IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation

IEEE Computational Intelligence Society

Evolutionary Computation Technical Committee

Welcome

Welcome to the website of the IEEE Taskforce on Evolutionary Scheduling and Combinatorial Optimisation, a taskforce under the Evolutionary Computation Technical Committee, IEEE Computational Intelligence Society. We are passionate to organise events and activities on evolutionary scheduling and combinatorial optimisation, to create opportunities for researchers and industrial practitioners to share ideas, seek for collaborations, and make friends together!

If you are interested in joining this taskforce or receiving updates from us, please feel free to contact Yi Mei.

NEWS

• [Best Paper Award]: A paper from our Taskforce "Grammar-guided Linear Genetic Programming for Dynamic Job Shop Scheduling" won a Best Paper Award of ACM Genetic and Evolutionary Computation Conference (GECCO) 2023.
• [Best PhD Dissertation Award]: Our Taskforce Member and Vice-Chair, Fangfang Zhang’s PhD thesis "Genetic Programming Hyper-heuristics for Dynamic Flexible Job Shop Scheduling" won the 2022 ACM SIGEVO Best PhD Dissertation Award (Honorable Mention/Runner-up).
• [Best Paper Award]: A paper from our Taskforce "Local ranking explanation for genetic programming evolved routing policies for uncertain capacitated Arc routing problems" won a Best Paper Award of ACM Genetic and Evolutionary Computation Conference (GECCO) 2022.
• [Best Paper Award]: A paper from our Taskforce "An Investigation of Multitask Linear Genetic Programming for Dynamic Job Shop Scheduling" won the Best Paper Award of European Conference on Genetic Programming (EuroGP) 2022.
• New book published: Fangfang Zhang, Su Nguyen, Yi Mei, Mengjie Zhang "Genetic Programming for Production Scheduling: An Evolutionary Learning Approach", Springer, 2021.

---

Go to TOP

---

Scope and Mission

Scheduling and combinatorial optimisation is an important research area at the interface of artificial intelligence and operations research. It has attracted the attention of researchers over the years due to its wide applicability to the real world and interesting computational aspects.

Evolutionary computation approaches have been successfully applied to solve these problems since they are highly flexible regarding handling constraints, dynamic environment changes, multiple conflicting objectives, and automatic algorithm design/configuration. With the growth of new technologies and business models, researchers in this field are continuously facing new challenges, which requires innovative solution methods.

This scope of this taskforce focuses on both practical and theoretical aspects of Evolutionary Scheduling and Combinatorial Optimisation (ESCO). Examples of evolutionary methods include genetic algorithms, genetic programming, evolutionary strategies, ant colony optimisation, particle swarm optimisation, evolutionary-based hyper-heuristics, and memetic algorithms. We also focus on novel hybrid approaches that combine machine learning and evolutionary computation to solve complex ESCO problems. Examples include using machine learning to improve surrogate-assisted evolutionary algorithms, and designing evolutionary algorithms for reinforcement learning and transfer/multitask learning.

The mission of this taskforce is to organise events and activities (e.g., conference workshops, tutorials, special sessions, competitions, journal special issues), to provide a platform for researchers and industrial practitioners to share ideas, seek for collaborations, and find solutions to their problems. It will serve at least the following groups of people:

• Evolutionary computation researchers who are interested in applying their algorithms to complex real-world scheduling and combinatorial optimisation applications.
• Operations research and combinatorial optimisation researchers who are interested in solving their problem using evolutionary computation techniques.
• Real-world practitioners who are interested in having their real-world problems to be solved effectively, and evolutionary computation is a powerful technique for this.

---

Go to TOP

---

Events and Activities

Tutorial: Explainable Artificial Intelligence by Genetic Programming

Yi Mei, Qi Chen, Andrew Lensen, Bing Xue, Mengjie Zhang
IEEE World Congress on Computational Intelligence, 30 June – 5 July 2024, Yokohama, Japan.

Tutorial: Genetic Programming and Machine Learning for Scheduling

Fangfang Zhang
IEEE World Congress on Computational Intelligence, 30 June – 5 July 2024, Yokohama, Japan.

Special Issue on Advancing Computational Intelligence in Autonomous Learning and Optimization Systems

Yaqing Hou, Liang Feng, Yi Mei
IEEE Computational Intelligence Magazine, 2024.

Special Session on Evolutionary Computation and Computational Intelligence for Scheduling and Combinatorial Optimisation

Yi Mei, Su Nguyen, Aaron Chen, Fangfang Zhang
IEEE World Congress on Computational Intelligence, 30 June – 5 July 2024, Yokohama, Japan.

Special Session on Evolutionary Computation for Electric Vehicle Routing and Charging

Yahui Jia, Qiang Yang, Wei-Neng Chen
IEEE World Congress on Computational Intelligence, 30 June – 5 July 2024, Yokohama, Japan.

IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation

Yi Mei, Nelishia Pillay, Liang Gao, Rong Qu
IEEE Symposium Series On Computational Intelligence, 5-8 December, 2023, Mexico City, Mexico.

The Distributed Ghost: Cellular Automata, Distributed Dynamical Systems, and Their Applications to Intelligence

Stefano Nichele, Hiroki Sayama, Chrystopher Nehaniv, Eric Medvet, Mario Pavone
Workshop at ALIFE, 27 July, 2023, Sapporo, Japan.

Special Session on Evolutionary Scheduling and Combinatorial Optimisation

Yi Mei, Su Nguyen, Aaron Chen, Fangfang Zhang
IEEE Congress on Evolutionary Computation, 3-5 July, 2023, Chicago, USA.

Special Session on Genetic Programming and Machine Learning for Scheduling

Fangfang Zhang, Mengjie Zhang, Yi Mei, Su Nguyen
IEEE Congress on Evolutionary Computation, 3-5 July, 2023, Chicago, USA.

Tutorial: Genetic Programming and Machine Learning for Scheduling

Fangfang Zhang
IEEE Congress on Evolutionary Computation, 3-5 July, 2023, Chicago, USA.

Keynote Speech: Human-Computer Intelligence in Combinatorial Problem Solving

Ruhul Sarker
IEEE Symposium Series On Computational Intelligence, Symposium on Evolutionary Scheduling and Combinatorial Optimisation, 4-7 December, 2022, Singapore.

Tutorial: Genetic Programming and Machine Learning for Job Shop Scheduling

Fangfang Zhang
IEEE Symposium Series On Computational Intelligence, 4-7 December, 2022, Singapore.

IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation

Yi Mei, Rong Qu, Nelishia Pillay, Liang Gao
IEEE Symposium Series On Computational Intelligence, 4-7 December, 2022, Singapore.

IEEE CIS Live Webinar: Selection Hyper-Heuristics for Automated Design, Configuration and Selection

Rong Qu
Online, 25 May, 2022.

Tutorial: Evolutionary Machine Learning for Combinatorial Optimisation

Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.

Tutorial: Introduction to automated design of scheduling heuristics with genetic programming

‪Domagoj Jakobović‬, Marko Ðurasević, Su Nguyen, Yi Mei, Mengjie Zhang
ACM Genetic and Evolutionary Computation Conference, 9-13 July, 2022, Boston, USA.

Special Session on Evolutionary Scheduling and Combinatorial Optimisation

Yi Mei, Su Nguyen, Aaron Chen, Fangfang Zhang
IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.

Special Session on Evolutionary Machine Learning for Planning and Scheduling

Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.

IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation

Yi Mei, Rong Qu, Nelishia Pillay, Liang Gao
IEEE Symposium Series on Computational Intelligence, 5-7 December, 2021, Orlando, Florida, USA (Virtual).

Keynote Speech: Scheduling Dynamic Job Shops – An Anticipative And A Self-Organization Approach

Juergen Branke
IEEE Symposium Series on Computational Intelligence, 5-7 December, 2021, Orlando, Florida, USA (Virtual).

Tutorial: Genetic Programming for Job Shop Scheduling

Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
IEEE Symposium Series on Computational Intelligence, 5-7 December, 2021, Orlando, Florida, USA (Virtual).

---

Go to TOP

---

People

Chair

• Yi Mei, Victoria University of Wellington, New Zealand

Vice-Chairs

• Jialin Liu, Southern University of Science and Technology, China
• Fangfang Zhang, Victoria University of Wellington, New Zealand

Members

• Juergen Branke, The University of Warwick, UK
• Xinye Cai, Dalian University of Technology, China
• Zhiguang Cao, Agency for Science Technology and Research (A*STAR), Singapore
• Aaron Chen, Victoria University of Wellington, New Zealand
• Wei-Neng Chen, South China University of Technology, China
• Wenbo Du, Beihang University, China
• Marko Ðurasević, University of Zagreb, Croatia
• Liang Feng, Chongqing University, China
• Liang Gao, Huazhong University of Science and Technology, China
• Yue-Jiao Gong, South China University of Technology, China
• Yinan Guo, China University of Mining and Technology, China
• Giovanni Iacca, University of Trento, Italy
• Yahui Jia, South China University of Technology, China
• ‪Domagoj Jakobović‬, University of Zagreb, Croatia
• Ahmed Kheiri‬, Lancaster University, UK
• Miqing Li, University of Birmingham, UK
• Xiaodong Li, RMIT University, Australia
• Yuxin Liu, Shanghai Maritine University, China
• Hui Ma, Victoria University of Wellington, New Zealand
• Setyo Tri Windras Mara, University of New South Wales, Australia
• Saúl Zapotecas-Martínez, National Institute of Astrophysics, Optics and Electronics, Mexico
• Weiyao Meng, University of Nottingham, UK
• Mustafa Misir, Duke Kunshan University, China
• Shivashankar B. Nair, Indian Institute of Technology Guwahati, India
• Su Nguyen, RMIT University, Australia
• Ender Özcan, University of Nottingham, UK
• Mario Pavone, University of Catania, Italy
• Nelishia Pillay, University of Pretoria, South Africa
• Radu-Emil Precup, Politehnica University of Timisoara, Romania
• Chao Qian, Nanjing University, China
• Rong Qu, University of Nottingham, UK
• Shady Salama, Kobe University, Japan
• Ruhul Sarker, University of New South Wales, Australia
• Yanan Sun, Sichuan University, China
• Ke Tang, Southern University of Science and Technology, China
• Hao Tong, University of Birmingham, UK
• Josu Ceberio Uribe, University of the Basque Country, Spain
• Shaolin Wang, Zhejiang Geely holding group
• Zhenkun Wang, Southern University of Science and Technology, China
• Bin Xin, Beijing Institute of Technology, China
• Huanlai Xing, Southwest Jiaotong University, China
• Xin Yao, Southern University of Science and Technology, China
• Wenjie Yi, University of Nottingham, UK
• Bo Yuan, Southern University of Science and Technology, China
• Zhi-Hui Zhan, South China University of Technology, China
• Mengjie Zhang, Victoria University of Wellington, New Zealand
• Yingqian Zhang, Eindhoven University of Technology, Netherlands

---

Go to TOP

---