An Experimental Study on Hyper-heuristics and Exam Timetabling

Bi̇lgi̇n, Burak; Özcan, Ender; Korkmaz, Emin Erkan

doi:10.1007/978-3-540-77345-0_25

Cited by 83 publications

(92 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It has also been observed that the choice of heuristic selection and move acceptance combination could lead to major performance differences across a diverse set of problem domains. This particular observation is aligned with previous findings in [2,15].…”

Section: Resultssupporting

confidence: 93%

Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Almutairi

Özcan

Kheiri

et al. 2016

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

Abstract. Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0-1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the 'unseen' problems in addition to the six standard HyFlex problem domains.

show abstract

Section: Resultssupporting

confidence: 93%

Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Almutairi

Özcan

Kheiri

et al. 2016

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the literature, these include both moving strategies (e.g. [3,9,16,26,30]) and constructive strategies (e.g. [6,12,16,27]).…”

Section: Introductionmentioning

confidence: 99%

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

Burke

McCollum

2009

European Journal of Operational Research

View full text Add to dashboard Cite

In this paper we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences that consist of different graph colouring heuristics to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different heuristics to construct solutions step by step. Based on these sequences, we analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research.It is observed that spending the effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate good quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 Abstract. In this paper we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences that consist of different graph colouring heuristics to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different heuristics to construct solutions step by step. Based on these sequences, we analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate good quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. ...

show abstract

“…Practitioners in industry often favor the use of very simple and readily understandable methods even if they deliver relatively inferior results (Ross 2005). Among the main criticisms of stochastic-based problem solving techniques are: (1) the fact that they involve some randomness and unpredictability, so that identical runs may deliver very different results; (2) there is little understanding about their average-and worst-case behavior (Ross and Marín-Blázquez 2005); (3) solutions quality greatly depend on a good parameter choice; and (4) the parameter tunning task requires time, knowledge and experience about the problem domain and properties which makes metaheuristics problemspecific solution methods that can be developed and deployed only by experts (Ross 2005;Bilgin et al 2006).…”

Section: The 2d Irregular Bin Packing Problemmentioning

confidence: 99%

An effective heuristic for the two-dimensional irregular bin packing problem

et al. 2013

View full text Add to dashboard Cite

This paper proposes an adaptation, to the two-dimensional irregular bin packing problem of the Djang and Finch heuristic (DJD), originally designed for the one-dimensional bin packing problem. In the two-dimensional case, not only is it the case that the piece's size is important but its shape also has a significant influence. Therefore, DJD as a selection heuristic has to be paired with a placement heuristic to completely construct a solution to the underlying packing problem. A successful adaptation of the DJD requires a routine to reduce computational costs, which is also proposed and successfully tested in this paper. Results, on a wide variety of instance types with convex polygons, are found to be significantly better than those produced by more conventional selection heuristics.

show abstract

An Experimental Study on Hyper-heuristics and Exam Timetabling

Cited by 83 publications

References 17 publications

Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Performance of Selection Hyper-heuristics on the Extended HyFlex Domains

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

An effective heuristic for the two-dimensional irregular bin packing problem

Contact Info

Product

Resources

About