Research Output


  This chapter introduces and overviews an emerging methodology in search
and optimisation. One of the key aims of these new approaches, which have
been termed hyper-heuristics, is to raise the level of generality at which
optimisation systems can operate. An objective is that hyper-heuristics will
lead to more general systems that are able to handle a wide range of problem
domains rather than current meta-heuristic technology which tends to be
customised to a particular problem or a narrow class of problems. Hyperheuristics
are broadly concerned with intelligently choosing the right heuristic
or algorithm in a given situation. Of course, a hyper-heuristic can be (often is)
a (meta-)heuristic and it can operate on (meta-)heuristics. In a certain sense, a
hyper-heuristic works at a higher level when compared with the typical
application of meta-heuristics to optimisation problems i.e. a hyper-heuristic
could be thought of as a (meta)-heuristic which operates on lower level (meta-
)heuristics. In this chapter we will introduce the idea and give a brief history of
this emerging area. In addition, we will review some of the latest work to be published in the field.

  • Type:

    Book Chapter

  • Date:

    30 November 2004

  • Publication Status:


  • Publisher



Ross, P. (2004). Hyper-heuristics. In Burke, E. & Kendall, G. (Eds.). Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 529-556. Springer-Verlag. ISBN 978-0387234601



Hyper-heuristic; meta-heuristic; heuristic; optimisation; search;

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