Case-based heuristic selection for timetabling problems

Burke, Edmund K.; Petrović, Sanja; Qu, Rong

doi:10.1007/s10951-006-6775-y

Cited by 173 publications

(99 citation statements)

References 43 publications

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“…Constructive hyper-heuristics construct solutions from "scratch" by intelligently calling different heuristics at different stages in the construction process. Examples of constructive hyperheuristic research can be seen in (Fisher and Thompson 1963, Kitano 1990, Hart, Ross and Nelson 1998, Burke, Petrovic and Qu 2006, Burke et al 2007). Local search hyper-heuristics start from a complete initial solution and repeatedly select appropriate heuristics to lead the search in promising new directions.…”

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

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A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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Abstract. One of the main motivations for investigating hyper-heuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NP-hard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyper-heuristic is able to achieve significant performance improvements over a recently proposed tabu search hyper-heuristic without lowering the level of generality. We also show that our hyper-heuristic is capable of producing competitive results against bespoke meta-heuristics methods for these problems. In some cases, the simulated annealing hyper-heuristic has even obtained considerable improvements over some of the current best problem-specific meta-heuristic approaches. The contribution of this paper is to present a method which can be readily (and automatically) applied to very different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

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Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

“…Other search methods which have been employed as hyper-heuristics include ant systems , choice functions (Cowling, Kendall andSoubeiga 2001, Rattadilok et al, 2005) and case-based reasoning (Burke, Petrovic and Qu 2006).…”

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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“…a metaheuristics is used to search a space of heuristics). Other approaches, not considered as metaheuristics, can and have been used as the high-level strategy in hyper-heuristics such as reinforcement learning [31,37,81,88], case-based reasoning [24] and learning classifier systems [98,112].…”

Section: Hyper-heuristicsmentioning

confidence: 99%

“…In selection hyper-heuristics, the framework is provided with a set of pre-existing (generally problem-specific) constructive heuristics, and the challenge is to select the heuristic that is somehow the most suitable for the current problem state. This type of approach has been successfully applied to hard combinatorial optimisation problems such as cutting and packing [98,112], educational timetabling [23,24,97] and production scheduling [25,43]. In the case of generation hyper-heuristics, the idea is to combine sub-components of previously existing constructive heuristics to produce new constructive heuristic.…”

Section: Hyper-heuristicsmentioning

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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“…Hybrid approaches involving combinations of heuristics and metaheuristics such as genetic algorithms and hill climbing techniques [10,11] have produced good results on benchmark datasets. Other successful approaches taken include multi-criteria approaches [12], constraint based techniques [13], case based reasoning [14], and recently hyper-heuristics [15]. Recent papers note that on the whole, methods used to tackle the examination problem tend to use problem specific information and heuristics in particular.…”

Section: Introductionmentioning

confidence: 99%

A New Neural Network Based Construction Heuristic for the Examination Timetabling Problem

Corr

McCollum

McGreevy

et al. 2006

Parallel Problem Solving From Nature - PPSN IX

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Abstract. This paper examines the application of neural networks as a construction heuristic for the examination timetabling problem. Building on the heuristic ordering technique, where events are ordered by decreasing scheduling difficulty, the neural network allows a novel dynamic, multi-criteria approach to be developed. The difficulty of each event to be scheduled is assessed on several characteristics, removing the dependence of an ordering based on a single heuristic. Furthermore, this technique allows the ordering to be reviewed and modified as each event is scheduled; a necessary step since the timetable and constraints are altered as events are placed. Our approach uses a Kohonen self organising neural network and is shown to have wide applicability. Results are presented for a range of examination timetabling problems using standard benchmark datasets.

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Case-based heuristic selection for timetabling problems

Cited by 173 publications

References 43 publications

A simulated annealing hyper-heuristic methodology for flexible decision support

A simulated annealing hyper-heuristic methodology for flexible decision support

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

A New Neural Network Based Construction Heuristic for the Examination Timetabling Problem

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