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147
A graphbased hyperheuristic for educational timetabling problems
 European Journal of Operational Research
, 2007
"... This paper presents an investigation of a simple generic hyperheuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics whic ..."
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Cited by 70 (24 self)
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This paper presents an investigation of a simple generic hyperheuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multistage hyperheuristic where the Tabu Search employs permutations upon a different number of graph heuristics in two stages. We study this graphbased hyperheuristic approach within the context of exploring fundamental issues concerning the search space of the hyperheuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyperheuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the finetuned bespoke stateoftheart approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyperheuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems. 1
A Classification of Hyperheuristic Approaches
"... The current state of the art in hyperheuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In ..."
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Cited by 58 (21 self)
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The current state of the art in hyperheuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyperheuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyperheuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyperheuristic research.
A Comprehensive Analysis of Hyperheuristics
"... Abstract. Metaheuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a metaheuristic for solving a problem i ..."
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Cited by 40 (16 self)
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Abstract. Metaheuristics such as simulated annealing, genetic algorithms and tabu search have been successfully applied to many difficult optimization problems for which no satisfactory problem specific solution exists. However, expertise is required to adopt a metaheuristic for solving a problem in a certain domain. Hyperheuristics introduce a novel approach for search and optimization. A hyperheuristic method operates on top of a set of heuristics. The most appropriate heuristic is determined and applied automatically by the technique at each step to solve a given problem. Hyperheuristics are therefore assumed to be problem independent and can be easily utilized by nonexperts as well. In this study, a comprehensive analysis is carried out on hyperheuristics. The best method is tested against genetic and memetic algorithms on fourteen benchmark functions. Additionally, new hyperheuristic frameworks are evaluated for questioning the notion of problem independence. 1.
Evolving Bin Packing Heuristics with Genetic Programming
 PARALLEL PROBLEM SOLVING FROM NATURE  PPSN IX SPRINGER LECTURE NOTES IN COMPUTER SCIENCE. VOLUME 4193 OF LNCS., REYKJAVIK, ICELAND, SPRINGERVERLAG (2006) 860–869
, 2006
"... The binpacking problem is a well known NPHard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presente ..."
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Cited by 38 (13 self)
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The binpacking problem is a well known NPHard optimisation problem, and, over the years, many heuristics have been developed to generate good quality solutions. This paper outlines a genetic programming system which evolves a heuristic that decides whether to put a piece in a bin when presented with the sum of the pieces already in the bin and the size of the piece that is about to be packed. This heuristic operates in a fixed framework that iterates through the open bins, applying the heuristic to each one, before deciding which bin to use. The best evolved programs emulate the functionality of the human designed `firstfit' heuristic. Thus, the contribution of this paper is to demonstrate that genetic programming can be employed to automatically evolve bin packing heuristics which are the same as high quality heuristics which have been designed by humans.
Automatic heuristic generation with genetic programming: Evolving a jackofalltrades or a master of one
 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2007, PROCEEDINGS
, 2007
"... It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who ..."
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Cited by 37 (14 self)
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It is possible to argue that online bin packing heuristics should be evaluated by using metrics based on their performance over the set of all bin packing problems, such as the worst case or average case performance. However, this method of assessing a heuristic would only be relevant to a user who employs the heuristic over a set of problems which is actually representative of the set of all possible bin packing problems. On the other hand, a real world user will often only deal with packing problems that are representative of a particular subset. Their piece sizes will all belong to a particular distribution. The contribution of this paper is to show that a Genetic Programming system can automate the process of heuristic generation and produce heuristics that are humancompetitive over a range of sets of problems, or which excel on a particular subset. We also show that the choice of training instances is vital in the area of automatic heuristic generation, due to the tradeoff between the performance and generality of the heuristics generated and their applicability to new problems.
Exploring Hyperheuristic Methodologies with Genetic Programming
"... Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are n ..."
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Cited by 34 (14 self)
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Hyperheuristics represent a novel search methodology that is motivated by the goal of automating the process of selecting or combining simpler heuristics in order to solve hard computational search problems. An extension of the original hyperheuristic idea is to generate new heuristics which are not currently known. These approaches operate on a search space of heuristics rather than directly on a search space of solutions to the underlying problem which is the case with most metaheuristics implementations. In the majority of hyperheuristic studies so far, a framework is provided with a set of human designed heuristics, taken from the literature, and with good measures of performance in practice. A less well studied approach aims to generate new heuristics from a set of potential heuristic components. The purpose of this chapter is to discuss this class of hyperheuristics, in which Genetic Programming is the most widely used methodology. A detailed discussion is presented including the steps needed to apply this technique, some representative case studies, a literature review of related work, and a discussion of relevant issues. Our aim is to convey the exciting potential of this innovative approach for automating the heuristic design process
An effective hybrid algorithm for university course timetabling
, 2006
"... The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were subm ..."
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Cited by 29 (5 self)
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The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an ‘International Timetabling Competition’ to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semiautomatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems.
An experimental study on hyperheuristics and exam timetabling
 Proceedings of the 6th International Conference on Practice and Theory of Automated Timetabling
, 2006
"... Abstract. Hyperheuristics are proposed as a higher level of abstraction as compared to the metaheuristics. Hyperheuristic methods deploy a set of simple heuristics and use only nonproblemspecific data, such as, fitness change or heuristic execution time. A typical iteration of a hyperheuristic a ..."
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Cited by 29 (9 self)
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Abstract. Hyperheuristics are proposed as a higher level of abstraction as compared to the metaheuristics. Hyperheuristic methods deploy a set of simple heuristics and use only nonproblemspecific data, such as, fitness change or heuristic execution time. A typical iteration of a hyperheuristic algorithm consists of two phases: heuristic selection method and move acceptance. In this paper, heuristic selection mechanisms and move acceptance criteria in hyperheuristics are analyzed in depth. Seven heuristic selection methods, and five acceptance criteria are implemented. The performance of each selection and acceptance mechanism pair is evaluated on fourteen wellknown benchmark functions and twentyone exam timetabling problem instances. 1
Selecting and Weighting Features Using a Genetic Algorithm in a CaseBased Reasoning Approach to Personnel Rostering
, 2006
"... Personnel rostering problems are highly constrained resource allocation problems. ..."
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Cited by 27 (5 self)
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Personnel rostering problems are highly constrained resource allocation problems.
Hybrid variable neighbourhood approaches to university exam timetabling
, 2006
"... Abstract. In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with ..."
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Cited by 26 (9 self)
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Abstract. In this paper, we investigate variable neighbourhood search (VNS) approaches for the university examination timetabling problem. In addition to a basic VNS method, we introduce variants of the technique with different initialisation methods including a biased VNS and its hybridisation with a Genetic Algorithm. A number of different neighbourhood structures are analysed. It is demonstrated that the proposed technique is able to produce high quality solutions across a wide range of benchmark problem instances. In particular, we demonstrate that the Genetic Algorithm, which intelligently selects approporiate neighbourhoods to use within the biased VNS produces the best known results in the literature, in terms of solution quality, on some of the the benchmark instances, although it requires relatively large amount of computational time. Possible extensions to this overall approach are also discussed. 1