Hyper-Heuristics: An Emerging Direction in Modern Search Technology

Burke, Edmund K.; Kendall, Graham; Newall, Jim; Hart, Emma; Ross, Peter; Schulenburg, Sonia

doi:10.1007/0-306-48056-5_16

Cited by 508 publications

(351 citation statements)

References 11 publications

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“…In many settings heterogeneous models make more sense-islands can use different parameters, different operators, and even different fitness functions. This closely relates to the emerging area of hyper-heuristics [13].…”

Section: Introductionmentioning

confidence: 78%

Homogeneous and Heterogeneous Island Models for the Set Cover Problem

Mambrini

Sudholt

Yao

2012

Lecture Notes in Computer Science

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Section: Introductionmentioning

confidence: 78%

Homogeneous and Heterogeneous Island Models for the Set Cover Problem

Mambrini

Sudholt

Yao

2012

Lecture Notes in Computer Science

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“…Hyper-heuristics are defined as search methodologies that select and combine lowlevel heuristics to solve hard computational search problems [6,16]. The general aim of a hyper-heuristic is to manufacture unknown heuristics which are fast, well performing and widely applicable to a range of problems.…”

Section: Heuristics Designmentioning

confidence: 99%

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

Terrazas

Landa-Silva

Krasnogor

2010

Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)

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The current research trends on hyper-heuristics design have sprung up in two different flavours: heuristics that choose heuristics and heuristics that generate heuristics. In the latter, the goal is to develop a problem-domain independent strategy to automatically generate a good performing heuristic for specific problems, that is, the input to the algorithm are problems and the output are problem-tailored heuristics. This can be done, for example, by automatically selecting and combining different low-level heuristics into a problem specific and effective strategy. Thus, hyper-heuristics raise the level of generality on automated problem solving by attempting to select and/or generate tailored heuristics for the problem in hand. Some approaches like genetic programming have been proposed for this. In this paper, we report on an alternative methodology that sheds light on simple methodologies that efficiently cooperate by means of local interactions. These entities are seen as building blocks, the combination of which is employed for the automated manufacture of good performing heuristic search strategies. We present proof-of-concept results of applying this methodology to instances of the well-known symmetric TSP. The goal here is to demonstrate feasibility rather than compete with state of the art TSP solvers. This TSP is chosen only because it is an easy to state and well known problem.

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“…A selection hyper-heuristic methodology combines heuristic selection and move acceptance processes under a single point search framework [5][6][7][8][9]. A candidate solution is improved iteratively by selecting, applying a heuristic (neighbourhood operator) from a set of low level heuristics, then passing the new solution through move acceptance to replace the solution in hand at each step.…”

Section: Introductionmentioning

confidence: 99%

A Hyper-heuristic with a Round Robin Neighbourhood Selection

Kheiri

Özcan

2013

Evolutionary Computation in Combinatorial Optimization

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Abstract. An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.

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Hyper-Heuristics: An Emerging Direction in Modern Search Technology

Cited by 508 publications

References 11 publications

Homogeneous and Heterogeneous Island Models for the Set Cover Problem

Homogeneous and Heterogeneous Island Models for the Set Cover Problem

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

A Hyper-heuristic with a Round Robin Neighbourhood Selection

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