IEEE.org | IEEE Xplore Digital Library | IEEE Standards | IEEE Spectrum | More Sites

Welcome to the website of the IEEE CIS 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!

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:

  • 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.

Contact

If you are interested in joining this taskforce or receiving updates from us, please feel free to contact the taskforce Chair Ya-Hui Jia or Yi Mei.