Hyperheuristics: Recent Developments

Chakhlevitch, Konstantin; Cowling, Peter I.

doi:10.1007/978-3-540-79438-7_1

Cited by 116 publications

(80 citation statements)

References 34 publications

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“…Hyper-heuristics have been employed for solving search and optimisation problems such as bin-packing [4,17], timetabling [14], scheduling [8,9] and satisfiability [2] among others. For detailed reviews of hyper-heuristics and their applications, please refer to [7,13,16].…”

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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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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“…In both cases, the distinguishing feature of search methods in the meta/hyper level is that they operate on a search space of algorithm components rather than on a search space of solutions of the underlying problem. Therefore, they search for a good method to solve the problem rather than for a good solution [27].…”

Section: Introductionmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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“…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. [10,2] are the recent surveys on hyper-heuristics. In this study, a new simple selection hyper-heuristic is introduced and tested across a variety of problem domains using Hyflex (Hyper-heuristics Flexible framework) [11], a software tool for hyper-heuristic development and research.…”

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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Hyperheuristics: Recent Developments

Cited by 116 publications

References 34 publications

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

A Hyper-heuristic with a Round Robin Neighbourhood Selection

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