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Adaptive and Multilevel Metaheuristics pp 3–29Cite as

Hyperheuristics: Recent Developments

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 136))

Abstract

Given their economic importance, there is continuing research interest in providing better and better solutions to real-world scheduling problems. The models for such problems are increasingly complex and exhaustive search for optimal solutions is usually impractical. Moreover, difficulty in accurately modelling the problems means that mathematically “optimal” solutions may not actually be the best possible solutions in practice. Therefore heuristic methods are often used, which do not guarantee optimal or even near optimal solutions. The main goal of heuristics is to produce solutions of acceptable quality in reasonable time. The problem owners often prefer simple, easy to implement heuristic approaches which do not require significant amount of resources for their development and implementation [12]. However, such individual heuristics do not always perform well for the variety of problem instances which may be encountered in practice. There is a wide range of modern heuristics known from the literature which are specifically designed and tuned to solve certain classes of optimisation problems. These methods are based on the partial search of the solution space and often referred as metaheuristics.

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  1. CASS Business School, City University, London, EC1Y 8TZ, UK

    Konstantin Chakhlevitch

  2. Department of Computing, University of Bradford, Bradford, BD7 1DP, UK

    Peter Cowling

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  1. Konstantin Chakhlevitch
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Carlos Cotta Marc Sevaux Kenneth Sörensen

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Chakhlevitch, K., Cowling, P. (2008). Hyperheuristics: Recent Developments. In: Cotta, C., Sevaux, M., Sörensen, K. (eds) Adaptive and Multilevel Metaheuristics. Studies in Computational Intelligence, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79438-7_1

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