Evolutionary scheduling and combinatorial optimization (ESCO) is an important research area at the interface of artificial intelligence (AI) and operations research (OR). ESCO has attracted the attentions of researchers over the years due to its applicability and interesting computational aspects. Evolutionary Computation (EC) techniques are suitable for these problems since they are highly flexible regarding handling constraints, dynamic changes, and multiple conflicting objectives. With the growth of new technologies and business models, researchers in this field have to continuously face new challenges, which required innovated solution methods.
Scope and Topics
This special session focuses on both practical and theoretical aspects of Evolutionary Scheduling and Combinatorial Optimization. Examples of evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimization, particle swarm optimization, evolutionary based hyper-heuristics, memetic algorithms. Novel hybrid approaches that combine machine learning and evolutionary computation to solve difficult ESCO problems are highly encouraged. Examples include using machine learning to improve surrogate-assisted evolutionary algorithms, and designing evolutionary algorithms for reinforcement learning and transfer learning.
We welcome the submissions of quality papers that effectively use the power of EC techniques to solve hard and practical scheduling and combinatorial optimization problems. Papers with rigorous analyses of EC techniques and innovative solutions to handle challenging issues in scheduling and combinatorial optimisation problems are also highly encouraged.
Topics of interest include, but not limited to:
- Evolutionary learning for combinatorial optimisation
- Production scheduling
- Vehicle routing
- Project scheduling
- Transport scheduling
- Airport runway scheduling
- Grid/cloud scheduling
- Evolutionary scheduling with big data
- Web service composition
- SDN scheduling
- 2D/3D strip packing
- Resource allocation
- Multi-objective scheduling
- Complex combinatorial optimization with interdependent components
- Automated heuristic design for combinatorial optimization
- Dynamic combinatorial optimization
- Innovative applications of evolutionary scheduling and combinatorial optimization
Please follow the submission guideline from the IEEE CEC 2021 Submission Website. Special session papers are treated the same as regular conference papers. Please specify that your paper is for the Special Session on Evolutionary Scheduling and Combinatorial Optimisation. All papers accepted and presented at CEC 2021 will be included in the conference proceedings published by IEEE Explore, which are typically indexed by EI.
- 31 Jan 2021: Paper Submission Deadline
- 22 Mar 2021: Paper Acceptance Notification
- 7 Apr 2021: Final Paper Submission & Early Registration Deadline
- 28 June 2021 - 1 July 2021: Conference Date
Special Session Organizers
Dr. Su Nguyen, La Trobe University, Australia (firstname.lastname@example.org)
Dr. Su Nguyen is a Senior Research Fellow and Algorithm Lead at CDAC, La Trobe University, Australia. His expertise includes evolutionary computation (EC), simulation optimization, automated algorithm design, interfaces of AI/OR, and their applications in logistics, energy, and transportation. He has 70+ publications in top EC/OR peer-reviewed journals and conferences and his current research focuses on novel people-centric artificial intelligence to solve dynamic and uncertain planning tasks by combining the creativity of evolutionary computation and power of advanced machine learning algorithms. 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. He delivered technical tutorials about EC and AI-based visualisation at Parallel Problem Solving from Nature Conference (2018) and IEEE World Congress on Computational Intelligence (2020). He served as an editorial member of Complex and Intelligence Systems and the guest editor of the special issue on “Automated Design and Adaption of Heuristics for Scheduling and Combinatorial Optimization” in Genetic Programming and Evolvable Machines journal.
Dr. Yi Mei, Victoria University of Wellington, New Zealand (email@example.com)
Dr. Yi Mei (M’09-SM’18) is a Senior Lecturer at the School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand. He received his BSc and PhD degrees from University of Science and Technology of China in 2005 and 2010, respectively. His research interests include evolutionary computation in scheduling, routing and combinatorial optimisation, as well as evolutionary machine learning, hyper-heuristics, genetic programming, feature selection and dimensional reduction.
Yi has more than 100 fully referred publications, including the top journals in EC and Operations Research (OR) such as IEEE TEVC, IEEE Transactions on Cybernetics, European Journal of Operational Research, ACM Transactions on Mathematical Software, and top EC conferences (GECCO). He won an IEEE Transactions on Evolutionary Computation Outstanding Paper Award 2017, and a Victoria University of Wellington Early Research Excellence Award 2018. As the sole investigator, he won the 2nd prize of the Competition at IEEE WCCI 2014: Optimisation of Problems with Multiple Interdependent Components. He serves as a Vice-Chair of the IEEE CIS Emergent Technologies Technical Committee, and a member of three IEEE CIS Task Forces and two IEEE CIS Technical Committees. He is an Editorial Board Member of International Journal of Bio-Inspired Computation, an Associate Editor of International Journal of Applied Evolutionary Computation and International Journal of Automation and Control, and a guest editor of a special issue of the Genetic Programming Evolvable Machine journal. He has organised a number of special sessions in international conferences such as IEEE CEC. He serves as a reviewer of over 30 international journals including the top journals in EC and OR. He was an Outstanding Reviewer for Applied Soft Computing in 2015 and 2017, and IEEE Transactions on Cybernetics in 2018.
Dr. Gang (Aaron) Chen, Victoria University of Wellington, New Zealand (firstname.lastname@example.org)
Dr Gang (Aaron) Chen is currently a senior lecturer in the School of Engineering and Computer Science at Victoria University of Wellington (VUW). He is also co-leading the strategic research direction on Evolutionary Scheduling and Combinatorial Optimization of the Evolutionary Computation Research Group at VUW. In the past, he worked as lecturer at Unitec Institute of Technology in New Zealand from 2010 to 2013 and visiting assistant professor in the School of Electrical and Electronic Engineering at Nanyang Technological University in Singapore from 2007 to 2010.
His primary research interests include reinforcement learning and learning classifier systems, evolutionary computation for job shop scheduling and combinatorial optimization, and multi-agent and peer-to-peer systems. His works have been published in top peer-reviewed journals, including IEEE and ACM Transactions, in the area of machine learning, evolutionary computation, and distributed computing. Gang Chen is currently a member of IEEE and IEEE Computational Intelligence Society. He served as the guest editor of a special issue on “Evolutionary Optimisation, Feature Reduction and Learning” in the Soft Computing journal. In the recent years he was involved in the technical committees of various conferences in the area of artificial intelligence and evolutionary computation. He is also the reviewer of high-quality journals in the research field of evolutionary computation, machine learning, and distributed computing.