Hyper-Heuristics: Theory and Applications

Pillay, Nelishia; Qu, Ronghai

doi:10.1007/978-3-319-96514-7

Cited by 101 publications

(96 citation statements)

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“…Initially, hyperheuristics (HHs) were classified from two perspectives: their nature and the nature of their available solvers. This process led to the definition of four categories, as perspectives are not mutually exclusive [6]. Based on its nature, a hyper-heuristic can pursue the 'selection' of low-level solvers (LLS), or the 'generation' of new LLS based on the building blocks of the available ones.…”

Section: B An Overview Of Hyper-heuristicsmentioning

confidence: 99%

“…It is unfeasible to pretend covering all kinds of hyper-heuristics in this manuscript. But, the interested reader is referred to [6], [79]. When properly trained, hyper-heuristics can provide outstanding results.…”

Section: B An Overview Of Hyper-heuristicsmentioning

confidence: 99%

“…Literature is prolific about these problems, and also about those that consider both kinds of variables, e.g., [5]. However, the interaction of this type of problems and hyper-heuristics has not been extensively studied yet [6]. An initial work on this direction is the research by Cruz-Duarte et al [7], which presented a preliminary study to apply hyper-heuristic models to continuous functions.…”

Section: Introductionmentioning

confidence: 99%

“…In general, a heuristic approximates a solution via loosely defined rules or based on trial-and-error. Heuristics are grouped into three categories according to their level of sophistication [6], as described next. Low-level ones (or just heuristics) are the simplest, which can be defined by single rules or direct actions over the search domain.…”

Section: Introductionmentioning

confidence: 99%

“…In generation HHs, then, each one of the available heuristics is decomposed into building blocks which are then combined for creating new heuristics. Both models have already been implemented in many applications [6].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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Section: B An Overview Of Hyper-heuristicsmentioning

confidence: 99%

Section: B An Overview Of Hyper-heuristicsmentioning

confidence: 99%