Call for Papers
Special Session on Genetic Programming and Machine Learning for Scheduling
CEC 2023 - IEEE Congress on Evolutionary Computation
1 to 5 July 2023, Swissôtel Chicago, USA
Aim and Scope:
Scheduling is an important optimisation problem that reflects the practical and challenging issues in real-world scheduling applications such as order picking in warehouses, the manufacturing industry and grid/cloud computing. Genetic programming, as a hyper-heuristic approach, has been successfully and widely used to learn scheduling heuristics for the scheduling problems. Learning scheduling heuristics with genetic programming has attracted the attention of researchers over the years due to its flexible representation. Other machine learning approaches such as reinforcement learning has also been widely used for scheduling problems. A number of machine learning techniques such as surrogate, feature selection, and multitask learning can be used to improve the quality of learned solutions/heuristics for scheduling. With the growth of new technologies, researchers in this field have to continuously face new challenges, which requires innovative approaches for scheduling.
This special session focuses on both practical and theoretical aspects of genetic programming and machine learning approaches for scheduling. Fundamental theoretical based approaches about genetic operators such as crossover, mutation are welcome. Novel approaches that use machine learning techniques for solving difficult scheduling problems are highly encouraged. Examples of machine learning techniques include surrogate, feature selection, and multitask learning. This special session is related to the IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation (IEEE ESCO), which involves a wide range of evolutionary algorithms for different combinatorial optimisation problems. However, this special session focuses on using genetic programming and machine learning techniques to learn scheduling heuristics/solutions for the scheduling problems.
We welcome the submissions of quality papers that effectively use genetic programming to solve the scheduling problems. Papers with rigorous analyses of genetic programming, machine learning techniques and innovative solutions to handle challenging issues in scheduling problems are also highly encouraged.
Topics of interest include, but not limited to:
- Production scheduling including job shop scheduling, open shop scheduling, flow shop scheduling
- Project scheduling
- Tour scheduling
- Crane scheduling
- Task scheduling
- Surgical scheduling
- Resource allocation in cloud computing
- Robot scheduling
- Berth scheduling
- Electricity distribution scheduling
- Designing water distribution networks
- Supply chain optimization
- Travelling salesman problem
- Knapsack problem
- Other scheduling problems
Evolutionary machine learning:
- Genetic programming
- Evolutionary reinforcement learning
- Neural network
- Hyper-heuristics learning, including heuristic generation and heuristic selection
- Genetic operators of evolutionary machine learning approaches
- Adaptive machine learning approaches
- Surrogate-Assisted evolutionary machine learning approaches
- Feature selection in scheduling
- Transfer learning in scheduling
- Multitask scheduling
- Local search in scheduling
- Hybridisation of evolutionary algorithms with other machine learning and optimisation techniques for scheduling
- Other evolutionary machine learning approaches such as artificial immune systems, learning classifier system, particle swarm optimisation, differential evolution, etc.
Please follow the submission guideline from the CEC 2023 Submission Website. Please specify that your paper is for the Special Session on “Genetic Programming and Machine Learning for Scheduling”. Special session papers are treated the same as regular conference papers.
- Paper Submission: Friday, January 27th, 2023
- Paper Re-submissions: March 17th, 2023
- Paper Final Notifications: April 14th, 2023
- Print-Ready Manuscripts: April 29th, 2023
Dr. Fangfang Zhang, firstname.lastname@example.org, Victoria Univeristy of Wellington
Prof. Mengjie Zhang, email@example.com, Victoria Univeristy of Wellington
Prof. Yi Mei, firstname.lastname@example.org, Victoria Univeristy of Wellington
Dr. Nguyen Su, P.Nguyen4@latrobe.edu.au, La Trobe University
Biography of the Organizers:
Dr. Fangfang Zhang is a Postdoctoral Research Fellow in the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. She has over 45 journal and conference papers including one authored book. Her current research interests include evolutionary computation, hyper-heuristics learning/optimisation, job shop scheduling, and multitask learning. She is the chairs of the special sessions on “Evolutionary Machine Learning for Planning and Scheduling” and “Evolutionary Scheduling and Combinatorial Optimisation” at CEC 2022, “Genetic Programming and Machine Learning for Scheduling” at SSCI 2021 and SSCI 2022, and “Evolutionary Scheduling and Combinatorial Optimisation” (BICT 2022). She is also the speaker of the tutorials of “Evolutionary Machine Learning for Combinatorial Optimisation” at CEC 2022, “Genetic Programming for Job Shop Scheduling” at SSCI 2021, and “Genetic Programming and Machine Learning for Job Shop Scheduling” at SSCI 2022. Dr Fangfang is an associate editor of Expert Systems With Applications. She is a member of the IEEE Computational Intelligence Society and Association for Computing Machinery, and 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. She is also a committee member of the IEEE New Zealand Central Section.
Prof. Mengjie Zhang is Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. His current research interests include artificial intelligence and machine learning, particularly genetic programming, job shop scheduling, and transfer learning. He has published over 700 research papers in refereed international journals and conferences. 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. Prof. Zhang is a Fellow of the Royal Society of New Zealand, Fellow of Engineering New Zealand, a Fellow of IEEE and an IEEE Distinguished Lecturer. He was the Chair of the IEEE CIS Intelligent Systems and Applications Technical Committee, the IEEE CIS Emergent Technologies Technical Committee, and the Evolutionary Computation Technical Committee, and a member of the IEEE CIS Award Committee. He is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and an IEEE Distinguished Lecturer.
Dr. Yi Mei is an Associate Professor with the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand. He has more than 100 fully referred publications, including the top journals in Evolutionary Computation and Operations Research, such as IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His research interests include evolutionary scheduling and combinatorial optimization, machine learning, genetic programming, and hyper-heuristics. Dr. Mei was a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee and a member of the Intelligent Systems Applications Technical Committee. He is an Editorial Board Member/Associate Editor of three international journals, and a Guest Editor of a special issue of the Genetic Programming and Evolvable Machines journal. He serves as a reviewer of over 30 international journals.
Dr. Su Nguyen is a Senior Lecturer (Business Analytics and AI) and Algorithm Lead at the Centre for Data Analytics and Cognition (CDAC), La Trobe University, Australia. His expertise includes simulation-optimization, evolutionary computation, automated algorithm design, interfaces of artificial intelligence and operations research, and their applications in logistics, energy, and transportation. Nguyen has a strong track record in developing simulation models, simulation-based decision support tools, and simulation-optimisation algorithms for industry applications. He has 70+ publications in top peer-reviewed journals and conferences in computational intelligence and operations research. His current research focuses on hybrid intelligence systems that combine the power of modern artificial intelligence technologies and operations research methodologies. He was the chair (2014-2018) of IEEE task force on Evolutionary Scheduling and Combinatorial Optimisation and is a member of IEEE CIS Data Mining and Big Data technical committee. Dr. Nguyen was the Chair of IEEE Task Force on Evolutionary Scheduling and Combinatorial Optimisation from 2014 to 2018 and is a member of the IEEE CIS Data Mining and Big Data Technical Committee. He delivered technical tutorials about evolutionary computation and artificial intelligence based visualization at Parallel Problem Solving from Nature Conference in 2018 and IEEE World Congress on Computational Intelligence in 2020.