Hyperheuristics for managing a large collection of low level heuristics to schedule personnel

Cowling, Peter I.; Chakhlevitch, Konstantin

doi:10.1109/cec.2003.1299807

Cited by 39 publications

(35 citation statements)

References 14 publications

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“…A larger and more complex problem, which has been described in Cowling and Chakhlevitch (2003) is summarised in this section. It involves a number of events, trainers, and locations to be scheduled over a period of time.…”

Section: The Training Scheduling Problem and Hyperheuristicsmentioning

confidence: 99%

“…A new general framework to deal with large and complex optimisation and scheduling problems, called a hyperheuristic, has been proposed by Cowling et al (2000). It tends to robustly find good solutions for large and complex scheduling problems and has been proven to be effective in many experiments (Cowling & Chakhlevitch, 2003;Cowling et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we would like to answer questions like: which learning algorithm can derive knowledge that could direct the search in order to produce good solutions? To achieve our goal, we compare three associative classification techniques CBA (Liu, Hsu, & Ma, 1998), MCAR (Thabtah, Cowling, & Peng, 2005), MMAC (Thabtah, Cowling, & Peng, 2004) and two popular traditional classification techniques (PART (Frank & Witten, 1998) and RIPPER (Cohen, 1995)) on data sets generated using a hybrid hyperhueristic called Peckish (Cowling & Chakhlevitch, 2003), for the trainer scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

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Mining the data from a hyperheuristic approach using associative classification

Thabtah¹,

Cowling²

2008

Expert Systems with Applications

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Article:Thabtah, Fadi A. and Cowling, Peter I. orcid.org/0000- 0003-1310-6683 (2008) Mining the data from a hyperheuristic approach using associative classification. ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request.Mining the data from a hyperheuristic approach using associative classification AbstractAssociative classification is a promising classification approach that utilises association rule mining to construct accurate classification models. In this paper, we investigate the potential of associative classifiers as well as other traditional classifiers such as decision trees and rule inducers in solutions (data sets) produced by a general-purpose optimisation heuristic called the hyperheuristic for a personnel scheduling problem. The hyperheuristic requires us to decide which of several simpler search neighbourhoods to apply at each step while constructing a solutions. After experimenting 16 different solution generated by a hyperheuristic called Peckish using different classification approaches, the results indicated that associative classification approach is the most applicable approach to such kind of problems with reference to accuracy. Particularly, associative classification algorithms such as CBA, MCAR and MMAC were able to predict the selection of low-level heuristics from the data sets more accurately than C4.5, RIPPER and PART algorithms, respectively.

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Section: The Training Scheduling Problem and Hyperheuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining the data from a hyperheuristic approach using associative classification

Thabtah¹,

Cowling²

2008

Expert Systems with Applications

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“…In the first case, a learning mechanism assists the selection of low-level heuristics according to their historical performance during the search process, e.g. [8]. In the second case, the focus is on searching components that once combined generate a new heuristic suitable for the problem in hand.…”

Section: Heuristics Designmentioning

confidence: 99%

“…Studying novel approaches for the development of hyper-heuristics is important since they are domain-independent problem strategies that operate on a space of heuristics, rather than on a space of solutions, and rise the level of generality on automated problem solving. 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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A Hyper-Heuristic Based on Random Gradient, Greedy and Dominance

Özcan

Kheiri

2011

Computer and Information Sciences II

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Abstract. Hyper-heuristics have emerged as effective general methodologies that are motivated by the goal of building or selecting heuristics automatically to solve a range of hard computational search problems with less development cost. HyFlex is a publicly available hyper-heuristic tool for rapid development and research which provides an interface to four problem domains along with relevant low level heuristics. A multistage hyper-heuristic based on random gradient and greedy with dominance heuristic selection methods is introduced in this study. This hyperheuristic is implemented as an extension to HyFlex. The empirical results show that our approach performs better than some previously proposed hyper-heuristics over the given problem domains.

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Hyperheuristics for managing a large collection of low level heuristics to schedule personnel

Cited by 39 publications

References 14 publications

Mining the data from a hyperheuristic approach using associative classification

Mining the data from a hyperheuristic approach using associative classification

Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

A Hyper-Heuristic Based on Random Gradient, Greedy and Dominance

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