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. 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, please feel free to contact Yi Mei.

NEWS

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:

Events and Activities

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

IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.

Special Session on Evolutionary Machine Learning for Planning and Scheduling

IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.

IEEE Symposium on Evolutionary Scheduling and Combinatorial Optimisation

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

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

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

Tutorial: Genetic Programming for Job Shop Scheduling

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

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