Intelligent Systems and Applications Technical Committee


Task Force on Hyper-heuristics


The web site of the Task Force (2018 - present) can be found at here.

Overview of the Group

Welcome to the Task Force on Hyper-heuristics within the Technical Committee of Intelligent Systems and Applications at IEEE Computational Intelligence Society.

Hyper-heuristics represent one of the recent emerging meta-heuristics which attracted an increasing amount of research attention. Instead of designing heuristic methods with pre-defined parameters or mechanisms, hyper-heuristics search for or learn the selections or configurations of problem specific 'low level' heuristics which are then employed on the fly to solve the problem in hand. They therefore concern the search space of heuristics rather than solutions themselves. The algorithms are thus self-adaptive, and are able to deal with a much wider range of problems without extensive development effort. In addition to employing search algorithms, the current literature has started to investigate a wider range of computational intelligence and artificial intelligence techniques including constraint satisfaction, decision support, fuzzy rules, knowledge based systems, learning, and many others, aiming to develop advanced hybrid intelligent systems. Due to their self-adaptive nature, hyper-heuristics have been successfully applied within intelligent systems to concern various real world applications including personnel scheduling, job shop scheduling, 2D/3D strip packing, routing, assembly line, timetabling, knapsack and many other complex problems.

Along with the current state-of-the-art Hyper-heuristics research development at the interface of AI, CI and OR, we aim to further promote the scope of both intelligence techniques and applications within advanced hybrid intelligent systems.


In addition to promoting the development of hyper-heuristics in research, under the Technical Committee of Intelligent Systems Applications, we also aim to motivate the applications of hyper-heuristics in real world complex and constrained problems.

The research of hyper-heuristics is inherently interdisciplinary, and lies naturally at the interface of Artificial Intelligence, Computational Intelligence and Operational Research. To realise the objectives, we aim to organise a range of activities under the proposed task force and jointly with task forces within other Technical Committees at IEEE Computational Intelligence Society:

  • To organise future events including workshops, special sessions and/or tutorials at international conferences
  • To organise special issues at international journals in AI, CI and OR
  • Facilitate the collaboration between researchers and practitioners in Hyper-heuristics by means of meetings and publications in international journals.
  • Contribute to the development of original thinking in Hyper-heuristics.
  • Exchange experiences and knowledge, promote critical discussion, and facilitate contacts with researchers and practitioners in this research area.

Chair and Vice Chair (2013-2018)

Chair: Rong Qu, University of Nottingham, UK
Vice-Chair: Nelishia Pillay, University of KwaZulu-Natal, South Africa


Key Resources

Recent Activities and Conferences


  • Burke, E. K., M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and R. Qu (2010). Hyper-heuristics: A Survey of the State of the Art, to appear at Journal of Operational Research Society, 2012.
  • Burke, E. K., M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and J. Woodward (2009). A Classification of Hyper-heuristics Approaches, Handbook of Metaheuristics, International Series in Operations Research & Management Science, In M. Gendreau and J-Y Potvin (Eds.), Springer (in press).
  • Burke, E. K., Kendall, G., Newall, J., Hart, E., Ross P. and Schulenburg, S. (2003) Hyper-Heuristics: An Emerging Direction in Modern Search Technology, Chapter 16 in Handbook of Meta-Heuristics, (Eds. F. Glover and G. Kochenberger), Kluwer Academic Publishers, 457-474.
  • Ross, P. (2005) Hyper-heuristics, Chapter 17 in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Methodologies (Eds. E.K.Burke and G.Kendall), Springer, 529-556

Web Links

Last Updated: 27 April 2018