Hyper-heuristics: autonomous problem solvers

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Springer Science and Business Media Deutschland GmbH

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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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Natural Computing Series

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Mısır, M. (2021). Hyper-heuristics: Autonomous Problem Solvers. In Automated Design of Machine Learning and Search Algorithms (pp. 109-131). Springer, Cham.