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 or receiving updates from us, please feel free to contact Yi Mei.
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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:
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Yi Mei, Su Nguyen, Aaron Chen, Fangfang Zhang
IEEE Congress on Evolutionary Computation, 3-5 July, 2023, Chicago, USA.
Fangfang Zhang, Mengjie Zhang, Yi Mei, Su Nguyen
IEEE Congress on Evolutionary Computation, 3-5 July, 2023, Chicago, USA.
Ruhul Sarker
IEEE Symposium Series On Computational Intelligence, Symposium on Evolutionary Scheduling and Combinatorial Optimisation, 4-7 December, 2022, Singapore.
Yi Mei, Rong Qu, Nelishia Pillay, Liang Gao
IEEE Symposium Series On Computational Intelligence, 4-7 December, 2022, Singapore.
Rong Qu
Online, 25 May, 2022.
Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.
Domagoj Jakobović, Marko Ðurasević, Su Nguyen, Yi Mei, Mengjie Zhang
ACM Genetic and Evolutionary Computation Conference, 9-13 July, 2022, Boston, USA.
Yi Mei, Su Nguyen, Aaron Chen, Fangfang Zhang
IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.
Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang
IEEE World Congress on Computational Intelligence, 18-23 July, 2022, Padova, Italy.
Yi Mei, Rong Qu, Nelishia Pillay, Liang Gao
IEEE Symposium Series on Computational Intelligence, 5-7 December, 2021, Orlando, Florida, USA (Virtual).
Juergen Branke
IEEE Symposium Series on Computational Intelligence, 5-7 December, 2021, Orlando, Florida, USA (Virtual).
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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