Fangfang Zhang

Fangfang Zhang

Lecturer, Victoria University of Wellington

Call for Papers

Special Session on Evolutionary Machine Learning for Combinatorial Optimisation

EAI BICT 2022 - 14th EAI International Conference on Bio-inspired Information and Communications Technologies

1-2 August 2022, Chengdu, China

Aim and Scope:

Combinatorial optimisation, such as scheduling and resource allocation for cloud/grid/high-performance computing, has attracted attention from both academics and the industry due to its practical value. Evolutionary machine learning, e.g., evolutionary computation techniques, has been widely used to handle combinatorial optimisation problems. Instead of manually designing heuristics for a specific problem instance, evolutionary machine learning has also been successfully used as hyper-heuristics approaches to select or generate heuristics for a class of problem instances, especially in dynamic problems, such as learning scheduling heuristics for dynamic job shop scheduling.

This special session focuses on both practical and theoretical aspects of evolutionary machine learning approaches to combinatorial optimisation for different kinds of application tasks. Novel approaches that use machine learning techniques for solving different combinatorial optimisation problems are highly encouraged. Examples of machine learning techniques include surrogate, feature selection, and multitask learning. This special session focuses on using evolutionary machine learning techniques to learn heuristics/solutions for the combinatorial optimisation problems.

We welcome the submissions of high-quality papers that effectively use evolutionary machine learning to handle the combinatorial optimisation problems. Papers with analyses and innovative algorithm designs to handle challenging issues in combinatorial optimisation problems are also highly encouraged.

Topics of interest include, but not limited to:

Application tasks:

  1. Production scheduling including job shop scheduling, open shop scheduling, flow shop scheduling
  2. Project scheduling
  3. Tour scheduling
  4. Crane scheduling
  5. Task scheduling
  6. Surgical scheduling
  7. Resource allocation in cloud computing
  8. Robot path planning
  9. Berth planning
  10. Electricity distribution planning
  11. Timetabling
  12. Designing water distribution networks
  13. Supply chain optimization
  14. Travelling salesman problem
  15. Knapsack problem
  16. Other combinatorial optimisation problems

Evolutionary machine learning:

  1. Genetic programming
  2. Evolutionary reinforcement learning
  3. Neural network
  4. Hyper-heuristics learning, including heuristic generation and heuristic selection
  5. Genetic operators of evolutionary machine learning approaches
  6. Adaptive machine learning approaches
  7. Surrogate-Assisted evolutionary machine learning approaches
  8. Feature selection in scheduling
  9. Transfer learning in scheduling
  10. Multitask scheduling
  11. Local search in scheduling
  12. Hybridisation of evolutionary algorithms with other machine learning and optimisation techniques for scheduling
  13. Other evolutionary machine learning approaches such as artificial immune systems, learning classifier system, particle swarm optimisation, differential evolution, etc.

Submission Guideline:

Please follow the submission guideline from the EAI BICT 2022 Submission Website. Please specify that your paper is for the Special Session on “Evolutionary Machine Learning for Combinatorial Optimisation”. Note that only papers longer than 6 pages can be included in the conference proceedings.

Important Dates:

  1. 11 April 2022: Paper Submission Deadline
  2. 23 May 2022: Paper Acceptance Notification
  3. 20 June 2022: Camera-ready deadline
  4. 1-2 August 2022: Conference Date

Organisers:

Dr. Fangfang Zhang, fangfang.zhang@ecs.vuw.ac.nz, Victoria Univeristy of Wellington
Prof. Mengjie Zhang, mengjie.zhang@ecs.vuw.ac.nz, Victoria Univeristy of Wellington

Biography of the Organizers:

Dr. Fangfang Zhang is currently a Postdoctoral Research Fellow in the School of Engineering and Computer Science at Victoria University of Wellington, New Zealand. She has 33 papers published in fully refereed international journals and conferences. She also published one authored book with Springer on Machine Learning: Foundations, Methodologies, and Applications. Her current research interests include evolutionary computation, hyper-heuristics learning/optimisation, job shop scheduling, and multitask learning. She is currently the Vice Chair of IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation. Dr. Zhang has organised several special sessions, symposiums, and tutorials in international conferences such as IEEE WCCI/CEC and IEEE SSCI. She has been serving as a reviewer for top international evolutionary computation journals such as the IEEE Transactions on Evolutionary Computation, and the IEEE Transactions on Cybernetics.

Prof. Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He has been serving as an associated editor or editorial board member for over 10 international journals including IEEE Transactions on Evolutionary Computation, and IEEE Transactions on Cybernetics He is the Tutorial Chair for GECCO 2014, an AIS-BIO Track Chair for GECCO 2016, an EML Track Chair for GECCO 2017, and a GP Track Chair for GECCO 2020. Since 2012, he has been co-chairing several parts of IEEE CEC, SSCI, and EvoIASP/EvoApplications conference (he has been involving major EC conferences such as GECCO, CEC, EvoStar, SEAL). He has been co-organising and co-chairing many special sessions, and also delivered a keynote/plenary talk for IEEE CEC 2018, IEEE ICAVSS 2018, DOCSA 2019, IES 2017 and Chinese National Conference on AI in Law 2017.