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Hyper-heuristics: Autonomous Problem Solvers

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Automated Design of Machine Learning and Search Algorithms

Part of the book series: Natural Computing Series ((NCS))

Abstract

Algorithm design is a general task for any problem-solving scenario. For Search and Optimization, this task becomes rather challenging due to the immense algorithm design space. Those existing design options are usually traversed to devise algorithms by the human algorithm development experts together with the specialists on the target problem domains. The resulting algorithms are mostly problem-specific as they are unable to solve a different problem than the current target. Unlike the traditionally developed algorithms, Hyper-heuristics are known as problem-independent solvers pursuing the grand goal of generality. Generality, in this context, means that effectively solving different problems with a single algorithm under varying experimental conditions. This generality element is chased by performing a high-level search across the algorithm space differently than the majority of the algorithms directly operating on the solution space. In that respect, by design, a hyper-heuristic can be applied to any problem with a search space of quantifiable solutions. This flexibility coming from their easy-to-use nature has been validated in various academic and real-world applications. The present chapter provides a general overview of hyper-heuristics while discussing their shortcomings and recipes for future hyper-heuristic research.

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Notes

  1. 1.

    https://www.automl.org/.

  2. 2.

    A hyper-heuristic bibliography: http://mustafamisir.github.io/hh.html.

  3. 3.

    http://titancs.ukzn.ac.za/EvoHyp.aspx.

  4. 4.

    http://www.asap.cs.nott.ac.uk/external/chesc2011/.

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Acknowledgements

This study was supported by the 2232 Reintegration Grant from the Scientific and Technological Research Council of Turkey (TUBITAK) under Project 119C013.

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  1. Department of Computer Engineering, Istinye University, Topkapı Campus, Maltepe Mah. Edirne Çırpıcı Yolu, No: 9, 34010, Zeytinburnu, Istanbul, Turkey

    Mustafa Mısır

  2. Duke Kunshan University, Duke Avenue No: 8, 215316, Kunshan, Jiangsu, China

    Mustafa Mısır

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  1. Mustafa Mısır
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Correspondence to Mustafa Mısır .

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  1. Dept. of Computer Science, University of Pretoria, Pretoria, South Africa

    Nelishia Pillay

  2. School of Computer Science, University of Nottingham, Nottingham, UK

    Rong Qu

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Mısır, M. (2021). Hyper-heuristics: Autonomous Problem Solvers. In: Pillay, N., Qu, R. (eds) Automated Design of Machine Learning and Search Algorithms. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-030-72069-8_7

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