale Black Box Optimisation”, 61673194, National Natural Science Foundation of China, ¥610,000 RMB (Overseas AI)
  • 2018, “Real-Time Tourist Trip Recommendation using Genetic Programming”, University Research Fund, Victoria University of Wellington, $28,720 NZD (Sole PI)
  • 2016-2018, “Digital Data in Schools: An Exploration of Research and Practice”, Victoria University of Wellington Digital Future Grant, $20,000 NZD (Co-PI)
  • 2017, “Evolving Interpretable Flexible Job Shop Scheduling Rules with GP”, Research Establishment Grant, Victoria University of Wellington, $10,000 NZD (Sole PI)
  • 2014, RMIT Near-miss grant ($25,000 AUD awarded for being ranked top 10% of the unsuccessful applications for the 2014 ARC DECRA funding)
  • 2009, IEEE CIS Walter Karplus Summer Research Grant

Awards

  • 2018, Victoria University of Wellington Early Research Excellence Award
  • 2018, Australasian Joint Conference on Artificial Intelligence Best Paper Runner-Up Award (paper)
  • 2018, International Conference on Web Services (ARC/CORE Rank A) Best Paper Runner-Up (paper)
  • 2017, IEEE Transactions on Evolutionary Computation (top journal in EC, IF = 10.629) Outstanding Paper Award (paper)
  • 2016, European Conference on Evolutionary Computation in Combinatorial Optimization Best Paper Nomination (paper)
  • 2014, 2nd Prize, Competition at IEEE World Congress on Computational Intelligence: Optimisation of Problems with Multiple Interdependent Components (as sole algorithm designer and programmer)
  • 2010, Chinese Academy Of Sciences Dean’s Award (Top 200 postgraduates all over China)

Supervision

I am a supervisor of 21 PhD students and 6 Master students, as well as a number of honours and summer research students (full list).

Professional Services

Editorship

  • Associate Editor of International Journal of Applied Evolutionary Computation
  • Editorial Board Member of International Journal of Computational Intelligence and Applications
  • Editorial Board Member of International Journal of Bio-Inspired Computation
  • Editorial Board Member of International Journal of Automation and Control
  • Guest Editor of Special Issue on Automated Design and Adaptation of Heuristics for Scheduling and Combinatorial Optimisation, Genetic Programming and Evolvable Machines, 2016

Conference Organisation

  • Finance Chair, Conference on Image and Vision Computing New Zealand (IVCNZ) 2020
  • Proceedings Chair, IEEE Congress on Evolutionary Computation (CEC) 2019
  • Tutorial Co-chair, Pacific Rim International Conferences on Artificial Intelligence (PRICAI) 2019
  • Sponsorship Chair, Australasian Joint Conference on Artificial Intelligence 2018
  • Technical Co-chair, International Conference on Data Intelligence and Security (ICDIS) 2018
  • Organisational Committee Member, International Conference on Computers and Industrial Engineering (CIE) 2018
  • Co-Chair, IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation, in IEEE SSCI 2019, 2020, 2021
  • Co-chair of Special Session on Evolutionary Scheduling and Combinatorial Optimisation, IEEE Congress on Evolutionary Computation (CEC) 2016, 2017, 2018, 2019, 2020, 2021
  • Co-chair of Special Session on Evolutionary Computation for Service-Oriented Computing, IEEE Congress on Evolutionary Computation (CEC) 2017, 2018, 2019
  • Co-chair of Special Session on Transfer Learning in Evolutionary Computation, IEEE Congress on Evolutionary Computation (CEC) 2016
  • Co-chair of Special Session on Computational Intelligence for Scheduling and Combinatorial Optimization, Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES) 2016

Conference Program Committee Member

I serve as a PC member of 40+ international conferences (full list).

Journal Reviewer

I serve as a reviewer of 40+ international journals, including the top/major journals in the EC/OR fields (full list).

Membership

Internal (VUW)

  • Red-Folder Committee Member (Admission for Masters of COMP/AI/CGRA, PGDipSc COMP/AI/CGRA)
  • ENGR/COMP489 (Honours Project) Committee Member, 2019, 2020

Useful Resources

Code

Datasets

  • Job Shop Scheduling
  • Flexible Job Shop Scheduling
  • Capacitated Arc Routing Problem
    • The gdb dataset: 23 small instances (~30 nodes and ~60 required edges).
    • The val dataset: 10 groups of medium to large instances (~50 nodes and ~100 required edges). Each group contains 3 or 4 instances (denoted as A, B, C, D), which are based on the same graph but different vehicle capacity.
    • The egl dataset: 8 groups of large instances (~150 nodes and ~200 required edges). The former 4 groups (e1 to e4) and the latter 4 groups (s1 to s4) are based on the same graph, but different subsets of required edges. Each group contains 3 instances (denoted as A, B, C), based on the same graph and required edges, but with different vehicle capacity.
    • The EGL-G dataset: 2 groups of large instances (~250 nodes and ~400 required edges). Each group contains 5 instances (denoted as A, B, C, D, E), based on the same graph and required edges, but with different vehicle capacity.
    • The Beijing&Hefei dataset: 2 large datasets, one generated from the road network of Bejing, and the other from Hefei, two big cities in China. Each dataset has 10 instances, with thousands of edges.
    • Large scale datasets, and the best solutions we found.
    • Multi-Depot CARP datasets.
  • Vehicle Routing Problems
  • Bin Packing Problem
  • Timetabling Problem

Contact